🎙️ In this episode, we discuss:

00:00 The Journey of an Innovator

06:27 The Birth of a Smartphone ECG

11:29 Overcoming Resistance in Digital Health

16:20 The Evolution of ECG Technology

23:43 The Importance of Early Detection in Cardiac Care

26:20 Innovations in 12-Lead ECG Technology

29:00 AI and Machine Learning in Cardiac Diagnostics

34:23 Remote Monitoring and Patient Empowerment

38:34 Navigating AI Diagnostics: Sensitivity vs Specificity

41:26 Consumer Wearables vs. Medical Devices

43:14 Future of AI in Cardiology and Personal Health Awareness

🎙️ In this episode, we discuss:

00:00 The Origin Story of Arrive Health

06:04 Rebranding and Evolving Mission

11:50 Real-Time Patient-Specific Drug Costs

18:08 Tackling Prior Authorization Challenges

22:56 Leveraging AI for Healthcare Efficiency

25:36 Understanding Scale and Impact

28:04 Collaboration with Payers and PBMs

30:27 Leveraging AI for Prior Authorization

32:43 Enhancing Access to Medications

36:27 Expanding Beyond Medications

38:19 Reframing Access to Care

40:51 Future Directions and Innovations

44:55 Wisdom for Innovators in Healthcare

If you look back at all the health-tech and drug development companies Harry has hosted on the show, an interesting pattern starts to emerge: a very large number of those companies have gone on to enormous growth and success in their markets. It could be that being on the podcast is like a catapult to success—or it could be that we're pretty good at finding companies that are already on a promising trajectory. Either way, there's no better example than Concert AI. 

The company’s CEO, Jeff Elton, first spoke with Harry back in July of 2021. At that time, the company was already one of the leaders in gathering and analyzing broad collections of data about cancer patients involved in clinical trials for new treatments. Its specialty was, and is, going beyond the very specific endpoints measured in clinical trials and looking to electronic medical records, genome sequencing data, insurance claims data, and other sources in order to build a more comprehensive picture of cancer patients and their journeys through the healthcare system. That kind of data can be very useful to companies trying to track the performance of their drugs after they’ve reached the market, and to researchers planning new clinical trials. 

And since that first conversation, the company has grown by leaps and bounds. It’s taken over management of more data sources, including the massive CancerLinq database formerly maintained by the American Society of Clinical Oncology. It’s struck up partnerships with some of the leading technology startups, research centers, and drug companies working to beat cancer. And it’s leaning hard into the new wave of deep-learning AI tools and their potential to help find patterns in vast amounts of data about patients. It’s probably safe to say that ConcertAI has gathered up more data about cancer patients than any other company on the planet. And investors have been rushing to pour money into the company, on the conviction that data is going to be the key to getting more and better cancer drugs to market. That’s certainly Jeff Elton's conviction too, as you’ll hear in today's interview.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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This week Harry interviews Gregory Bowman, an associate professor in the department of biochemistry and molecular biophysics in the School of Medicine at Washington University in St. Louis. Bowman is the current director of Folding@home, a distributed computing project currently focused on analyzing the structures of coronavirus proteins to find targets for new drug therapies that could help end the pandemic.

Understanding and modeling the 3D structures of tiny, ever-shifting protein molecules is a notoriously complex problem. Folding@home cuts through it by sending crystallography data and other information to thousands of home computers and using it to model possible protein configurations—effectively creating a large, networked supercomputer. The project has been underway in various forms since 2000, but has recently concentrated fully on the SARS-CoV-2 virus that causes covid-19. The hope is that the work will reveal locations on viral proteins where small-molecule drugs could bind, disrupting the virus's ability to enter human cells and replicate itself. 

By patching together so many distributed machines, "We are the first computer to reach the exascale," Bowman says. "Our peak performance is about 10-fold that of the world's fastest traditional supercomputer. Even before the 100-fold growth we have experienced since starting our work on covid-19, we were running calculations that would have cost millions of dollars to run on the cloud." Now that number is in the hundreds of millions of dollars. 

Anyone can contribute to the effort by going to foldingathome.org and downloading the Folding@home software to their Windows, Mac, or Linux machine.

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Harry's guest this week is the founder and CEO of a New Zealand firm, SaferMe, that had developed proximity-based smartphone apps for worker safety. When the coronavirus came along, their apps turned out to be a great way to help companies build their own "contact tables" to identify, test, and isolate SARS-CoV-2 carriers.

In epidemiology, contact tracing is the art of determining who has crossed paths with an infected individual, so that those exposed can be alerted and can take appropriate action, such as self-isolating. Health agencies around the world are building public smartphone apps to assist with contact tracing, but they're being deployed at a national scale, whereas many businesses need more detailed information to protect their workers. 

Van Marrewijk says SaferMe had already built technology that creates a "virtual safety bubble" around each worker—issuing an alert, for example, if lightning is approaching or if they come too close to a hazard such as a mine shaft. "We already had this technology going and we had already done GDPR [data privacy] compliance," he says. When the company noticed early in the pandemic that some of its clients were using the app as the foundation for in-house COVID-19 contact tracing efforts, it quickly built a dedicated app.  

"Someone reports sick, your contact tracer can hit a button and quickly see 'These are the eight people out of a group of 40 that perhaps should stay home or be tested until we sure,'" Van Marrewijk explains. "That gives some assurance there's a proper process in place."

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Though the p-value "determines everything we do in  drug development or medical research," says Dr. Ulo Palm , it may be one of the most misunderstood and misused quantities in experimental science—drug discovery included. At its core, the p-value shows the probability that an observed effect was due to random chance. In other words, if a drug seems to outperforms a placebo with an associated p-value of 0.05, there's only a 5 percent chance that the study was wrong and that the drug is, in fact, no better than the placebo. A p-value of 0.05 is the accepted threshold for validity in most scientific research, even though it's an arbitrary standard set nearly a century ago by statistician Sir Ronald Fisher. "People don't often realize that this p-value of 5 percent was pulled out of thin air," Dr. Palm says. "If Sir Ronald Fisher had had six fingers, we would all be using a p-value of 6 percent."

The issue, Palm says, is that an arbitrary dividing line of 0.05 leads journal publishers (and paper authors themselves) to reject or ignore real effects that don't happen to meet the threshold. If a drug trial yields a p-value of more than 0.05, "You should never ever say it is not working," he tells Harry. "You can only say we were not able to make a determination. That's it." By examining the spread of a data set, confidence intervals, data from individuals, and other measures, Palm says, today's researchers can get a more realistic picture of the promise of a new compounds as medicines.

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Building on his March 2020 interview with Jake Glanville, the founding partner and CEO of South San Francisco-based computational antibody engineering startup Distributed Bio, Harry speaks with three company scientists in the trenches: JP Buerckert, director of computational immunology, and Shahrad Daraekia and Jack Wang, both senior scientists. Together they're working on projects such as engineering existing human antibodies to the SARS virus so that they'll also work against the novel coronavirus, SARS-CoV2.

The company's special sauce lies in its computational algorithms for analyzing antibody gene sequences and generating billions of new candidate antibodies against different pathogens. "We have a very strong wet lab team that is generating data for us and then we have a very strong data team that is sorting through these data" to help scientists decide which antibody leads to move forward with, Buerckert explains.

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If you've seen the recent Netflix docu-series "Pandemic," about efforts to check previous viral outbreaks, you've seen former Pfizer scientist Jacob Glanville in action. The inventor, entrepreneur, and Ph.D. immunologist capitalized on the advent of cloud computing to provide vaccine and drug developers with high-throughput genomic sequencing of antibodies in humans and other species. He calls it "using the ability to look deep into these maelstroms of antibodies to try to understand why vaccines fail to hit conserved epitopes [where antibodies attach to antigens] on influenza or HIV, or how to better produce an antibody medicine." Revenue from the service allowed the startup to grow without outside capital. Today the company is developing a universal flu vaccine for pigs and humans. 

Glanville says we'll know by April whether existing anti-malarial, anti-HIV or anti-Ebola antivirals work against the COVID-19 coronavirus. A vaccine will take far longer to develop, he says. Meanwhile, Distributed Bio is using its search platform to find new antibodies—derived from antibodies that neutralize the SARS virus—that could recognize the new coronavirus and provide instant (but relatively short-lived) protection. Glanville compares the search to "taking five billion spaghetti noodles and throwing them against the wall and seeing what sticks."

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Schrödinger makes software that models the physics of atomic-scale interactions to predict the chemical properties of candidate drug molecules, helping its customers speed up drug discovery. A decade ago, Farid tells Harry, the company faced the chicken-and-egg challenge of convincing customers that its computational platform works, so that they would scale up their commitment, so that they could gather evidence it was working. Close collaborations with customers like Nimbus Therapeutics helped it improve the software and surmount that challenge. 

"In order to really take it to the next level and make a difference, it was necessary to use the software as customers ourselves," Farid says. "You get real-time feedback, honest feedback. You can imagine how much we learned from that."

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Rapid sequencing of viral genomes is giving physicians and epidemiologists new ways to identify, track, and potentially slow outbreaks of viral infections such as the novel Wuhan coronavirus. That means high-throughput genome sequencing—which had predominantly been a research tool—is taking its place as a front-line weapon in the fight to prevent pandemics, says Febbo, a medical oncologist. "Last year, 40 percent of our consumables in sequencing were for clinical testing, and we see the clinical testing increasing at a pace that's faster than research testing," he says.

Whole-genome viral sequencing, as a supplement to more traditional PCR-based testing for RNA sequences, can not only reveal exactly which virus is afflicting a given patient, but can reveal where a virus originated and how it is evolving to evade vaccines or other interventions.  

"The fact that the WHO heard of the first cases [of the Wuhan coronavirus] at the end of December, and the New England Journal published the full genome on January 24, within a month, because of the availability of sequencing, already, places like the CDC are using that information to design the probes for the RT-PCR to develop front line tests—never before has anything like that happened," Febbo notes. 

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Doctors helping couples conceive through in-vitro fertilization typically must screen multiple fertilized embryos to select one embryo for implantation—but the process is fraught with risk and subjectivity. from In 2018 Gilboa and her colleagues Daniel Seidman and Eyal Schiff co-founded AIVF, an Israel-based startup developing decision support tools that use deep learning and computer vision to lower the risk by identifying the most promising embryos for intrauterine implantation.

The company's technology takes the place of old-fashioned visual evaluation of embryos by humans, instead of capturing time-lapse video of embryos from the moment of conception to the fifth day after conception, at multiple focal planes. "It's an obscene amount of data," Gilboa says. "Instead of looking at the embryo once a day under the microscope, we have tons of images to annotate and look for the biological features that we know are correlated with success."

Proprietary machine learning algorithms use the video data, together with patients' health history and genomic data, to predict which embryos have the highest chance of developing into a healthy newborn. In theory, the technology will lower failure rates, decreasing the number of fertility cycles required for conception and therefore lowering the overall cost of IVF treatment.

"Many people don't get to fulfill their dream of having a child, and this is really heartbreaking for me," Gilboa tells Harry. "This is what really drives me as an embryologist to be able to provide a new, next-generation IVF treatment that would be accessible, that wouldn't be so expensive."

Check out the full show notes for this episode and other MoneyBall Medicine episodes on our website. For more on how data is transforming reproductive medicine, listen to Harry's interview with Alan Copperman.

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Tom Davenport knows analytics, big data, and AI—he teaches executive courses on the subject at Babson College, Harvard Business School, the Harvard School of Public Health, and the MIT Sloan School of Management, and is widely known for his books on analytics and AI in business, Competing on Analytics (2007), Only Humans Need Apply (2016), and The AI Advantage (2018).

Davenport notes that a number of life science startups are attempting to use machine learning, big data, and AI to reinvent drug discovery (a subject thoroughly covered in previous episodes of MoneyBall Medicine). But in other areas, progress has barely begun. A few startups are trying to bring machine learning into the world of providers and payers, to offer insight-based recommendations about care gaps and treatment. And a few researchers are studying the use of deep learning for pattern recognition in radiology and pathology imaging. But substantive advances are years away.

On the clinical side, Davenport says, "The biggest changes are in the institutions that have more data—combined provider/payer organizations like Geisinger and Kaiser—who absorb the risk of care and need to make informed decisions about it, and are more focused on treating the entire patient and keeping the patient as well as possible. But even there it's still early days."

Healthcare organizations that haven't already started to implement analytics may never catch up, Davenport warns. "This is not an area where it's going to be successful to take a fast-follower strategy, because it requires so much data, so much learning, and so much trial and error over time."

This episode is part of a special series of interviews with speakers at the AI Applications Summit produced by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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Milind Kamkolkar joined Cellarity in January 2019 to help the company to prove that it is now possible to "encode a cell" digitally—to use big data, deep learning, and other methods to model many different interconnected networks of molecular interactions.  "The whole idea...is really only feasible now," he says. "What changed over the last number of years is the ability to compute at scale."

The promise of Cellarity's computational models, Kamkolkar says, is that they look broadly at cell behavior, rather than taking a reductionist approach. "If you could see the forest and the trees, what does that look like?" he says. "Really taking into account all of these networks that exist not only at the molecular level, not only at the cellular level, but also at the tissue level, and being able to look at all of it at once. You could argue it sound quite preposterous, but I love the ambition."

Kamkolkar joined Cellarity from Sanofi, where he was the industry's first enterprise chief data officer, driving the transformation of Sanofi into a data-driven organization. Previously he was the global head of data science and AI and digital medicine at Novartis.

This episode is part of a special series of interviews with speakers at the AI Applications Summit produced by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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One of the most amazing successes in the battle against cancer over the last two decades has been the introduction of antibody drugs that harness the body’s own immune system to kill tumor cells. Finding those drugs may sound like a biology problem rather than a machine learning or a big-data problem. But actually, these days, it’s both. Harry's guest this week is Leonard Wossnig, who’s the chief technology officer for a UK company called LabGenius. The company uses a combination of synthetic biology, high-throughput assays, and machine learning to hunt for new drugs within a subclass of antibody medicines called T cell engagers that, loosely speaking, can grab tumor cells with one end and then grab tumor-killing T cells from the bloodstream with the other end. And Wossnig says the key to the whole thing is having the best data possible—meaning, data about their candidate T cell engagers and how specifically they bind to their targets in the lab assays. LabGenius has built an automated platform called EVA that runs experiment after experiment and uses active learning to zero in on T cell engagers with just the right ability to bind to their intended targets. One of the big takeaways from the interview is that companies that want to use AI to speed up drug discovery need the biggest, cleanest, and most consistent data sets possible.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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Dr. Alan Copperman is director of the Division of Reproductive Endocrinology and Infertility and Vice Chairman of the Department of Obstetrics, Gynecology, and Reproductive Science at the Mount Sinai Health System. He's also a clinical professor of Obstetrics, Gynecology, and Reproductive Science at the Icahn School of Medicine at Mount Sinai; medical director of Reproductive Medicine Associates of New York, one of the world's leading IVF centers; chief medical officer at Semaphore Genomics, a health intelligence company; and medical director at Progyny, a benefits management company.

Copperman tells Harry that data first came into his practice in a major way at RMA, which needed to "learn about what the best way is to take care of patients to optimize their success rates. We fell back on that term that you use, 'MoneyBall Medicine,' because we want to have the best embryologists, the best egg-retrieving doctors, the best embryo-transferring doctors. We want to put a team on the field that optimizes the success rate for every couple who walks into our doors...I just got excited about using information to drive better decisions."

Copperman notes that in his career he's moved from operating on organ systems—the uterus and the Fallopian tubes—to operating at the cellular level, biopsying individual eggs, sperm, and embryoes. "Running next-gen sequencing, we get close to a million data points on every embryo we biopsy to figure out if they're healthy or not," Copperman says. "We need mathematicians to interpret genetic code, then we have to translate it back to a human level and develop decision support tools so that doctors can talk to patients. So it starts off with patients and ends in patients, but the pathway is just so completely different than it was three years ago, no less 30 years ago."

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Gini Desphande says she likes to think of "AI" as augmented intelligence rather than artificial intelligence: a system of human plus machine intelligence that can speed up drug development and cut R&D costs and failure rates in clinical trials. AI "really isn't at the point where it's automatable," she says. "We still need a lot of human intelligence to be coupled with this technology, to determine what are the questions you want to ask and to evaluate all the targets that come out, to say 'Do these make sense?'"

NuMedii's specialty is analyzing bulk tissue to isolate gene sequences in single cells that can point to new drug targets and drug candidates for diseases such as idiopathic pulmonary fibrosis. "The AI component helps us look at new targets that are not obvious to the human eye," she says. "It enables us to find network-level connections between diseases of interest and targets that are relevant for that disease. We can look at which nodes are coming into play and which ones should be manipulated for a particular disease."

This episode is part of a special series of interviews with speakers at the AI Applications Summit produced by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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Dr. Chris Boone, vice president and lead for global medical epidemiology and big data analysis at Pfizer, is a health futurist, social entrepreneurs, executive, professor, patient advocate, and self-proclaimed "data hippie." He says he long aimed to be CEO of a health system, but eventually embraced his "true self" as a student of informatics, business intelligence, and big data analytics. 

"I come into the world of pharma not as a conventional or traditional pharma guy but as someone who cut his teeth in the provider world," he says. "It's just something that came naturally to me. There was always an intellectual curiosity about how we can do things better, and how we could ultimately disrupt the way that we currently treat patients, and ultimately transform the system for the betterment of patients."

In the pharma business, he believes that big data analytics can disrupt clinical research and development and ultimately the commercialization of therapies for patients. He's an advocate for the use of real-world data and evidence, AI, and machine learning to accelerate the process of proving a drug's effectiveness, ultimately curbing the rising costs of drug development. 

That real-world data can include clinical data, EHR data, lab test results, claims data, molecular profiling, data from wearable health-monitoring devices, environmental factors, and patient diaries. "We're trying to create alternative ways to generate evidence that are acceptable to regulators," Boone says.

This episode is part of a special series featuring speakers from the AI Applications Summit, produced in Boston by Corey Lane Partners. Check out the full show notes and other MoneyBall Medicine episodes at our website.

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Harry talks with Kevin Tabb, MD, the CEO and president of Beth Israel Lahey Health, the product of Lahey Health's merger this spring with Beth Israel Deaconess Medical Center (BIDMC) and several other hospitals in the Boston region. How does Dr. Tabb manage change inside a growing organization that—by his own admission—has to build and implement new tools, processes and the actionable data it needs to evolve beyond the fee-for-service era.

Dr. Tabb was CEO of BIDMC before the merger, and previously served as chief medical officer at Stanford Hospital & Clinics in Stanford, CA, as well as head of the clinical data service division at GE Healthcare IT. Raised in Berkeley, CA, he emigrated to Israel at the age of 18, served in the Israel Defense Forces, studied medicine at Hebrew University's Hadassah Medical School, and served as a resident in internal medicine at Hadassah Hospital.

Tabb says the most significant challenge for healthcare leaders is "figuring out how to calibrate the pace of change," in particular the gradual but accelerating change in business models from fee-for-service to outcomes-based global payments, and the shift toward "treating patients as people" and focusing on health rather than sickness. The big question, he says, is "How far ahead of the curve should we get, so that we’re ready for the significant changes to come, but not so far haead that we’ve shot ourselves in the foot and can't survive the interim period."

The task requires "constant calibration" and "is more of an art than a science," Tabb says. But three key tools can help healthcare organizations manage the transition, he says: good, actionable information; incentives (monetary or otherwise) that are aligned among parties; and defined toolkits for change (which could include, but should never be limited to, new technologies).

Check out the full show notes and other MoneyBall Medicine episodes at our website.

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Harry talks this week with Salesforce's vice president of strategic research, Peter Coffee. The computer-industry veteran and former tech columist says that in the era of 1) outcomes-based payments for medical care, 2) an aging patient base, and 3) ubiquitous sensors and continuous data collection, there's a huge opportunity—and financial incentive—for healthcare providers to employ technology platforms that improve the client experience.

Might Salesforce end up marketing such a platform? Coffee says it's logical for the company, best known for its cloud-based customer relationship management software, to think about offering hospitals or medical service providers a configurable, CRM-style system for managing patient intake, consultations, recurring exam schedules, transportation to clinics, and the like.

Coffee says Traditional healthcare organizations didn't have the insights or incentives to think about improving long-term wellness or keeping their customers (patients) happy—just the opposite, in fact. "If you didn't diet and you didn't exercise, you ended up consuming more procedures, for which they would get paid," he says. "So what you have to do is shift the point of rotation to where the patients' health and the providers' incentives are aligned with each other."

That means pivoting to a data-driven model for managing service to patients—but not necessarily using centralized or concentrated systems. Coffee points out that Salesforce's architecture allows participation by thousands of third-party developers, potentially helping patientst themselves take ownership and control of their data.

If insurers also bought into this larger shift, they could transform themselves from "a necessary evil of payment management" into "the primary custodian of your wellness" and a force for efficiency and savings, Coffee also tells Harry. "The people who are the payers today know a lot about where the unnecessary friction and areas of process cost are arising in the system," he notes.

"You put all of these things together," Coffee says, "and you have the necessity, the opportunity, and the capacity to deliver the kind of transformational change that I think we all agree healthcare is ready to enjoy for the first time in centuries."

Find Salesforce's 2017 Connected Patient Report here.

Check out the full show notes and other MoneyBall Medicine episodes at our website.

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As one of the researchers involved in the 70-year-long Framingham Heart Study, Rhoda Au is in a unique position to investigate whether changes in speech patterns in middle-aged people could prefigure the onset of Alzheimer’s disease later in life, and whether early detection might give patients more time to take preventative measures. She’s been part of the Framingham study since 1990, and she’s applying voice analysis software to 9,000 digital audio recordings of neuropsychological exams of Framingham patients to see whether there were telltale biomarkers in the speech of patients who went on to develop dementia.

Au is a professor of anatomy and neurobiology at Boston University, a professor of epidemiology at Boston University School of Public Health, a senior fellow at the Institute for Health Systems Innovation and Policy at BU’s Questrom School of Business, and the Framingham study’s director of neuropsychology.

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Kathryn Teng, MD, is division chief of internal medicine and community medicine at MetroHealth, one of three major healthcare systems serving Cleveland and the rest of Cuyahoga County in Ohio. She believes that healthcare costs are out of control in part because too many patients go directly to specialists about issues that their primary care physician or nurses could and should handle. But figuring out how many primary care doctors a big healthcare system like MetroHealth needs, and where they should be placed, is a data, analytics, and management problem.

When she arrived at MetroHealth in 2015, Teng set out to collect data points to help with decisions across what she calls the “four quadrants” of population health: access to care, patient experience, provider and caregiver experience, and lower costs. “The real joy in this job,” Teng says, “is really around…trying to achieve the vision of population health, which is how do we provide the right care for the right patients by the right team members, and in the right modality.”

For more information this episode and access to all of our past episodes, go to https://glorikian.com/podcast/

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Many of the processes carried out in traditional chemistry labs searching for new drugs or drug targets can be sped up through factory-style automation—and in fact, “combinatorial chemistry” was a big boost for the field. But Alán Aspuru-Guzik, a theoretical chemist in the departments of chemistry and computer science at the University of Toronto, says “the transition to autonomy is what we really want.” Think of a “self-driving chemical lab” that uses big data, AI, and robotics to explore chemical space through a cycle of synthesis, characterization, and testing: that’s what happening both at Aspuru-Guzik’s Cambridge, MA-based startup Kebotix, in cooperation with commercial partners, and at his lab in Toronto, where he holds the Canada 150 Research Chair in Theoretical Chemistry. “We’re trying to put together the molecular Lego pieces, with a finite set of reactions and fragments,” Aspuru-Guzik says. “The art of being successful is not getting lost in an infinite forest of possibilities.”

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Dr. Jennifer Carter says it was watching friends and family members stricken with cancer struggle navigate the complexities of the healthcare system in the early 2000s that inspired her to start a company in the area of precision medicine. At that time, the development of targeted therapies for cancers with specific genetic markers was already offering new hope to patients, but it was also creating new challenges for doctors and patients, who had to digest, manage, and interpret unprecedented amounts of data. The vision of her company N-of-One, she says, was around "how do you create something that could cut across all the different stakeholders and create the knowledge necessary that connected physicians and patients with cutting edge diagnosic and treatment strategies in a way that made it understandable and accessible." That ended up being "a very good strategy for physicians, patients, and the company," Carter says—an observation confirmed by QIAGEN's acquisition of N-of-One in January 2019.

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Adjusting to a more collaborative style may take doctors some time, says Dr. Mark Boguski, but if they stop confining themselves to disciplinary boundaries, they'll be able to see connections between different areas of medicine that aren't taught in medical schools. Boguski draws on examples from oncology, where he says doctors are gradually being retrained to think in terms of disease pathways instead of discreet organ systems.

Dr. Boguski is the chief medical officer of Liberty Biosecurity and founder of the Precision Medicine Network. He's a member of the U.S. National Academy of Medicine and a fellow of the College of American Pathologists and the American College of Medical Informatics. He's served on the faculties of the U.S. National Institutes of Health, the Johns Hopkins University School of Medicine, and Harvard Medical School, and as an executive in the biotech and pharmaceutical industries. He is the former vice president and global head of genome and protein sciences at Novartis, and a graduate of the medical scientist training program at the University of Washington in St. Louis. He has written a series of books on cancer for the general public, under the series title "Reimagining Cancer."

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Harry Glorikian: Welcome to the Moneyball medicine podcast,

Dr. Boguski: It's a pleasure to be here Harry.

Harry Glorikian: So, Mark I was reading that statement and when I hear a statement like that that I read at the top of the show I step back and I think systems biology, not necessarily disparate pieces. And so, it seems like over time if I go back to doctors, you know they'd look at the patient as a whole and now it looks like we're looking at them in pieces.

Dr. Boguski: It's actually worse than that you know when I was in medical school we actually did physical rounds on the patient - on the patients on our floor, you know we'd go around to the bedside and examine every one of them. Today people do rounds in a conference room sitting in front of their laptops and there's actually less patient interaction, than there used to be.

Harry Glorikian: You also say like it doesn't stop there, by looking at the bigger picture and not confining ourselves to disciplinary boundaries. We'll be able to make connections between different fields of medicine and glean information, that isn't taught yet in medical schools.

Gaining insights that have the potential to transform medicine and when I hear that, I think again systems biology but how data is going to help us reassemble the parts because there's so much detail in each part.

Dr. Boguski: So, let's start with oncology because considering revolutionizing all of healthcare is just too big a bite to take. The example that of interdisciplinary interaction that you mentioned plays out in something called a tumor board or the multidisciplinary tumor board. Where all of the sub specialties are our representative the medical oncologist, the surgical oncologist, the radiation oncologist, cancer genetic counselors, advanced practice nurses, radiologists they all come together to discuss a case and that's where the multidisciplinary input occurs.

The problem is they only happen once a week or maybe twice a month and that's not helping individual patients in real time. So, there's something we've been working on in conjunction with Dr. Mike La Posada at the University of Texas Galveston called the diagnostic management team, and I see this team as an integrator of the various data inputs from different specialties. Where a group comes together and reaches a consensus interpretation of all these data streams coming into effect, one patient at a time.

Harry Glorikian: Well that was going to be one of my comments and I had put together a piece on this on my blog but it is it time to just - when I think about corporate worlds we used to always joke that when there was a reorg, the CEO had read some book or something and now we're reorg-ing around it, but I don't know if I've ever seen a reorg in medicine based on where technology is going and are we at the point where let's take oncology. Do we need to reorganize oncology do we need to forget about the organ per se I mean I don't want to take it away from the surgical oncologist, because they need to understand that organ and actually work on it. But the treating oncologist I mean we're doing basket studies, we're looking at pathways we're understanding how drugs perturb a pathway.

You know that has nothing to do with the where the it is in the body and I feel like if we look at AIDS and we're playing - we played whack-a-mole and now we understand how to beat it back. Oncology is sort of following in the same footsteps of once you perturbed two maybe three pathways, you've sort of cut off the lifeblood of whatever this you know mistake that's happening in the body is going on.

Are we at the point of organizations or institutions need to really rethink how they do this for the benefit of patients?

Dr. Boguski: Absolutely we are, as an aside before I come back to describing the situation more in detail. I'll tell you the biggest problem is change management it's getting people to behave in in ways that they weren't trained to they're not comfortable with and may take some extra time initially for them to learn and this diagnostic management team concept is one of those things that people will have to be motivated to adopt.

So, with respect to the – to oncology but when I was at Novartis back in 2009, 2007. I'm sorry 2005, 2007 there were only a handful less than five targeted therapies. The first one dates back to the late 1990s it was Herceptin the second one was Gleevec which is approved around 2001 2002 but even then we foresaw a day when the FDA would approve drugs, not based on the Oregon system in which the tumor originated but the underlying molecular pathway.

So, let's say that was back in 2006 that we thought that would eventually happen. It's actually taken until 2017 for that actually to happen there was a drug Pember ilysm app that had previously been indicated from melanoma, but was finally indicated for any tumor of any tissue origin that had DNA mismatch repair as its molecular genotype and phenotype.

So, the biggest challenge in oncology and is really educating the up-and-coming oncologists and pathologists to think in a systems way, to think in terms of pathways in that organ systems.

Harry Glorikian: But it I look at it two ways, one is do we need to rethink med school from that perspective because there's data streams coming from everywhere now. The other thing is let's face it if the institution you go into like the corporation is structured a certain way, you file into the structure in it.

So, if you had a computer science group or a data analytics group that was associated with the treating oncologist and you know a tool booth said, you know if you're not using genomics in oncology today it's like driving at night without headlights. Wouldn't that force the specialty to go down a certain road and we I'm assuming we would see a benefit towards patients?

Dr. Boguski: So, here's the deal when I listen to Barrett Rollins podcast which was excellent. I think he kind of left out one thing. And that is when you talk to the head of an NCI Cancer Center they only treat about 10% or 15% of cancer patients in the U.S. so if you really want to have an impact you want to get to that what I call the 80% market. Which is private practice or group practice on college in community hospitals and regional health systems and the reason I bring that up is because I - not long ago I was talking to the president of a major Oncology Group with 1,500 oncologists in you know in a wide group of practices all over the country.

 And according to him about 2/3 of his oncologists never heard of DNA you know don't really want to learn about it and they're thinking of retiring early because they can't understand you know this the subject matter.

Harry Glorikian: But that's crazy - I mean but that's insane I mean - I think about that - I hear that all the time and it absolutely just floors me, because I think to myself the patient is getting the incredible short end of the stick right? We always talk about health care cost going up, well if you're not treating them the right way of you know I would think that of course you're not you know health care costs will go up because you're not getting the best outcome. What do we need to do to turn the ship faster?

Dr. Boguski: So, what you have to realize is that these Doc's who don't really know about DNA we're never trained in it. I mean you know a generation ago or half a generation ago genomics didn't really exist in the typical training of an oncology fellow or even going back to medical school, not everyone was a specialist in genomics or over immunology now the dominant science in oncology is genomics and immune-oncology and the practitioners particularly those outside academic medical centers just simply don't have the background to understand what these tests can provide.

And so, we identified, you know the major gap is really an educational one they need tools and hopefully on a mobile platform that they can consult in real time and not have to take extra time out of their day to go and read you know 25 papers in the literature. They have fingertip access to the latest knowledge about biomarkers and pathways cetera and overtime is they use these essential tools over and over again. You know that will help educate them to take advantage of some of these modern diagnostics.

Harry Glorikian: Well I always think to myself like if we think about the super consolidation that's been happening in medicine over the past say since the Affordable Care Act has come into play we're not talking about the one to little hospitals that are sitting out there, now there - they're big conglomerates for lack of a better term.

 I would think they would be able to create an internal group that would then assist or read out to everybody out there think of it like a central HR group in a sense but I want to step back and so we've known each other for a long time. What are you doing these days? What are you focused on at Liberty and just give us a little bit of background there?

Dr. Boguski: Sure, well Liberty biosecurity is a company that views the biggest threats to human health in the 21st century as biological threats and these can be man-made biological threats or simply the result of shifting ecosystems as a consequence of climate change or they can just be really hard medical problems, that no one else is cracked yet. So, we brought together a multidisciplinary group of people. Who are connected in a way that we're only sort of one node away from anyone that we can live that we need to help solve a problem.

So, we're working on two major things now I'll describe oncology first because one of the reasons that it's hard to innovate in oncology is people try to do it in the United States. Where there's a lot of legacy institutions - you know legacy standard of care. It's very difficult to innovate in a system that's already running a certain way.

So, we're actually happening helping the government of Thailand and one of the largest companies in Thailand kind of reinvent medicine. We call it skipping the lane line and it's pretty obvious what that metaphor means, but we're trying to in conjunction with the government which has this concept of tylium 4.0. That concept involves changing the economy from an industrial economy to a smart economy and skipping the land line in the process.

So, we're helping set up several advanced cancer research centers and existing hospitals and these will be dry runs or trials or pilot projects that will eventually be incorporated into a new physical institution called CP Medical Center, which is due to opening in about four and a half years.

Harry Glorikian: If I gave you a whiteboard right now and you were to redraw oncology, how would you redraw it? How would you incorporate genomics, digital ecology, image analysis? How would you just walk through that quickly?

Dr. Boguski: Well it's interesting because in the design of this new hospital they really have to think about how to juxtapose different departments and divisions and so we have a Greenfield situation here, where we can help them put together things that were separate that belong together and then sort of the transformation of oncology.

So, you want radiology to be right next to pathology because these are the two diagnostic specialties and 60 days 70% of clinical decisions are made on data that comes out of the pathology lab. So, I think Eric Topol is the first person who really called this out explicitly but I would combine radiology and pathology into a new specialty called rad path in which their primary job is to synthesize data streams into a report that can be used by the clinicians. So, that that's one of the things I would do. I would also transform tumor boards into more frequent real-time diagnostic management team meetings that meet more frequently, that meet in time to make a therapeutic decision at the time when one is being made. And those are two of the things that we will be experimenting with in this time and at CP Medical Center.

Harry Glorikian:  I'm always thinking that when you analyze an image and utilize the machine learning or artificial intelligence or all the different methodologies necessarily that are out there today, I think the systems do an amazing job of seeing things that a person might not be able to see. When I was interviewing Massimo blue Cemil, in one of the podcasts. He was saying they've come up with a way of having the pixel sort of look at each neighboring pixel and you can see a blockage in an artery when it's not visible to the naked eye. And the machine can actually look at images that aren't necessarily easily visible to a human eye, so we get a processed image whereas the machine can look at raw data.

Where do you see that sort of capability going and is it going to advance what we're doing in the medical area?

Dr. Boguski: So, I'm a pathologist by training so I'll signal that bias upfront but as you've said in the introduction and I do take a more systems view of Medicine because I've not only been a pathologist in my career but I've been in genomics, bioinformatics I've been in the biotech sector, I've been in the pharma sector and I'm seeing the problem from many different angles. So, getting back to pathology, pathology has been criticised for not adopting digital technologies sooner and they're often compared with radiology who adopted them you know almost overnight.

You know the problem between the two fields is that with pathology you still have to remove something from the body and process it in a laboratory before you can digitize it. So, the savings that you realize from not having in in radiology not having film libraries and chemical tanks to develop x-ray film, that that changed the economy of radiology. It's harder to do in pathology and so as I'll just have been slower to adopt it and also because pathology departments, all those 60 70 % of clinical decisions are made on their output.

They tend to be viewed as cost centers in their health system not nonprofit centers and so everything, you know if you look at the c-suite. They want every test to be as cheap as a complete blood count or urinalysis and with genomics and digital pathology, whole slide imaging you know that's not what it costs. So, people have to retool and recapitalize their equipment in order to fully realize the value of digital pathology.

But as you said once that's done, we can use it to augment humans by pre-processing the slides and pointing out suspicious areas that pathologists need to put their human eye on, we can also use it to spot things that you a pathologist might not spot. Actually, let me let me express that a different way, so one of the diagnostic modalities for predicting the efficacy of immune oncology drugs is of the body's immune response to the tumor.

Now that's done right now with anti PDO antibodies, it's just a brown stain on a regular microscope slide in a DNA setting, it's done with tumor mutational burden. But both of these things are really surrogate markers for lymphocytic infiltrate in the tumor and pathologists don't normally have the time to manually count all the lymphocytes associated with a tumor. A computer can do that in two seconds and but you know just imagine being able to replace expensive time-consuming the long term around time tests with just an AI or machine learning application on a $2.00 HNE microscope slide. So, that's where part of the potential really makes sense.

Harry Glorikian: Yep and I think in some ways it would help standardize the process, right? You and I both know you go from institution to institution you will get a different answer depending on who's looking down the barrel of that scope. So, you know interestingly enough I was also reading sort of you know there was a paper presented at the 2018 machine learning for healthcare conference at Stanford University. Where you know - MIT Media Lab researchers so we're not even talking about you know a university hospital or something like that but MIT Media Lab researchers detailed a model that could change the dosing regimes, to be less toxic specifically in glioblastoma with a self-learning technique where the model sort of literally of just dosages, eventually finding an optimal treatment path with the lowest possible potency and frequency of doses that should still reduce the tumor size to a you know degree comparable with traditional regimes. And you know they showed that this seems to be working quite well. How do you see something like that being incorporated in this practice of oncology? Because it seems that technology when applied across a number of areas, should have a probability of increasing outcomes, yet decreasing cost over time. I understand that there's going to be an initial bite to take all this on but it's just like anything else we do in corporate America. You got to spend it upfront and then you realize the savings on the back end.

Dr. Boguski: Right that's why you have to take a systems view of the healthcare system or you know or an individual - a hospital system. Again, each department is either a profit center or cost center and that's not a holistic view of the value that the diagnostic laboratory supplies. Getting back to more directly answer your question I think one thing that's never mentioned you know people talk about the DNA driven data transformation of oncology but one of the nuances, that is seldom is the common networks of therapy. So, let me give you an example for they're both targeted therapies and immunotherapies for melanoma and lung cancer and many of the solid tumors.

In fact, for melanoma there are there are six different targeted drugs you can try and there are two immunotherapies you can try or you can try some combination. So, where computers are really necessary and figuring out the best common it's a real possibility given an individual patient or a patient avatar that looks like that patient. So, back in the day when there were only six targeted drugs you could figure that out on your head. Right you know today there's about a hundred and fifteen targeted drugs or immunotherapies we're going tissue agnostic.

What the heck do you do with the combinatoric of that kind of pharmaceutical armamentarium you have in front of you now?

Harry Glorikian: Oh, I remember I you know I could almost when it when all this first started you could keep up with the papers.  I can't possibly even try - so if you didn't have a system to help you in some way, I don't know how you would manage between the gene, the drug, all the other details around a patient and how do you keep that straight, I don't know how you would practice what you practice. It would be like you know flying a plane without all the other instrumentation around you.

Dr. Boguski: Yeah so this is the the missing link in oncology and pathology training now, it's training our future oncologists and pathologists to think in systems biology ways to teach them enough about combinatorics. So, they apply those principles to what's coming out of a eyes and machine learning algorithms and have the ability to synthesize them based on at least some understanding of the underlying technologies that lead to these data streams.

Harry Glorikian: So, what do you think the changes are that we need to make and institutions today to get the I don't want to say the biggest bang for the buck but before lack of a better term, it is a business. But at the same time we're need to be looking at patients right? and I always try and tell people that talk to me about oncology issues that they have is always remember that the person on the other end of this yes they want your best interest but it is still a business, so there there's sort of interesting ways to look at that. Where do you believe that this is going?

Dr. Boguski: Well I'll answer that - my first thing that I'm not a businessman but I know enough about business that when young people, who are thinking about are developing new technologies come to me for advice or small companies ask me what they can do to get their methodology or their technology incorporated into the workflow physicians. I said you're aiming at the wrong target, you've got to develop a value proposition for the c-suite and not just think that that Oncologists are going to adopt this because again there's two challenges, it's how to how to support it from a revenue point of view and in the change management it's getting them things to do differently so it's really dual targets for introducing new technologies and new operating systems and new standard of care. It has to make sense to the c-suite it has to make sense to the practitioners and it's that combination, I think that you have to convince to adopt a new way of doing things.

Harry Glorikian: So, just shifting gears for one I'm not actually shifting gears and we're moving it up the pathway in a sense is how do you feel about liquid biopsies? As the next generation of where we're going with this, as opposed to actually looking at the tumor. You know, I know right now it's approved for treatment monitoring, right because we can actually, we knew there was a tumor we right?

But I'm thinking about how do you think about it from a treatment monitoring perspective but then ultimately there's no reason why we couldn't see something before it actually happens.

Dr.Boguski: So, I'm very excited about liquid biopsies. I think there's a lot of work to do yet before they become routine for cancer care, but I think about them this way. The standard of care now in terms of clinical practice and a sort of FDA approval is imaging.

You treat a patient with a drug, you're doing you know some sort of Radiologic study to show that the drug is working and you often monitor response to therapy that shows visually that the tumor is shrinking. You know what if you could replace all of that expensive technology and logistics with a simple blood draw and get the answer in in a couple of days, rather than have you know your radiology exam scheduled you know a month or three weeks in advance? So, that's one thing there - there's a cost-benefit ratio to the conceiving of replacing radiologic imaging with liquid biopsy.

The other thing it could be it could be much cheaper it's not yet but cost turnaround time and the ability to detect the presence of a tumor before it's even visible by radiology is another big potential advantage. In fact, I know one little company that can actually has technology that you can tell from the DNA sample collected from the blood, which tissue the mutations are likely to be coming from that's exciting technology too because it can direct your attention to where you might want to concentrate the imaging resources.

Harry Glorikian: Well I keep thinking about you know these technologies will also - can also cause a complete shift in the business model in other words I could go to CVS, and you know with one of these non-phlebotomist oriented technologies, draw blood ship it off, have it done and now instead of the patient driving fifty to a hundred miles in some cases to an institution. Everybody could be sort of monitored on a regular basis.

Dr. Boguski: That's particularly intriguing you know given the work that we're doing in Thailand because the CP group owns the 7-eleven brand for Asia, and you know they're thinking holistically about this monitoring patients in the community without having them coming to the hospital, you know and have an expensive time-consuming radiology scan. When they might be able to just drop into their local retail pharmacy and have the test done there.

Harry Glorikian: Well that's when I think about CVS and Aetna I mean if if you go into the hospital, they sort of lose right because now they have to pay. Whereas if they're able to sort of monitor you or keep you healthier at their local CVS. They change the economics of this and so you know telemedicine is the other area, where something happens as they see something in the CVS. Well the doc can technically be right there. They don't need to be at an institution, so it's interesting how this whole shift is happening from technology enabled medicine. And I know that's heresy and the worlds were used to without where we come from but you see it how technology has affected everything else and so I think you know we're at the cusp of a revolutionary shift, now whether the institutions can shift as quickly is the part that worries me the most.

Dr. Boguski: Well again it gets back to innovating in in the U.S. so many things are ingrained in our healthcare system that it's very difficult to innovate in any one step of the process when it affects upstream and downstream activities as well as the economics of it. and again that's why this opportunity to work with the government and major a major company and Thailand gives us a better shot at changing things over the next four to five years, because they're motivated to become a smart economy, skip the landline and go right into some of these new clinical and business models that you're describing.

Harry Glorikian: It's interesting I wish we could do that here but I don't think that's gonna happen anytime soon except from external forces like Aetna CVS, Walgreens and you know maybe Humana or any of these other groups that are coming together or maybe Apple, Amazon or these other different groups that are out there. I know you had listened to a couple of the earlier podcasts on precision medicine and you had said to me a few things were missing or what's burning, what did we what did we leave out that we should have put in there?

Dr. Boguski: Well there there's a lack of organized training the neck for the next generation of oncologists and pathologists into this new way of thinking. Now eventually by generational turnover and things like that the you know oncologists will begin thinking in more of a systems biology, tissue, agnostic manner. Again, Anatomy will always be important for surgical oncologist and radiation oncologists, so we don't want to we don't want to ignore them because their therapies are anatomically directed but more and more of medical oncology is going to be tissue agnostic and we're simply not training our residents and fellows in this way of thinking.

They're still being trained in a in a fairly traditional manner.

Harry Glorikian: It's interesting well I mean I always think when Kaiser announced they were going to open their own medical system, now this was post Affordable Care Act because they could see that things were moving to a value-based as opposed to fee-for-service. Do you think we need more medical schools along those lines to really get us to where we're going?

Dr. Boguski: Yes, I do and the reason is, is that again even in medical schools that want to do this there was a lot of tradition. You know it's the professor of teaching you know his or her subspecialty and there's not as much opportunity to integrate in a systems biology mindset in those traditional teaching models. I know Harvard Medical School teaches their curriculum based on system biology now, but not up not every Medical School has adopted that yet.

So, I think it will take some new medical schools that train and in some rudiments of computer science and in statistics in order for the practitioners not to become you know the AI specialists but simply to understand where those data come from. So, they can they they're they can trust the data coming from human augmented machines.

Harry Glorikian: Well it's interesting right if you think that physicians will also be measured based on performance and outcome, just like regular corporate America, right? That they're gonna want to go to institutions that give them the tools to be the best not just go to school per se but become even more choosy then maybe then they already are about where they attend school to be able to be the best at what they do.

Dr. Boguski: So. how do you how do you do that marketing and communication you know that that's another challenge you know it's change management and marketing and communication. These two things are often ignored or downplayed when you're trying to change your system people tend to focus on the technology and the bleeding edge science but they don't consider the mundane aspects of how do you get the message out and how do you how do you manage change among established practitioners.

Harry Glorikian: Well it's interesting, right when I look at a company and think about strategy the first thing I look at is the age of the management team and I don't mean to generalize, but it as a as a rule of thumb you know I think are they over 55 or under 55. And if they're over 55 it's generally what you see is a mentality of TTR, time to retirement alright and do I shift or do I just make sure that nothing screws up along the way.

And if it's under 50 right then I actually almost have to do something because I'm gonna be around for a while. So, I have to actually make some fundamental shift or put my mark on it and so again not to generalize because I know you know people like you and others that are on the bleeding edge of change, but I think that those you guys might be the exception as opposed to the norm.

Dr. Boguski: Well I'm a big believer in neuroplasticity and I think anyone at any stage and age in their career can learn this stuff but they haven't had the tools to teach themselves, and I think that's been one of the missing links or big gaps in the way people think about this. They never think about how you're gonna market communicate and provide tools in order for the people who better learn to be able to readily learn.

Harry Glorikian: Well some people are very comfortable with change right and some people are not comfortable to change at all, as we all know. So is there anything else that you thought was a missing portion in in some of the areas that we talked about?

Dr. Boguski: No, I think we've pretty well covered it. I mean again the missing link is education and training both at the early career level but also in terms of continuing medical education and I think the other big challenge is focusing on convincing the c-suite that this is going to either reduce costs or improve patient outcomes or both, and it's convincing the physicians and in the c-suite executives as both groups in order to get changed really enacted.

Harry Glorikian: Mark, thanks so much it was great having you on the show and look forward to hearing how the Thailand experiment works out.

Dr. Boguski: Well let's get together again in six months to a year and I'll let you know.

Harry Glorikian: Okay, excellent thank you.

That's it for this episode hope you enjoyed the insights and discussion for more information, please feel free to go to www.glorycamp.com. Hope you join us next time, until then farewell.

The combination of better data and more powerful computing is helping researchers reinvent the process of discovering new drugs. Within 5-10 years, we’ll likely see a huge wave of new medicines that were either discovered or designed using AI—drugs that will finally help us get control of our most stubborn health problems, from cancer to cardiovascular disease to obesity and metabolic disorders to neurodegenerative diseases. And the biotech startups that will do most to contribute are the ones that have both proprietary data, and original ways to use AI to sift through that data. Harry's guests this week are from a startup called Pangea Bio that’s working hard on both. As Pangea's co-founder and COO, John Boghossian, and its president of AI, Sona Chandra, explain, the company specializes in gathering data from the natural world, especially data about compounds manufactured inside the cells of plants and fungi. They narrow down the possibilities by working with indigenous cultures to find the plants or mushrooms that people have already been using for centuries in traditional medicine. They've also built three separate computational platforms that filter through all that data, to single out the small molecules that have the biggest effects in the human body, especially the central nervous system. 

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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Harry's guest Sandy Aronson argues that AI and apps are not the solution for better healthcare; more effective care workflows enabled by AI and apps are the solution. Aronson is the executive director of information technology at Partners HealthCare Personalized Medicine. His team develops the IT infrastructure needed to support genetic-based personalized medicine in both patient-based and laboratory settings. This episode is the second in a two-part series on getting AI, machine learning, and analytics working in the healthcare provider setting, recorded as part of the AI World conference produced by Cambridge Innovation Institute in Boston in December 2018.

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What if we could use machine learning to train software to read CT scans of patients with intracranial hemorrhaging? Time to diagnosis could be doubled, potentially saving lives. This week Harry discusses such questions with Dr. Aalpen Patel, a physician-engineer who chairs Geisinger's department of radiology and directs is 3D imaging and printing laboratory.

This episode is the first in a two-part series on getting AI, machine learning, and analytics working in the healthcare provider setting, recorded as part of the AI World conference produced by Cambridge Innovation Institute in Boston in December 2018.

You can read a full transcript of this episode and browse all of our other episodes at glorikian.com/podcast.

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Harry's guest in this episode is Massimo Buscema, director of the Semieon Research Center in Rome, Italy, and a full professor at the University of Colorado at Denver. Buscema researches and consults internationally on the theory and applications of AI, artificial neural networks, and evolutionary algorithms. The conversation focuses on AI and its applications in healthcare, and how it can enhance what we can see and uncover what we cannot.

You can read a full transcript of this episode and browse all of our other episodes at glorikian.com/podcast/.

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Harry's guest for this episode is Dr. Barrett Rollins, the chief scientific officer and faculty dean for academic affairs at Boston's Dana Farber Cancer Institute and the Linde Family Professor of Medicine at Harvard Medical School. Harry and Dr. Rollins dig into how large-scale DNA analysis can one day put much more usable information into the hands of oncologists, and how that data affects individual patients, the practice of medicine, and new therapies under development.

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Harry's guest this week is Dr. Joel Dudley from the Icahn School of Medicine at Mount Sinai, where he serves as executive vice president of precision health, associate professor of genetics and genomic sciences, and founding director of the Institute for Next Generation Healthcare. Dr. Dudley explains how his group is utilizing data to uncover health problems that can't be detected through normal methods, as well as his groundbreaking paper on the link between Alzheimer's disease and herpes.

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Harry talks with Wim Van Hecke, the founder and CEO of Icometrix—builder of a cloud-based AI platform for analyzing brain MRI and CT scans—to find out how the startup's FDA-cleared technology is changing the way radiologists and other physicians interpret neuroimaging data.

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Harry's guest this week, Dr. Robert Green, is a professor of medicine and genetics at Harvard Medical School and director of the Genomes To People research program at Brigham & Women's Hospital and the Broad Institute of Harvard and MIT. They dig into the individual genome, how genomic data is being used, and the impact of genomics on various stakeholders in the healthcare system.

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Harry talks with Sharon Terry, president and CEO of Genetic Alliance, about the way drug discovery, diagnostics , and the treatment of patience are changing.

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Today's radiologists face a deluge of data, and their work can be tedious and error-prone. But should humans even act as radiologists? It's becoming clear that computers and humans working together are better than either alone. Harry's guest this week is Fabien Beckers, CEO of Arterys, a startup creating products at the intersection of AI, the cloud, and medical imaging. Beckers has led the growth of Arterys from four co-founders to a team of 100 today, as the company brings the first FDA-cleared cloud-based end-to-end platform for medical imaging and analytics to market. Beckers, who holds a PhD in quantum physics from the University of Cambridge and an MBA from Stanford, says his vision for the company is to accelerate data-driven medicine by combining consistent quantification of medical imaging, in combination with molecular, genomic, and patient history data.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Harry speaks with Wout Brusselaers, CEO and founder of Deep6.AI, about new technology that could help find patients for clinical trials in minutes rather than months. This is Part 6 of a six-part special series recorded at the AI Applications Summit, produced in Boston by Corey Lane Partners in October 2018.

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There are about 30 trillion human cells in your body, but there are about 38 trillion bacterial cells, mostly hanging out in your large intestine. And that’s not even counting all the viruses, fungi, protists, and other microbial cells that live on your skin, in your bloodstream, and all around your body. So in effect, what you think of as you is not really you. You’re actually a walking colony of many different organisms. All of which cooperate peacefully, for the most part—unless the balance goes awry, and then you can get very sick, very fast.

The microbiome has been getting more and more attention from researchers and doctors now that we’re starting to have the tools we need to identify and measure all those microbes and see what they’re up to. Harry's guest this week is serial healthcare and AI entrepreneur Leo Grady, whose company Jona is on a mission is to help patients and physicians keep up with the skyrocketing amount of scientific literature about the microbiome and try to translate it into real steps people can take to improve their health.

If you’re a Jona customer, you start by sending in a fecal sample. Then the company uses a large-scale gene sequencing technique called shotgun metagenomics to get a profile of all the microbes in your GI tract. Since everyone’s microbiome contains a different mix of microbes, the next step is to use large language models to sift through the published science about the microbiome and find the studies that relate to the specific bugs in your microbiome. Then the company gives patients and their doctors a report that parses out whether their microbiome makeup might be contributing to their health problems, and whether there might be any health or nutritional interventions that would help. It’s all in the early stages. And right now Jona’s test is mostly available through concierge medical services, executive health clinics, and other offices that do a lot of cash-pay tests. But Grady thinks that over the long term the service has the potential to turn the microbiome from a former black box into something closer to what he calls an “organ of data"—meaning a part of the body that doctors can, in a sense, visualize and analyze in the same way we can use MRI and other forms of imaging to scan our other organs.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

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Harry's guest is Guido Lanza, president and CEO of Numerate. Together they tackle the question: What if the introduction of AI into drug discovery allowed us to create a true learning loop? This is Part 5 of a special six-part series recorded at the AI Applications Summit, produced in Boston by Corey Lane Partners in October 2018.

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Harry asks Dr. Ron Alfa, vice president of discovery and product at Recursion Pharmaceuticals, what efficiencies could be achieved and what problems could be solved if data science were applied to drug discovery. This is Part 4 of a special six-part series recorded at the AI Applications Summit, produced in Boston by Corey Lane Partners in October 2018.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Harry's guest is Dr. Shrujal Baxi, medical director of Flatiron Health. On the agenda: how technology can help create the real-world evidence needed to achieve better patient outcomes and accelerate research into new areas. This is Part 3 of a 6-part special series recorded at the AI Applications Summit, produced in Boston by Corey Lane Partners in October 2018.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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In this episode Harry talks with Milind Kamkolkar, chief data officer at Sanofi, about how big pharma can start using new data sources to uncover new insights about disease. This is Part 2 of a special 6-part series of episodes recorded at the AI Applications Summit produced in Boston by Corey Lane Partners in October 2018.

How to rate MoneyBall Medicine on iTunes with an iPhone, iPad, or iPod touch:

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Harry interviews Andrew A. Radin, co-founder and CEO of twoXAR, a Mountain View, CA-based AI-driven drug discovery startup that unifies disparate data to identify potential disease treatments. This episode is Part 1 of a 6-part special series recorded at the AI Applications Summit produced in Boston by Corey Lane partners in October 2018.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Leapfrog Group president and CEO Leah Binder talks with Harry about data transparency and how it helps inform healthcare decisions by putting the right information in the hands of patients and employers.

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

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Transcript

Harry Glorikian: Welcome to the Money ball medicine podcast, I'm your host Harry glory camp. This series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious, value-based healthcare economy. We look at the challenges and opportunities they're facing and their predictions for the years to come.

So, my guest today is Leah Binder, Leah is the president and CEO of the Leapfrog Group. The Leapfrog Group represents employers and other purchasers of health care who call for improved safety and quality and hospitals. She is a regular contributor to forbes.com, The Huffington Post and The Wall Street Journal expert forum. She was named on Becker's list of the 50 most powerful people in healthcare in 2014, and consistently cited by modern health care among the 100 most influential people and top 25 women in healthcare.

She has served on numerous national boards and councils including the Institute of Medicine collaboration on patient engagement, the Health Care Financial Management Association Leadership Advisory Committee, but Corey healthcare systems advisory panel, AARP champions for Nursing strategic advisory council and the national priorities partnership board. Prior to her current position, she spent eight years as vice president at Franklin Community Health Network an award-winning rural hospital network in Farmington Maine.

he previously worked as a senior policy adviser for the office of mayor Rudolph Giuliani in New York City and started her career at the National League for nursing, where she handled policy and communications for more than six years. Thank you very much for joining me today, good to have you here.

Leah Binder: Well, thank you for having me, it's a privilege.

Harry Glorikian: So, we had spoken quite a bit back when I was putting together the book Money ball medicine but for those people who are not familiar with the Leapfrog Group, can you tell us a little bit about the group its mission and what you feel the biggest impacts are it's made to date?

Leah Binder: Sure and I want to congratulate you by the way on your book, I really thought it was fantastic. I've been giving it out to a lot of my colleagues I strongly recommend it. So, congratulations on an excellent book, I think it really captured some very important issues about where we are in healthcare right now. So, it's a great contribution.

Harry Glorikian: Thank you so much.

Leah Binder: The Leapfrog Group is a non-profit, we were national. We were founded in the year 2000 by a group of senior executives in large companies, who were concerned about healthcare quality and costs. They were particularly concerned about a report that came out from the Institute of Medicine, right around that time that was called to air is human. Which said that, upwards of a hundred thousand people were dying of preventable medical errors in hospitals every year.

And they were astounded by that number not only because that's a lot of people dying and when they did the numbers on their own covered lives, for many of them that meant one of their people were dying you know every day or every other day, because many of these executives such as companies like GE covered a lot of people. And they were not only were they just devastated to think that, their people were experiencing this kind of loss but also that they didn't know anything about it, that in spite of all of their efforts to try to improve healthcare and get their cost down and manage their health benefits well.

They had no idea that this was going on and this had such an impact, and so they wanted to change that, so they decided that it was time to have a more transparent marketplace in health care. They would through leapfrog this nonprofit, they would start to publicly report on the relative safety of every hospital in the country. And they would encourage their employees in the American public to shop for their hospital care to think about safety, before they walk in the door of a hospital. And in so doing not only would they protect their employees, but hopefully drive a market for improvements in safety and throughout the country and improve the quality of care nationally.

So, that's the goal, we publicly report on hospitals and now we're moving into other settings as well. But our goal is to collect information using the leverage of large purchasers, large companies typically a purchasers of health benefits, using their leverage to suggest to hospitals and other health systems that they give us information, that's otherwise not available. And then we publicly reported in the interest of giving consumers what information they deserve to have about how the health care system is doing.

Harry Glorikian: So, when I was writing the book, I found that even when patients were armed with data and information tools designed to help them decide between you know different healthcare providers. They don't seem to be using the tools you know to the degree that they can. Are patients ready to fully you know activate their emerging role take advantage of it or can we make technology easier to use for them in some way?

Leah Binder: Well, yes to both. Yes, consumers are increasingly using information but not anywhere near the level that they should, but that will change. We are in a very fast changing environment not only in healthcare but in general. I think consumers even five years ago shopped very differently for almost everything, and now that's changing as well in healthcare and it's been changing. And so, I think we're seeing those changes in the way consumers are using technology to make decisions about their own healthcare and when the Millennials get a little bit older just a little bit and they start to recognize their own mortality and need health care more. I think that's when we're going to see the real explosion in the change in healthcare.

Because they just aren't, there to intolerant of the idea that they cannot use a smartphone for instance to access pretty much any information they could possibly need to make it an important decision like a healthcare decision. So, I think that we're going to see a major shift in consumer use of technology. But I think that one of the big changes we've seen with this newly transparent environment and the is not as much on consumer behavior yet, but on how the healthcare industry itself functions, in anticipation of consumers increasingly using information to make those kinds of decision. And that's where we've seen I think some very significant shifts in the healthcare industry already.

Harry Glorikian: So, can you give me an example of where you're seeing that? I've seen a lot from CMS seems to be really pushing, you know wanting information to be transparent or putting information out there. But you know, what do you see happening really on the ground and can you give me a couple of examples?

Leah Binder: Sure one thing we're seeing with hospitals is an unbelievable focus on their own metrics. I've been out visiting a number of hospitals and I'm struck by how many of them have on their walls, information about how they're doing on a whole variety of patient safety metrics like, how many Falls and how many you know infections and etc. And many of them are putting this on walls that are accessible by patients but they're all over the place. I see metrics everywhere; this is a very different. I never saw that five years ago or very rarely saw that five years ago.

So, they're within health systems, they are communicating to clinician to clinician, how they're doing and they're looking at real card data to do that. And so there's just that level of internal transparent see that we're seeing, that does have a big impact I think on performance. And I think also there's a whole new job title in healthcare, and if you've seen this, I think this came about really it started about five years ago we started to see this, but now it's become much more ubiquitous this new brand new job title. Chief, usually called the chief engagement officer and so it's usually a c-suite title.

So, chief engagement officer reporting to the CEO of a health system, this person is usually responsible for patient engagement, how patients are experiencing the health care system. So, you think, well health care should have been doing that from the very beginning of course that should have been all about patient engagement, right. That's what everybody should be doing but you know for whatever reason and there's lots of reasons we could go into her an hour, they have not put the patients at the center of absolutely everything that goes on in healthcare, that's not the tradition in health care.

So, the fact that they're now seeing these new chief engagement officers emerge is another sign that health systems are truly changing their orientation to their work and recognizing that they have to pivot around new priorities, and the new priority is the patient. So, we're seeing a real shift.

Harry Glorikian: So, now do you believe that's driven by how the system is being compensated or is it competition or technology? What do you think is the driver?

Leah Binder: I wish, I could say it's driven by how they're paying, because how they're being paid because that would mean that where we're seeing what I would say is the most sustainable kind of change. If we were really paying healthcare and we had a different kind of economic infrastructure of our healthcare system, I would say that's a very long-term change that will benefit all of us. And I think many of, both within the healthcare system and outside of it, would like to see that happen and are pushing for that to happen and we're seeing certainly some inroads around that in. For example, in the notion of value-based payment etc., and we're seeing that happen, however I would not say that that right now is the driver.

I still think right now probably the majority of health care is paid fee-for-service with some significant inroads and other models, but still fee-for-service really does dominate the landscape in terms of the payment of health care, where I see it's driven though is that those who are paying is shifting and shifting in significant ways. Right now most large employers and many smaller employers have shifted toward high deductible health plans which are typically three-thousand-dollar deductible, for example for families or more for families and about 1,500 or more for individual plans and in that, underneath that deductible.

So, before you hit that deductible every dime has to be spent by the employee or the covered person, including drugs and other kinds of services that in more traditional health plans were already covered, even if you hadn't already spent it ductile. So, I'm not trying to give a boring lecture on insurance policy or anything, but the point is that for many people who are covered by health insurance they're actually paying most of their health care if not all of their health care in a given year out of their own pocket. And that is about one in three American workers now covered by one of these plans, that's a gigantic shifts happened over the past ten years.

It's gone from zero, covered by one of these to a third of all workers that's a major transformation of our health care system. It's been cited in lots of reports and research studies as a change in our thinking about health care. So, the people paying the bills and health care now is changing, and when consumers are paying out of their own pocket it does change the way they behave in the marketplace. As opposed to, for example paying just the standard copay, they're actually wanting to know what is that doctor going to bill me for this visit, and that changes how they think about their choice of that doctor and that service.

Now there's lots of debate about whether it's good or bad or whatever, but beyond that is just simply the fact that, that's changed the economics of health care. Which in turn has gotten the attention of health care providers at least, who recognized that they had better become more responsive to consumers because it's the consumers directly paying a lot of their bills.

Harry Glorikian: So based on that, what should have done senior healthcare IT leaders, you know startup companies you know we're hearing about Google and Amazon delivery. What can they do to sort of help the providers that are on the ground, you know clinicians, operational people you know improve healthcare delivery, you know on the ground you know. How to get them to think about it differently and how to get them to implement it?

Leah Binder: I think that for startups, one of the first pieces of advice I would have for any startup is, not to approach the healthcare market without someone on your team and in a very significant position on your team, who is from the healthcare system and very familiar with it and how your product or service integrates. I say that because, I see some startups that come into the market and they don't necessarily have a person who's that integrated who has that knowledge of the healthcare system. And they come in and they say, well I have this product and it will for example improve patient safety.

I can look at all the numbers and say that patient safety is nowhere near where it needs to be, and this product solves all problems in patient safety or many problems in patient safety. So, obviously it's going to be very popular, and we're going to do extremely well in the marketplace. They don't necessarily understand some of the barriers that have existed in the market and why great ideas around patient safety have not always sold the way they should in theory sell. And it takes really someone from within the healthcare system to understand some of the frankly insane nuances of the health care system.

There are things about the health care system there just don't make any sense in a normal market, so you have to have someone in the inside who understands that else. You can easily go down a road that sounds logical but doesn't make just don't work in healthcare. So, that's my first piece of advice for startups, but in terms of technology I think that technology that is easily accessible by consumers, is always going to be a good start for anyone. But it isn’t necessarily going to be immediately impactful and usable in healthcare.

It's a longer-term play, as I mentioned I think Millennials as they come more into the market as consumers are increasingly demanding that level of accessibility in the healthcare system. The new enterprises, we're seeing like CVS and Caremark and the work that we're seeing certainly with Warren Buffett etc. Amazon the entries in the marketplace of traditionally consumer focused, extremely innovative organizations into the health care system suggests that it's coming but it's not immediate. So, don't expect immediate overnight results, but it is something that will definitely be a tipping point soon. So, it would be great to be positioned in that marketplace.

Harry Glorikian: So, speaking of those of that trend is you know, what do you see is the top healthcare technology trends that are around the areas that you're really working in sort of transparency information. I keep thinking of like you know your smartphone knows exactly where you are and can give you pricing nearby or something like that. and then you know of course the big hot button right, AI machine learning and where is that playing a role. And what do you see happening in those areas, and who might be some of the companies that are driving in that area?

Leah Binder: I see a lot of work around AI for administration of claims data for purchasers and attempts to, I think one of the first efforts around AI with regard to purchasers was to try to see if you could predict who is going to need the most health services in the future. So, to try and look at claims and patterns of use of healthcare benefits to see if you can, you know identify those people who were most likely to for example have a heart attack in the next five years or something, and so to be able to intervene with them earlier.

I think that that has largely not yielded quite the results. I think everyone hoped for and I think now there the effort is really around at least that I'm seeing for purchasers is to really look at how can we identify the best practices, the best possible providers and help guide employees toward, or steer them toward those higher performing, more efficient providers. I'm seeing increasing efforts by purchasers for instance to give their employees services like second opinion services or other kinds of support, so they can navigate the health care system. And I think they're using AI a little bit to try and form the right kinds of networks and develop the right kinds of expertise that they need. Because even though leapfrog provides a lot, I mean for example my organization leapfrog provides a lot of quality and safety information, we don't pretend to provide enough of it.

And I think that employee and really the market, our information on quality safety transparency cost is really still at an early stage. And I think that employers are starting to use their claims data in more sophisticated ways to get at information that they can use right away as opposed to waiting for the rest of the country to catch up on quality and safety. So, I'm seeing a lot more aggressive efforts to help people navigate the health care system by employers.

Harry Glorikian: Obviously you are talking to the leaders of these employer led health plans and so forth -. What should they be doing more of or what could they be doing more off to drive this?

Leah Binder: Well, the first thing that they should really be doing is accessing or expecting their plans to access all of the data that's available. So, as I mentioned I don't think, we ever can say we have quite enough we're still in the early stages in some respects of getting as much data as we need, but there is good data that's out there. So, asking and insisting that their employees can access the best possible data, so they can make good decisions about where they're going to seek care and then use that data in innovative ways, and put money on the table for that.

There's companies like Ingersoll Rand for instance, who are actually providing incentives, financial incentives for employees to use their services that they provide to help employees navigate the system, so to give them information on you know which are doing a better job of and where they can get second opinions etc. So, when their employees use it, they actually get money in their health savings account. That's a really good and innovative way and I think that it's a simple way too. It's not all that complicated for employers to just say, we want you to just talk to them try to get a second opinion make sure you know what you should know about the performance of the providers, you're considering and then use it.

I think where we're going to see more technology come into play and I'm hopeful, but I haven't seen it happen yet but I would suggest it's a good idea. So, I'm hopeful that somebody's going to do it is, where we see employers able to connect their claims from their health benefits with other kinds of health care that they invest in, but they tend to think of as separate. So, like worker’s compensation or disability, short-term or long-term disability benefits they're all connected to the health of the same people. But they often see them as totally separate enterprises and in fact they're connected and the company's paying for both.

So, the more we can see longer-term and more integrated assessment of the overall spending around individual patients, and how individual people are impacted by a whole variety of things that happen to them in the healthcare system, that's when we're going to start to see more nuance in purchasing behavior. So, an example would be, we've had employers start to try to really understand how errors and accidents, infections in hospitals are affecting their own employee population. These things don't appear on standard claims, typically sometimes they do but not typically.

Typically, if there's let's say and medication error made, there's no particular bill, there's no line on your claim that says you know you paid for this error. It's kind of buried inside the claim, if it's even noted in the claim and it's hard for employers to detect it, and yet these are very common. All the literature on errors and accidents is that they are extremely common, that as many as one in four patients admitted to a hospital or experiencing some kind of harm. So, it's very common and employers are paying for that, so they really do want to understand where it's happening and most often and in so doing be able to try and prevent it.

And there are ways to use AI as well in exploring claims and to look for things like excess length of stays, that don't match a diagnosis or things like that help them to be able to at least trigger a closer review of a claim and to begin to observe patterns that are troublesome. So, I think that what we're seeing with for technology at least from the employer perspective is an ability to be much more nuanced and much more sophisticated in really looking at the experience of their employees. And then using that in more effective ways to help their employees get the right kind of care.

Harry Glorikian: Jumping back to Leapfrog. So, what will be happening at leapfrog in the next couple of years where is the, where are you taking the organization and what would you like to see the organism and develop and/or produced to help this, in this long goal?

Leah Binder: Our goal is to save people's lives and on a fundamental level, so that you are protected when you go to a hospital or any kind of health system. But that your well-being is a primary consideration which will protect your life. It is a, you know five hundred people a day, upwards of 500 people a day die preventable errors in hospital. So, it is a major issue for people to be protected from that. So, we want to change the market, so that that's not happening anymore and so that people can better protect themselves by making the right choices.

And so we continue to focus on patient safety and using all of the technology that is available to us and to our members, our purchasers to try and do that. Whether it's find the errors and publicly report them, which is what we do at leapfrog for employers as a group nationally, or find them in your own claims for one in particular purchaser which we simply, we advocate that they do and we help connect them with the resources to do it.

And then what we're focusing on right now is hospitals, but we are also in 2019 moving toward ambulatory surgery centers, as well as outpatient surgery. 60% of all surgeries are now done on outpatient basis or in ambulatory surgery centers. So, we're going to be looking at safety and quality there as well. And in addition to what we do in reporting this data ourselves, we also advocate with CMS to make sure they report it, and we've been strong advocates since our inception and in many respects why CMS currently reports so much data is, a lot of the work of people at leapfrog and are continuing very strong efforts to make sure, not only that we can get the information but also that it's made publicly available to everyone.

So, we continue to be needed believe me to get that information available to people, and to get it used to make it easy for purchasers to use it and in sophisticated ways and to get, to drive that market for better care.

Harry Glorikian: Are there any, I guess stories that you could share where this information really made a difference with either an individual or a group, whether it's the cost impact or anything of that nature that you could share? You know just going back to Moneyball medicine, which is all about you know how data is changing practice of medicine or how patients look at their care and how they manage themselves and how that affects. Obviously what we all look at is is price or cost or you know combination of those two things.

Leah Binder: Right and we definitely have had a number of successes that we do think are important, and that you mentioned price and cost and I just want to make a little comment about that, they're different. The cost of care to the purchaser is one thing, the cost to the provider is another thing. Those are two different things, but for purchasers they're very interested in price transparency. They want to know how much each provider is charging their employees and then that's the price and then they want people to be able to compare among prices.

That's really important, but it's not the only thing and the example that I give around that is that, you can know the price that a particular, say Hospital is charging for childbirth. Let's say for a normal vaginal delivery and for a C-section etc., you could find out the pricing. But what you also want to know is what is the rate of C-section, because that varies tremendously. We see variations in our data you know some hospitals will have upwards of 40 or more percent of all births via C-section, others will have you know below 20 percent C-section.

So, a C-section is roughly twice the cost to an employer and to consumer, it's twice the cost of a vaginal delivery. So, if you're going to a hospital that has a much higher propensity for C-section births you're going to pay more and that's not a price issue they may charge a slightly lower price for their C-sections that is a cost issue and that's a quality issue. So, quality and cost and price are all integrated and it's not enough just to pull out one. You have to look at all of them together and so our examples of what we've seen with leapfrog have to do with that integration.

An example would be, there's a hospital, we publicly report as I just mentioned C-section rates by hospital where the only source that information we ask hospitals through the Leapfrog Hospital survey to voluntarily report to us on that. It is a standardized rate, so it's adjusted for all of the factors that can go into differences among hospitals in their C-section rates. We try to adjust for those things and it's a rate, that's used by Joint Commission for example which is accrediting body for most hospitals and other, it's endorsed by the NQF it. So, it's a good measure of C-sections that you can use to compare among hospitals, and again we do find major variation.

So, one Hospital which we wrote up in a case study which is on our website leapfroggroupe.org and available anyone if you want to take a look at it is, they recognized through doing a leapfrog survey that their rate was higher than others and it did not meet the Leapfrog standard. And they as a result launched a campaign and they lowered that rate, significantly another meeting to standard. Simple example maybe, but that is saving a significant amount of dollars to the people, the women who are using that hospital as well as their employers who are paying for much of their care.

So, that is an example when we've seen reductions, and we've seen improvements in maternity care for everything that we've been reporting. And in some cases dramatic, we were reporting on early elective deliveries. These are deliveries, they're done without a medical reason early it too early in the pregnancy of 37 to 39 weeks as opposed to 40 weeks, which is when mother nature typically decides time to give birth. And so they're scheduled anyway and to try and actually get a jump on mother nature, so that I guess you can get the right doctor or there's various reasons it's just more convenient to schedule it.

But it's not safe, it's not a good thing to do. It's not safe for the baby, it's not safe for the mother and often results in a NICU stay which are very expensive as well as just not safe and not healthy. So, those went from a rate when we first started reporting them publicly, again we are the only source of that information. Back in 2010, we were reporting a rate of about 17% and now the rates down to about 2% nationally. So, that's a massive decline a major change in the delivery of maternity care and it has definitely saved, probably hundreds of thousands of babies from a stay in the NICU and saved a lot of costs as well.

So, in maternity care we can definitely see the impact of the transparency movement. And we are not doing the work by the way, we're not a critic for the enormous amount of work it takes to reduce you know your rate of early elective deliveries or your rate of C-sections. That's some pretty substantial leadership and hard work by providers, but transparency and markets work and that's what we see when we start publicly reporting on a measure like that.

Harry Glorikian: Yeah, know I mean, we all know that you know transparency changes a lot of markets. It's when things are not transparent and opaque that strange things happen, either people comparing themselves to others because they have no idea what the other person is doing or just the patient being informed. And you know I always thought to myself you know once this information is available, and you can make some pretty interesting apps and analytics to identify different things either to the providers themselves or to the patients.

Leah Binder: Right and the providers when see their own performance in comparison with others, it does help them to understand what they can do better. And and it usually motivates and galvanizes changing, that's a key aspect of everything.

Harry Glorikian: So, is there anything that I haven't asked you that, you would love for the listeners of today to hear about, either changes in the marketplace technology or you know things that leapfrog is working on itself?

Leah Binder: Well, one of the areas of technology that we put a lot of emphasis on is the safety of technology used by hospitals, and specifically how safe it is, how well it protects patients from common errors. So, an example of what we have classically looked at is computerized physician order entry or provider order entry, depending on who you talk to, but it's CPOE, computerized order entry. It's used for medications and the prescriber enters an order in to the system the CPOE system.

They enter the medication order in for a specific patient, it connects to the patient record and if that order would cause an allergy problem with the patient or it's a drug interaction with something else that the patient is taking, then the CPOE system fires an alert at the physician. Typically, it says you know this the patient's allergic, do you want to change the order etc. And that has really reduced errors in the hospitals the most common error made in hospitals by far our medication errors. And so the CPOE systems have had an impact on that.

So, what leapfrog has done is, we actually give a test to hospitals. They can, it's a web-based time test where they can assess whether their system is alerting the way it should and not alerting too much. You want to avoid frivolous alerts so that physicians start ignoring all the alerts. So, it's actually kind of a balancing act, but we look for systems note that alert when there's a really terrible medication error that's being made. So, if doctor enters or prescriber enters something that would definitely cause the patient significant harm or even kill the patient, we want to make sure that system alerts to them and we test for that.

So, we've found that about a third of the orders we've tested do not alert properly, and so there's definitely work that needs to be done. So, what I think is the take home message that we've learned from this work with CPOE and I think a lot of hospitals have shared with us is that, technology in hospitals is not plug-and-play. You don't just buy it off the shelf and plug it in now they all sort of know that. But in theory but in reality technology is something you have to monitor constantly.

You have to be vigilant about it, you have to make sure it's constantly working to the benefit of the patient, and you can't assume that technology replaces all of the other kinds of efforts you make to keep your patients safe. It augments what you do to keep your patients safe, but it doesn't replace it. And I say that too because I think when CPOE especially when it first came around, a lot of hospitals thought well. We've got this technology now so we can skip a step, we cannot have the nurse check the order at the bedside or something like that. They would skip a step and that's not safe either, we have found as I said a lot of orders are not alerted properly so that step shouldn't be skipped.

And also it actually just doesn't protect the patient enough, but when it's combined with the systems that are already in place and checks and balances around order entry or any other kind of safety issue, you do find that technology can vastly improve the safety for patients. So, we've looked at that, we've looked at barcode medication administration and we're very interested in continuing to monitor. Not just whether hospitals have good technology in place but whether they monitor it and they use it most you know as effectively as possible. And both of those things have to be combined for technology to be effective.

Harry Glorikian: Well, I want to thank you for your time today. This was wonderful and it's great you know continuing our conversation over time. I'm sure they've all talked many times in the future on many different things and I can only wish you guys extreme success, because I'm also getting a little bit older. So, you want the system to work as well as it can.

Leah Binder: Right, we all have a role to play and making sure that happens, and I really do appreciate your book. So, thank you for writing it and for making it available. It's been a great resource.

Harry Glorikian: Thank you very much for your time, really appreciate it.

Leah Binder: Thank you.

Harry Glorikian: And that's it for this episode hope you enjoyed the insights and discussion. For more information, please feel free to go to www.glorycamp.com. Hope you join us next time, until then farewell.

Harry's guest John Glaser, senior vice president of Population Health at Cerner, speculates on how business models in healthcare are changing and how artificial intelligence and EMR systems will work together in the future.

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Transcript

Harry Glorikian: Welcome to the Money ball medicine podcast. I'm your host Harry Glorikian, this series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious, value-based healthcare economy. We look at the challenges and opportunities they're facing and their predictions for the years to come.

My guest today is John Glaser, who is the senior vice president of Population Health at Cerner. Cerner is a health IT company that is one of the largest suppliers of electronic health record systems in the United States. John joined Cerner in 2015 as part of the Siemens health services acquisition, where he was the chief executive officer. Prior to Siemens, John was vice president and chief information officer at Partners HealthCare. He also previously served as vice president of information systems at Brigham and Women's Hospital.

John received his PhD from the University of Minnesota, he has written over 200 articles and three books on the strategic application of IT and health care. Including the most widely used textbook on the topic, “Healthcare information systems a practical approach for health care”. John is on the faculty of the Wharton School at the University of Pennsylvania, the medical university of Southern Carolina, the School of biomedical informatics at the Texas Health Science Center and the Harvard School of Public Health.

John focuses on strategic relationships with Cerner clients and advancing Cerner's population health solutions and services. John, welcome to Moneyball medicine, it's great to have you here.

John Glaser: Harry, it's a pleasure.

Harry Glorikian: John, tell me what does it mean to be vice president of Population Health. What is Population Health?

John Glaser: Well, it's a fuzzy term in some ways but basically the idea is that there are organizations. They'd say I'm accountable for the health care and the health of a group of people, it might be an employer who says I'm responsible for my employees or the state Department or a health care provider, who has a series of lives attributed to the - health plan, but the point is they're accountable. And so they need a series of tools and technologies that help them manage health and manage health care this is analytics to see you know who's receiving what care, how costly is it.

This is a series of care management to the degree they need someone to help them navigate the care process or social determinants. So anyway, at the end of the day accountable organizations need technology to help them fulfill their obligations to those who they are to serve, and that's what population health IT staff intends to do.

Harry Glorikian: You know medicine has been historically based on a fee-for-service model, where you're paid on what you do. And now that we've seen sort of a shift not as much as I'd like to see, but a shift towards value-based medicine, in other words paying providers based on outcomes. Do you see what you're doing or and/or the business model sort of shifting towards how we do, what we do?

John Glaser: Yeah, I think Harry we're in the early stages of an extraordinary change in the business model of delivering health care, and it runs along a couple of different dimensions so to speak. One is we're moving from reactive sick care to proactive management of health, so you know you show up. We'll fix you so we got to make sure that you remain healthy. So, that's one dimension, the second dimension is fragmented, where you go here for this type of care there for that type of character this integrated continuum of care that occurs across.

So, we manage you throughout all the steps that need to be taken to you know replace your hip for example. The third is moving from volume or rewarded for volume to where you're rewarded for quality and efficiency. And then last but not least is a shift from where we're centered on the clinician to where we're centered on the patient's. So, these four dimensions represent an extraordinary shift in business model. I think frankly you could argue that the business model shift that healthcare is undergoing, now is the most profound business model shift of any industry in the last 100 years.

Where you look across transportation, telecommunications and all kind of financial services etc. Now business model shifts are hard and they take time to play through. So, I suspect that we will be spending decades frankly to make this particular shift occur and to occur well. So, population health and other technologies are being brought to the table as organizations prepare for this future that awaits all of them.

Harry Glorikian: Well, I think it's interesting right, I mean every once in a while it sorts of strikes me. I think it's because I've been in the business for such a long period of time is, every other industry has been digitizing or measuring for forever, it seems like. And it seems like for us, it really hasn't been that long. I mean if you think before the Reinvestment and Recovery Act and you walked into a doctor's office, the entire wall would be paper. And so in, I think digitization has only been eight years maybe on a grand scale.

John Glaser: Yeah, I think that's fair Harry, we're less we're not as far along as other industries, now you have to be a little careful because the degree to which digitization will occur or to which it will be impactful varies. So, organized religion has not been digitized and unlikely to be to any material degree, similarly the legal profession has to degree. But at the end of the day when you have a sort of cohort of people who are experts and whose knowledge is really the asset here. It's hard to digitize it, you can digitize a lot of stuff surrounding them but to digitize a smart financial planner or the smart lawyer or smart doctors, just challenging.

So, but that being said we are late, you know I think in a way sometimes health care gets accused of being behind. You know as if it's full of a bunch of button heads who didn't know any better while their colleagues, and retail or leapfrogging them. So, well you know there are button heads in health care, but by and large they're pretty darn smart. They are thinking, they have made perfectly rational economic decisions based on the business model at the time. So, if I'm rewarded for volume I don't need all this other IT stuff, I just need to keep the beds filled in the clinic schedule fill.

So, I'm investing like a perfectly rational person would do. Now we'll see that shift as that occurs but nonetheless there's been decades of non-investment, because the economics didn't warn it.

Harry Glorikian: That begs the question of, what have been the challenges that the healthcare industry is faced with respect to this digitization of data?

John Glaser: Well, I think there's a number of challenges Harry, one is the range and complexity of the data is just off the charts. So, if you say well how would I describe a person in their phenotype and all the different types of data that were brought here. It's a much more complex record than your financial record, and now we're going to add to it by saying, golly we really understand the social determinants that influence you and we really need to understand how to motivate you, and we really need to understand your genome, you know. So, we have this incredibly complex set of data that comes to be.

The second thing that is a knowledge base, it probably has few knowledge bases which are challenging to master. One of the ways you can see that is if you go back 40 years ago and see how many specialties were recognized by the American Board of Medical Specialties roughly a dozen. You say well how many are recognized, today roughly a hundred. Well, why does that happen because the knowledge base expands and becomes so challenging, you get to increasingly narrow what we expect a human being to master.

The third is you have these very complex processes that occur. This is not manufacturing the hospital people show up they have complications they go south all this kind of stuff; you have to be able to sort of manage the workflow on an ad-hoc basis. So, I think it is, this complicated data world complicated, knowledge world complicated work process role and then last but not least, so now when I was in graduate school Harry I was an organization theorist. And one of things you notice is that for sociologists hospitals of the most studied organizational form of all time, because sociologists can't figure out how they work.

They're too complicated, parallel power structures doctors and administration, lots of committees etc. Anyway the whole thing is one of the reasons, I think healthcare is behind in IT partly because the economics didn't warrant it, but partly you say it's arguably the most complex arena to apply IT that we've ever had. And so that's hard, she was very challenging to get apps at work effectively in this complexity.

Harry Glorikian: Interesting, it's funny because I think to myself sometimes it's a product of the way the system paid itself that caused some of these shifts to happen, where in other sciences we come up with a way of organizing the information about, what we need to work with. Because we're looking for a certain outcome, that we're trying to measure, and where you're being sort of remunerated based on what you do, not what the outcome is. That rubric of organizing sort of becomes looser -.

John Glaser: Yeah.

Harry Glorikian: In a sense. So, helping this along I mean I you know we hear so much about artificial intelligence, the analytics machine learning. I mean the definitions that worlds are keep expanding it seems like it. Where do you see whichever term the AI, the machine learning, the analytics and the electronic health record system sort of intersecting and what does that look like? Do you have some examples?

John Glaser: Yeah, here I think I mean it's something back a little, but one of things I find interesting about IT is about every decade a class of technology arrives it changes the world. So, you go to the 60s is the mainframe, 70s is the minicomputer, the 80s is a networked personal computer, the 90s was the web. Year 2000 was the mobile device, you know the iPhone debuted in 2007, see well what is it this decade. I say well frankly, I think it's this broad umbrella called AI, that will change the world just as the others have changed the world. And just like the others to have time to change the world, so all this.

Now it's, this broad term in a way you get all hung up about what AI really means and all that stuff. But I think frankly  listen it's the whole field of advanced analytics applied. Now in a way they're sort of the, we see sort of four broad arenas in which you would apply this one, is determination of structure. So, you say the machine is reading an image and saying, we got a you know this is what's going on here. You know whether it's an eye disease with your eyes or a tumor or whatever or the machine is looking at a pattern arising, listen this drugs these drugs are hurting people. You can see this in the pattern you know or treatment A is better than treatment B.

So, there's a creation of structure artists but this mess, frankly that we have so that's one category. The second is increasing contextual awareness. So it's okay, John Glass was taking care of Mrs. Smith, the Machine says I know what's going on with Mrs. Smith, I got a record, I know they're presenting conditions, I know what preferences. I know John's preferences etc. I know what the evidence says I know what the payment is. And so I'm going to present data to John in that context. Here's what you should pay attention to, here's likely your next series of actions etc., I'm going to shape myself to this particular interaction. Sometimes we see this on retail sites with a sort of attempt to shape.

The third is I'm going to do operational flow; an example of  this remember my days at Siemens with the smart city. So, the city Siemens we got a traffic jam on the Main Street, so I'm going to alter the light sequence to sort of move it along a little bit here. And so in a process sense you say the Machine says I got to take Mr. Smith down to get his radiology exam, I can tell that there's a 30-minute wait in radiology. I can see he needs his blood drawn, the phlebotomist is one floor below, I'm gonna let the phlebotomist play through, draws blood and then I'm gonna send it on a writ, it's sort of choreographing a process.

And then last but not least, it is the sort of clinical decision aids. You know it's the readmission algorithm, it is the thing that says you know this person is likely to be better off in this type of skill facility versus that's, a lot of the predictive stuff you know comes in there. So, you look at all that and say wow there's some real power here, and a lot of that role leverage EHR data and particularly golly, if we can bring it all together and if we can deal with some of the, you know the mess that is in there and if we get better at social context etc. So, I think we're learning Harry and it's an exciting time and there's you know the gazillion start-ups, there's some big gorillas.

You know the Google’s of the world and Amazon's of the world, Microsoft's etc., we're all playing in this thing. So, anyway we're in the early stages of this decade of this very profound change, which will in a way preserve the EHR as the core. I mean you still got to collect the data Docs and nurses stuff to work with something. I mean they're interacting with something that goes on, but the nature of that interaction will be quite different in the years ahead.

Harry Glorikian: Do you see the EHR, do you see this integrating with the EHR? Do you see the EHR becoming the data like that something reaches into and then does an analysis for a specialization? How do you see this melding?

John Glaser: All the above, I think in some cases the intelligence will be part and parcel of the EHR, because of nothing else speed you know. So, when you enter an order and the thing comes back what are you serious, there's got to be a better way to do this. You know that will be part and parcel in that by this. On the other hand, which you see for example in Population Health is to extract data from lots of different EHRs. Because - regions have the plus you bring the claims and the devices and all those other stuff. And what I think that increasingly the population health will be is and I got to keep Harry healthy.

So, and I need to pull together enough that can characterize him, you know clinically characterized them, socially characterized and how do I motivate in etc. Now I haven't characterized Harry what's the plan to keep Harry healthy so I got to come up with a plan and then I got to monitor the plan -. You know all sudden he stopped taking his drugs or all of a sudden his blood Sugar's wobbly etc. So, the plan has to alter itself so I think we need to do some that's all intelligence. You know the intelligence of rationalizing the date of the intelligence I'm going to infer a plan and the intelligence is enough to monitor.

So, there'll be this layer that sits on top of EHRs and I think frankly, you know is people begin to say I want to bring my data into my mobile device and integrate it there. There will be intelligence applied there, you know there's quite local or a cloud-based, but specific to the device guiding you or me and decisions we might make.

Harry Glorikian: What have you seen from either the start-up companies or are new things that are going on, but what do you see that's really exciting like what sort of application area do you see where it's improved an outcome for a patient and lowered the cost, so in that sense?

John Glaser: You know we see, I mean there's lots of spot examples and what I think that's kind of interesting about this whole arena is, at times the we talk about the sort of general purpose intelligence. That's kind of a Watson thing or how you know in the 2001 Space Odyssey, but in fact the real powerful stuff is really quite targeted. You know it's the intelligence and a Siemens MRI, this is part 62 is feeling you know get over here and fix it before it really fails. Which is different from a part that says in your glucometer your blood sugars are bounced around through something, which is different from, If we don't do something, now you're gonna be readmitted with it for the, anyway very targeted intelligence here. So, you see lots of neat examples, you see neat examples of sepsis algorithms that say, we've got to do something now before this because you can go south in a hurry. We see neat examples on readmissions where they really do drop readmission rates. We can see examples where we say rather than send this person to this skilled level facility upon discharge send them to a lower or higher. And we see actually a third of the time decisions get changed you know be to the right place to go up and do this though.

So, and we see examples of more effective ordering, we see examples where you can do post Marcus, you can really pick up signals in the data. This is a drugs hurting people or treatment is better than for treating to be. So, you go through this range of things that are, wow it's pretty darn nice alright add off all those stuff. I think Harry one of these sort of you know, there's some broad big challenges that are out there. I'll give you an example of a broad big one here. So, when you look at an EHR see, how many instances of medical knowledge are there and of typical EHR say, well what's an instance of medical knowledge or an order set you know a health maintenance reminder.

You know all are sort of instances of medical knowledge, in general there's in excess of a hundred thousand. So, wow you know how, who's maintaining this. I don't know and what day was a recent update, I don’t know about either. So, one of the challenges we have is we introduced all this intelligence to these systems, and it just grows this sort of body of knowledge. It becomes brittle you know, because nobody's watching the store so to speak there. So, you say wait a minute how about if we have the machine watch and the Machine point out that this thing is updated and then actually look at the data machine learning and make the updates itself etc.

So, you know, no way you have machine healing and management of its content. I said wow that's pretty neat frankly, we'll have to do something if this stuff is going to get brittle break and hurt somebody. But anyway how we figure that out beats me, and I think that's becomes one of those great challenges that confronts industry in addition to continue to find and leverage. Lots of very specific point examples of where the intelligence has really made a, to much more gain although quite focused game.

Harry Glorikian: So, that begs the question of, I mean there's got to be either new capabilities people need to learn or new people we need to hire, that are going to get involved. But this, it seems like healthcare is going to be a booming area for jobs and new types of jobs that, or new skills that they're gonna have to teach doctors in medical school just to be keep up with all of this. So, what do you see as the opportunities?

John Glaser: Well, I think there's lots of opportunities and those young people aren't even mid-career people looking to shift here healthcare and healthcare IT and informatics is data science all this stuff is gonna be a rich and fertile area for quite some time. So, I think Harry, they range from what I call the methods of guys, you know men and women who really understand how to apply different analytical techniques to make this stuff, and really quite you know a lot of you know machine learning techniques and other types. So, there's the methods people that got it one here.

The second is a series of people who actually understand the clinical context, because sometimes the massive people come up in the person of clinical context, says no I mean I'll give you an example this goes way back when. We were looking at data on how you do have the Machine determined smoking status you know. So, can the machine go through and say, Harry's a smoker or non smoker and it gets complicated maybe stop five years ago or whatever what happened to be. And we were looking at one particular note and it said smoking status unclear, and so what does that mean.

And the physician who is working with us miss said, that means the resident is tired and just didn't want to go down this rabbit hole, and just wrote smoking status and clear. Well, you got to know the context to know that are these kinds of things. So, we'll always have to have people who understand the move of where that's going on. The third is understand, people who understand workflow so where and how do I introduce this you know. Do I do this we're in the middle of the exam, do I do it after the fact, I do it to the doctor, do I do it to some staff in there. I mean where do I fit this and if I fit this what do I want them to do.

So, you might have logic that says the social determinants indicate this person is in a nutritional wasteland. We, got to deal with it okay, who does what when that is informed. So, there's a series of what is the process and their choreography that goes with it. And then last but not least it is people who design stuff. You know my wife recently bought a Volvo xc90, you know. I know Harry if you said which has more lines of code, a high-end SUV a 787 or the space station. The answer is a high-end SUV by factor two, sheer lines of code you know to park correctly avoid crashing somebody to dim one other light. You know it's amazing here on these kinds you couldn't crash that thing if you wanted to.

So, the point is how do I help my wife or anybody with that bring the knowledge in and sort of you know what to do, a guide the interaction that is going on and these kinds of things. So, I think there's between the methods people, the context people, the workflow and the computer human design people. There's lots of opportunities to do this, and then the last one at least obviously people who evaluate, say is it doing any good.

Harry Glorikian: So, that begs a question of I'm looking at all the other industries is, when they've tried to apply these advanced machine learning applications AI etc. And their first go, they tried to obviously do what we always do, take it and melt it into my existing workflow. And they never seem to get the return that they were expecting. And then when you see them shift their workflow based on the power that the system provides, they seem to get much more. So, how's that gonna work in healthcare? Because we're pretty rigid in our workflows. So, how do, do you see that influencing the workflow and what we learn?

John Glaser: And I think you know we will iterate, because we know a lot of smart people in this industry, and they'll say, I don't really know, but let's try it here and they say well you know we're off 15 degrees and so you'll have to iterate. You know I think one of the ways that organizations of all this what I always root for short cycle learning try this too, try that too, they just sort of short cycle their way into you know better to do this. In some ways we do have rigid workflows but on the other hand, I think I find over my forever time in this industry is that, if you help a caregiver save time or do a better job of delivering care, they'll adapt quite readily. Where they're not happy is if you cost some time you know or they set notice.

So, you know subject to regulation and reimbursement because there's certain things you've got to do here. So, I think what will iterate and you know define novel ways of doing this kind of stuff, and frankly one of the great things that you know the is learning from your colleagues, you know about what did you try in your organizations. The chief medical officer is talking to chief medical officers and vendors learning from their clientele etc. We'll get better at all this.

Harry Glorikian: There's a lot of new entrants into the field, right. You've got the Google’s and you've got the Apple’s and there's and the list is incredibly long of wellness company sort of budding, right up against the line there of regulated versus non regulated care. Where do you see or Amazon's for ran into this? Where do you see their impact of the system and I don't want to say good or bad, but how do you see that changing things?

John Glaser: I think that's unclear to me is how that will evolve, and you see multiple threats. You know you see a thread of you know health plans and providers fusing and merging, you know you see the Walmart’s making moves. You see the CVS is making moves, all you know a pharmacy and retail and health organizations, they're all making moves in ways that are quite striking. And you see them trying to take out the middleman of the PBM by mu, you know worry.

So, this is restructuring that is occurring and it's not just in the sort of non-provider’s side of this thing. You know a couple years ago at CERN or you know maybe decade ago, we would have said well what's a large Health System we said well, about five billion in revenue annual revenue. And so today what would you say, about twenty billion in revenue. So, there did the bigger getting bigger in lots of ways here. So, even the provider side of restructuring these very large health systems with a whole lot of regional systems that are going on. So, you have all that and that's on the non IT side. Let alone the tech giants you know coming in, let alone the consumer guys coming in some of which are tech giants.

Let alone the traditional EHR vendors Cerner being one epic being and other all scripts etc. Let alone to your point a gazillion starts of a remarkable talent, some in the consumer side, some on the analytic side you know all over the place. You see well how will it all send a lot, good question how will that all settle out. So, I don't know that we know in a way, what that will look, I think some things are clear. You know one of which is you could who poo poo the tech giant say, well they tried that before and it turned poorly for them, it'll turn poorly for them again. Don't count on it, the world is a different place, the technology is better, there's got smarter etc.

The other is if you said the traditional boundaries, you know providers on one side, health plans on another, reach pharmacies that are those boundaries are blurring fast. And so you see that and providers getting into medic right or other stuff. So, I think a lot of it if I were a health system sir what do I do about all this stuff, well I'd start forming relationships with a lot of people who may have been traditional antagonists in the past, you know the health one, the retail guys. And it's they're learning just as you're learning and starting to go through all of that kind of stuff here.

And I think you know you it becomes harder you turn to a core vendor, you say well jeez you know certain or at they're going what do you think of all these guys you know help me navigate this technology stuff or consulting firms, you can go off and do all this. All right very complicated, very confusing time, and I think the other sometimes you know you know Harry  healthcare straddling two business models is, a fee for service and the value-based care, you know what a pain in the butt, it is a pain in the butt, how long will it last decades. It's not one of these two years and it's over and done, so settle in for a multi-year period of forming. That will go on across the board.

Harry Glorikian: That, I'm sure that doesn't make physicians or anybody listening to that -, but so I you know as I look forward in the next, I hate even saying like you know three, five, ten years there's a lot of digital disruption coming. Yeah, I try to stay up on all the bleeding edge which moves in to tech very quickly. Whether it's AI, dare I say block chain, virtual and augmented reality things like that. Do you see, what do you see having impact on the healthcare arena?

John Glaser: You know, I think it's hard to do that I mean and at the end of the day for me, you know what I think is you step back and say for any particular class of technologies at a very fundamental level, tell me what it does. And then I will tell you whether, I think that's important. So, I'll give you an example, what is flight do. So, as well enables you to get from point A to point B a lot shorter period of time without the infrastructure, you don't need railroads you need a landing strip at either end, well that's pretty remarkable. I could see where that's valuable to me. What does refrigeration do? It allows perishable things to last longer, they say well jeez would I do that. Well, I'm you know I'm moving in pharmaceuticals across the globe that matters a lot. So, you step back at a very fundamental level and say, why is this, what is it and why is it profound. So, I for example look at block chain and you say what is it it's a new way of doing accounting that has the ability at perhaps to remove the middleman, like the bank or the Law Offices Center. Do I think that will fundamentally alter health care? No, I don't do, I think we'll see it sure you know we'll see it as people do credentials for doctors etc.

So, now on the other hand you say what about this intelligence, say well  really could help a series of decisions contextual where structure of data is that a big deal. Absolutely, it's a big deal. Do I see the fact that you and I might have technology on us? You know our mobile device that can communicate with us knows, where we are etc. Is that going to be a big deal? Absolutely a big deal. So, I think it's hard but the trick is to step back and say at a very fundamental level what does it do. And the answer, you'll get an answer and the salience of that answer will vary by industry. Block chain may be more important for financial services than it is for healthcare, than it is for you know other religions there.

But I, but even when you do that, they say ok. So, I think consumer stuff is really important but there's a zillion companies, how do I sort through we through chat. That's still an issue you know even if you believe the area's really critical.

Harry Glorikian: So, what other topics or subject areas, I don't want to say keep you up at neither, but are very salient to what's happening in this whole digitization or movement of healthcare in this direction.

John Glaser: Well, I think it's be careful here, because I do believe it's a profound business model change, and I do believe it takes time for those occur. You know if you look for example here last year, what percent of retail in the US was done over the web versus in a store. And the answer is 12%, you say wow you know how long we've been at this. Well you know Amazon incorporated in 1994, Google in 1998. So, 20 years later it's 12%, and so that's not fair because gasoline isn't sold over the Internet.

So, okay well it factored out some of that stuff and you say, but still and some industries has been devastating you know consumer electronics, but other industries it hasn't groceries are still largely untouched by the internet, you know jewellery large the untouched etc. So, it takes long periods of time and differential impact. It's not a universal impact that comes across the border. So, I think we're gonna big business model change is going to take time. We have some extraordinary technology coming, so what is critical. When there's no inherent reason to believe it's all going to work out well. You know you sometimes what golly is gonna try, that's not a given here at all.

You know there's and you can see it in the EHR burnout issue of the doc, we could very well drive a whole bunch of people out of the business at a time one certainly I, and beginning to need them as I decay slowly but surely in the years ahead here. So, we could break it in some ways are ever, so what matters a lot is that the industry with all the competitive juices that flow around here is that, it learns from each other and guides us. Yes, there's businesses here but there's also you know a moral and a civic responsibility, we collectively have that it turns out well, as we go through this.

So, the thing that I get really pretty well at night but the thing I worry about is that we don't learn and learn together to make this thing as effective and efficient and as highly tuned as possible, and for sakes we don't break it along the way.

Harry Glorikian: So, where do you see, I mean I always think to myself some of the big shifts have happened because of the way that government has influenced those shifts. In other words, if we kept paying everybody based on everything they did, they'd be perfectly happy. But you know we came up with this thing called the Affordable Care Act. We said, well you know maybe we should pay based on outcomes. How much do you believe the government is playing a role in this shift versus competitive dynamics which I don't believe necessarily exists in healthcare?

John Glaser: Well, I think the government's, the single most potent actor in all of this. You know it accounts for half the payment you know between Medicare and Medicaid, it accounts. So, how it decides to pay is enormous consequent. It is also because it is government has the ability to absorb being the first mover disadvantage. You know the free rider effect and so government can make those moves bear the free rider effect. Because it's government, whereas any individual player first of all isn't big enough to do that, but you know it suffers that consequence.

So, it is the big gorilla and it can deal with the first-mover dissident. Now it has challenges in that, it is a political animal. It is surrounded by Congress, it is surrounded by elected officials who come and go. So, you know it's gonna get buffeted by those particular wins and all the stuff that makes politics complicated, you know that we go through. And it's got a big task who's trying to figure out how in the world you take a country or 330 million, people are very diverse and sort of satisfied them all. And I remember it spending time at ONC and I thought golly meaningful use. You know what does meaningful use me you get 3,000 ideas and you can only take 12.

So, this is a they have a tough job to go off and to do this whatever they do the industry will follow, the payers will follow, the plans will follow etc. So, that's on the top of the regulatory though for example the FDA see we’re gonna loosen up you know the process by which you get new stuff approved. It has an enormous influence on whether that goes on or not or you know advances occur within biomedical discovery. Last but not least for example, on HIPAA where it's kind of striking to me as hip as 20 years old. It covers provider’s health plans and intermediaries but it doesn't cover Amazon and it doesn't cover Apple.

So, if government has decisions to make about the privacy context, you know what it does or doesn't do. Anyway I think it is the most significant actor that exists in the landscape today and as it moves, so well the industry.

Harry Glorikian: Any closing thoughts or anything that you think, you know the listeners would want to hear about in these in these shifts, before we sign off here.

John Glaser: No, I think first of all it's been a pleasure. I appreciate the opportunity spend a little time with you Harry and also with those who are listening in to this stuff. I think for all of you the, we are being, you've probably gathered from my comments and perhaps comments rather. It's a remarkable time to get through it we'll take our collective intelligence hard work and thoughtfulness. And so I look forward to working with everybody, who's listening to this stuff to let's go make this thing happen.

Because at the end of the day the consequences are real, I mean I think about this every now and then area, you know you if you go to pick a hospital that's near us. There are people, the people who are in there are some, there's somebody's spouse, there's somebody's parents, there's somebody's siblings. So, this is real people who are loved by others going through a bad time unless they're giving birth to a kid, which is usually a pretty good time.

And so it's very real it's very personal little level and we ought to recognize the magnitude of that and the importance of that as we collectively work our Fannie's off to make this thing as good as we can be, and learn as we go through this. Anyway I feel like a sermon, but nonetheless go forth and make this world a better place. We all need it and look forward to it.

Harry Glorikian: Thank you very much for the time John.

John Glaser: Thank You Harry.

Harry Glorikian: And that's it for this episode. Hope you enjoyed the insights and discussion. For more information, please feel free to go to www.glorycamp.com. Hope you join us next time, until then farewell.

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Harry's guest is Dekel Gelbman, founding CEO of FDNA. The company uses a combination of computer vision, deep learning, and other artificial intelligence techniques to improve and accelerate diagnostics and therapeutics for children with rare diseases.

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Transcript

Harry Glorikian: Welcome to the Moneyball medicine podcast…

I'm your host Harry Glorikian. This series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious value-based healthcare economy. We look at the challenges and opportunities they're facing and their predictions for the years to come.

My guest for today is Dekel Gelbman, who is the founding CEO of FDNA. He leads the corporate and business strategy of an innovative digital health company that develops technologies and SAS platforms used by thousands of clinician’s researchers and lab sites locally in the clinical genomic space. The main mission of the company is to give hope to children with rare diseases and their families.

FDNA which was founded in 2011, uses a combination of computer vision, deep learning, and artificial intelligence to analyze patient symptoms, physical features and genomic data in combination with a network of thousands of genetics professionals worldwide. Then they drive scientific insights to improve and accelerate diagnostics and therapeutics impacting the lives of children with rare diseases.

Harry Glorikian: Dekel, welcome to the show, good to have you.

Dekel Gelbman: Thank you very much, it's a pleasure being here.

Harry Glorikian: Dekel, we've known each other almost since the day you showed up here in Boston deciding whether you would place yourselves here as a company. Tell me how this whole thing got started, because it's not exactly what you would consider a normal route into the world of diagnostics or using AI and machine learning, and it was quite a while back. I mean it will you were guys were at the forefront of this before I think a lot of other people got involved.

Dekel Gelbman: Absolutely you know, when we started we knew almost nothing about healthcare. We were techies, the background of this company was actually two founders that were very successful in developing facial recognition software that was sold to Facebook in early 2010s. And the drive, I think for this company was how do we make an impact, real social impact with this technology or with our know-how around facial recognition. And so by exploring a lot of fields, Healthcare was really very compelling because of the impact that you can, you can make and we started to meet with various specialists and different practices in health care. And then almost by accident, we stumbled across genetics and we were amazed to learn that back then and for decades’ geneticists would look at faces of patients and make a lot of the diagnostic choices based on facial patterns that they could identify. And it was just a lightbulb moment right, then there we understood that we can really drive change, we can disrupt this entire field, we can really drive with a strong computational basis diagnostics. And that was really the genesis of FDNA how we started.

Harry Glorikian: Yeah, I remember when you guys we were sitting at what was it Henrietta’s Table at the Charles Hotel and I said you guys told me this and I was like, oh my god that's just brilliant. I was like, and I always thought it would be direct to the patient. But you guys decided to go to the clinician and come about it from a sort of group learning, group educational perspective on how you teach the system. Tell me a little bit about how it's designed or and how its deployed and how it keeps learning?

Dekel Gelbman: So with AI, I think today even more than ever it is very obvious that it's a data plane. The more data you have the better the data is the better the technology can become. Learning algorithms and especially today with deep learning models, if you have enough data and the data is good, you can train a very accurate and advanced technology. But the problem in the challenges in this world, especially with rare diseases and genetic disorders is access to that data, how do you get data. When we started, we started with a lot of collaborations with different researchers around the world and everyone was very enthusiastic, but every single research site had only very limited quantities of data.

And so it got us thinking you know what's the best way to start gathering all the data - collecting, curating it. And I remember, it was one of our developers who said you know everyone uses iPhones right now, let's develop an app and ask all the geneticists around the world to help us annotate data and collect data. And we said you know, let's give it a try and that's how Face2gene our current platform was born, and in hindsight you know several years after launching Face2gene, this was a very successful strategy.

We were able to deliver an application that produces real-time value clinical value to clinicians and in return and we distributed it for free by the way. In return, we got a lot of data, and we were able to really advance our development of the technology significantly, because of this strategy.

Harry Glorikian: Well, and interestingly enough if I remember our conversations correctly, it wasn't just the acquisition of data but it was having experts in the field constantly teaching the system how to be more accurate by their experience.

Dekel Gelbman: That's the old AI. So, when we started really supervised learning or having experts teach the system, how to think was how we started, how people thought about AI at the time. In 2014, there was a different trend towards deep learning, where you really don't teach the system anything the computer identifies patterns on its own. It's sort of a black box and that's some of the criticism towards AI today is that being a black box. And that made curating quality data even more important more significant to that process because we no longer influence the system's method of learning.

So, everything that we influence is, how we collect the data, how we ensure the quality of the data and how we feed the system with data to avoid biases, overfitting, and a lot of the different problems that AI presents today with deep learning.

Harry Glorikian: Can you give me some examples of where this has really changed a timeline, improved that diagnostic Odyssey? How that's affected you know a patient or a family, and where do you see this, you know where do you see going from a cost perspective and so forth?

Dekel Gelbman: Absolutely, so you know it's very hard to give macro examples or macro data about time to diagnosis, but on a case-by-case basis we hear all the time from our physicians, from physicians using Face2gene, how this integrated into their workflow? How it simplified the workflow? How it helped them choose the right diagnostic tests? How it helped identify specific diagnoses for patients that were looking for a diagnosis for years? So, there are multiple examples and they've been published elsewhere both in scientific publications and the media.

But I want to tell you is what we've learned in our journey, because when we, you know as you articulated that in the beginning, the mandate that we had going into this journey was how can we help physicians identify or diagnose rare diseases in pediatric settings earlier. And as we started to gain traction as more and more hospitals started to use this as part of their workflow, as more and more researchers started to use this technology to make discoveries. We started hearing back from the laboratories, and this coincides with more accelerated adoption of next-generation sequencing.

The labs are starting to offer exome sequencing and whole genome sequencing to physicians as the primary genetic test. But they came back to us and said, listen we get too much information we generate too much information when we do an exome sequencing. And so we want clinicians to really adopt this as a test because of the broad coverage, we need to make sure that when we analyze the results we present to them results that are relevant, clinically relevant. And so it's not reasonable to present to a clinician, a thousand different variants that may or may not be pathogenic meaning that they may cause a disease or not.

We need to be able to present with that to them a short list of variants that may be causing a disease. In order to do that, we need to integrate what we call our jargon, calls phenotypic information, phenotypic being the information that captures the clinical observation of a patient. Is the patient tall, does the patient have certain clinical symptoms and does the face present certain patterns that are linked or associated with these diseases? And guess what Face2gene captured a lot of this phenotypic information as part of the clinical visit, the clinical evaluation. And then it dawned on us that you know we really hit something.

We started to investigate this further and we've participated in the study called PEDIA, that aimed to prioritize exome sequencing results based on facial analysis. The results were staggering we showed that for this cohort of patients, for this group of patients that had monogenic disorders that manifest in facial analysis. We can improve the diagnostic rate from about 40 percent to almost a hundred percent, and at that point, the term next-generation phenotyping was born and adopted by us as where we're going with this company.

We realized that if we offer a computer-based way, an AI best method to look at a patient and correlate that with the patient's genome, we would be able to pinpoint with very high accuracy, the disease-causing variants. And you're talking about cost, you can imagine what this does to this entire industry or the potential of what this can to the entire industry. This can facilitate genome sequencing for the entire population, and it now makes sense because we have a scalable approach into how to analyze and interpret genome sequencing data for the entire population.

And this could have a lot of impact on the future of precision health or precision medicine and that is obviously going to have a huge impact on cost. It's very hard to predict right now what that impact is going to be, and obviously, if we are to pursue this path, we need to go well beyond just a facial analysis, we need to look at the holistic phenotype of a patient. So, that's where we are right now and that's the journey ahead of us.

Harry Glorikian: So, when you were building this, tell me some of the experiences or lessons that you learned. You know you originally said, you know we were working on algorithms then we went to a black box machine learning system and you've worked it into the physician’s workflow. Give me some of your experiences on what it really took to get this to where it is today.

Dekel Gelbman: I think you touched on that, the last point I think is the most important one and the most difficult one in healthcare today is integrating with workflow. It is almost unimaginable to change the workflow of a caregiver. They're just too darn busy and trying to, re-educate them is never going to work. A lot of startups are trying to circumvent the healthcare provider. We don't believe in that future; we don't think that providers would disappear. We just think that their role is gonna change and so our strategy was how do we empower the caregivers; how do we empower physicians. And we do that by giving them pertinent data and giving them the ability to make educated decisions.

So, we're helping physicians and they're grateful and the community of clinical geneticist or medical geneticists really embraced us. Because we were giving them something that they were missing for years and years, and so we actually saved him a lot of time. The traction and the responses and the endorsement that we received from the physician was where we were focused, I would say in the last four years, really how do we give, how do we provide tools that are useful. And you know a lot of this is exploration, we develop something, we test it, we get feedback from the clinicians sometimes they love what we do, sometimes they don't. But they're very open and they're very responsive.

So, for us, that is probably one of the biggest assets that we have as a company is our relationships with our user base. And that really was important in our approach of, how we develop this technology. Everything is driven by what can be useful for our target audience. We learned along the way a lot of things and there are a lot of challenges. Workflow was one, right so how do we give the physicians the flexibility to use these tools and technologies without changing their workflow. Privacy is a huge issue and physicians are probably the gatekeepers for a lot of the privacy regulation around the world.

I'm talking about HIPAA and today GDPR are. The patient privacy is very important and it looks as though the last gatekeeper is the physician and they're doing a tremendous job. But we had to step up and improve our entire process. And go through compliance processes and ISO certification. Today we're ranked one of the highest ranking scores on AWS as in terms of our security and privacy infrastructure, but it took a lot of effort. Another thing that we've learned I think is how to be ethical in AI. And this is a I think a hot button today specifically in genetics, along the years most of the data that was curated was curated for Caucasian populations, and this created a huge gap in our knowledge our medical knowledge as a society on other ethnicities.

And so we made it a point to diversify our database so that we can be used not only for the Caucasian population but for ethnicities in Africa and Latin America and the Asia Pacific. And this made a huge difference by the way, not only did it made us grow our presence and today were being used in over a hundred and thirty countries around the world but it actually improved our AI. And this is a very interesting thing that I've learned along the years. When you train the system to look at different ethnicities, the morphology the way the face looks can be influenced by a variety of influencers. The ethnicity obviously environment can change how your face looks, not as much with the pediatric population but still and your genetics influence how your face looks like.

So, you have to discount some of these factors and by training the system on a very diverse ethnic population, you're basically taking off the table the differences that relate to ethnic origin, and you focus on the pathogenic morphology, only the morphology, only these patterns that are caused by those genetic disorders. So, just account a few things that we've learned along the way.

Harry Glorikian: How big of a data set do you need to or where are you guys now, compared to where you know it was just a few years ago? I imagine that acquiring this data because of the app is much easier, the amount of data that you're able to get in is significantly higher than going out there and trying to do this yourself or coming up with a specific piece of instrumentation necessarily to do this. And then it was just recently that you guys started incorporating the genomics part of it, and the announcement was not that long ago. But, how do you see that working into the success of the company?

We what we always try to come up with some special piece of technology whereas I feel like the tech world is moving so fast forward, and what it's bringing is pretty damn good quality and it keeps improving thinking of you know the iWatch and the detail you can get off of an iPhone camera and so forth. So, how do you see that playing a role in what you guys are doing?

Dekel Gelbman: So, you know again one of the challenges at the outset of the company was dealing with very small amounts of data. Our target number of diseases just with the facial analysis technology is somewhere between 2,500 and 4,000. And for each of these diseases sometimes there are only five reported cases in the history of publications. So, we're working with extremely small sets of data, for us that was a technology challenge that we've addressed through some methods like translational learning, where we learn from bigger data sets. And then we take that back to a smaller data set and apply what we've learned but generally speaking we work with very small data sets across or for each specific indication.

Face2gene was very successful in gathering more and more information to date, we have more than 120,000 patients that were processed and analyzed through face2gene obviously that enriches our database. The pace of uploading more and more patients into the system is increasing every month, and so I wouldn't be surprised if in two to three years we will actually reach around a million patients processed through this system. So, that really enhances our ability not only to improve the AI around identification of specific phenotypes but also broadens the coverage, so we can see more and more diseases.

And you were talking a little bit about other sensors like the iWatch. Part of our next-generation phenotype in approach is indeed to enhance our collection from beyond just a facial data into other phenotypic data. So, vital signs that are collected through wearables are part of that,  video processing even voice processing. So, the voice can be a very strong indication for certain diseases. Obviously, medical device information that is collected through existing medical devices and medical imaging, all this information should be funneled into a central location that will be able to improve our insights.

Now there are a lot of companies out there that are doing similar or have similar efforts. Our unique approach is that we take all this information and the sole purpose of that is to then look at the genome and try to identify the disease-causing variants. We're not developing radiology decision support tools or not developing agent diagnostic devices. Our sole purpose is to look at this information say, how can from this information we would be able to infer insights from the person's genome.

Harry Glorikian: So, you had started this with you know we're a bunch of technology guys that sort of stumbled into the world of healthcare. What are the experiences you can share as, you know what type of people do you need on the back end doing the coding, doing the work but then integrating that would say people who might be knowledgeable in the disease state and sort of making that whole thing happen? And you're not all in one place, you have different sites and so that whole process is there of lessons you can share or the magic you can share to help bridge that gap.

Because I always feel that technologists can code, but you need somebody that understands that health dynamic, that disease state, that workflow and then to have to somehow almost meld into one person to be able to produce something that is usable.

Dekel Gelbman: I wish I had a formula, it's not very easy to quantify what you need in order to succeed. I would say that generally and this is something that I truly believe in, disruption never comes from within an industry. It takes an outsider to look at something and try to solve a problem that exists for many years. At the same time, without the relationships that we've created over the years and without the involvement of medical geneticists in our company, we would have never understood the breadth and the depth of the problems that we're trying to solve. So, for us the AI approach was very straightforward, but going into diving into the details. it started to become extremely complex in terms of how the syndromes are categorized, how genetics works and that's information that we simply didn't have.

But as we dove deeper and deeper with the support of many experts in the genetics field and we have an extremely broad and involved scientific advisory board. If you take a look at our website, it's probably about 30 to 40 people that are involved, we don't pay them. They're there volunteering because they really believe in the future of this technology holds. Without their involvement we would have never succeeded to put technology to solve a problem. And without naming names, you know there are other companies out there that are very sophisticated and considered very prominent in the machine learning world.

I think their approach to involving the industry is wrong, taking just one or two sites to train a system or two to be the domain expert is not the right approach. You have to broaden the scope as much as possible, that's what we've done. We've been working with almost everyone in this field.

Harry Glorikian: Well yeah I mean, I think technology lends itself to or the technologies these days lend themselves to. I don't want to say crowdsourcing but you can get a much larger set of input if you're managing this correctly. When you're hiring people or when you're looking at certain skill sets that weren't on board, how do you think about that. Where might be some of the places that you'd look to find these individuals aren't falling off trees and if you were in the Bay Area, you'd be fighting tooth and nail for you know the person that hasn't even graduated yet. So, how are you taking on the right people and finding the right skill sets?

Dekel Gelbman: So, you know especially in the algorithmic development world, talent is extremely expensive, whether it's in the Bay Area, whether it's in New England or whether it's in Israel. These people are extremely expensive, the competition over recruitment is fierce and we're competing with some you know 800-pound gorillas in the market Amazon, Facebook, Google etc. The one thing that we have in our company that I've rarely seen in other companies is a purpose. And so this is a highly marketable trait for a company when you're recruiting, getting people on board that believe in the purpose of the company, believe that they can make an impact.

I think is such a powerful thing to have as a company, and coincidentally that's the kind of trait that I'm looking for when I hire people. So, the experience is important and dedication, diligence, intelligence all these traits are very important. The number one trait for me though is passion because I truly believe that if you're passionate about what you do and if you enjoy what you do and if you believe in what you do, then you're gonna put you know more from yourself into the company. You're gonna be more productive, you're gonna care.

And so that is probably the number one trait that I'm looking for when I'm hiring people, and that doesn't have to do with geography or with where you went to school. It's just you know it's what you care about, and so it's not that rare to find employees and talent that connect to the mandate of the company that believed in our vision, and recruitment has never been a huge issue for us.

Harry Glorikian: So, where do you see the company going next, from a technology evolution perspective, from clinical impact perspective and then you know sort of your vision beyond that. But sort of those two things I think the incorporation of technology these days is almost like a race, where you're constantly trying to keep up with the next chipset that's incorporated, the next software improvement that's coming faster than I've ever seen it in any other time. And then clinically, where do you see that going?

Dekel Gelbman: So, I think we have to be modest in our perspective on the impact that we can make and we need to be cognizant of macro-economic changes in healthcare that we have very little influence on. So, we need to look from the sidelines and try to evaluate where this field is going. We are strong believers that, we are entering into an era of precision health, we're strong believers that the main driver for that is genomics. We obviously believe that AI is a driver for these huge data sets and what we can do with them. And so within or from that insight, we believe that if we focus but really focus very hard on developing the best technology that regardless of time. I know that's a huge issue for startups right, but regardless of time whatever, it takes one year two years or five years. We need to focus on making this technology a standard of care alongside genomics and doing that for us means, focusing on value, showing value demonstrating value, showing how we can improve the benefits for all the stakeholders involved in our little space, which are physicians, researchers, labs obviously patients and then life science companies.

Harry Glorikian: If I read that correctly you're looking beyond the rare disease space.

Dekel Gelbman: I think the immediate value of what we're doing right now applies to the rare disease space. But the future implies that genomics is gonna play a key role in risk assessment for more complex and also more common diseases. As we start rooting ourselves into the genomics field, yes we see ourselves tagging along to that journey and going beyond rare diseases in the future into almost all diseases. But there's a huge gap that genomics needs to catch up to apply to other diseases.

Today I think you know mostly genomics is applied to rare diseases, oncology and that's pretty much where most of the genomics is focused right now.

Harry Glorikian: Yeah, I've always thought about some of the stuff that you guys are doing and saying well what if we just started applying that to a broader population. You know we call it a rare disease it seems to manifest itself in, what might be categorized as an issue or a problem or how it hinders someone from you know the life that they want to lead etc. But I want to say that there's, the deviation of that is you know, there's probably people that you call normal that probably have some of these traits that we're just they're subtle. So, you don't pick up on them.

So, I always wondered at the application of technology to the broader population.

Dekel Gelbman: I would argue that naming rare diseases is a huge disservice to these type of diseases. If you think about this if you think about the future of precision healthcare every disease is rare, because every disease is gonna be categorized as a unique subset of interactions between different biological systems and mechanisms. And so I think that in 20 years the term rare disease is gonna be obsolete because we will look at every single disease as a unique set of genotype-phenotype and other biological input or feeds into a computerized system, that's gonna analyze everything.

So, yes today we focus on rare diseases, we focus on the genomic side and, but that's I think that's gonna change along the years. We definitely look at FDNA on a very long term scale, we've always been able to do that with the support of our investors and the founders and even our employees. And I think that this is the right way to look at a startup.

Harry Glorikian: Anything I haven't asked you, words of wisdom you know experiences that you want to share before we sign off?

Dekel Gelbman: I think you've done a great job, thank you. It's always a pleasure to talk to you Harry and hear your insights on the world of health care and how that's developing. I think, we have the privilege to be operating in a very unique era. And hopefully we're gonna benefit from good timing and we're gonna seize the opportunity as a company. But even more important than that I really hope that the effort that we're doing with developing this technology is going to create a huge impact on patients.

Harry Glorikian: Yeah, I do believe in it's interesting, yeah I'm not sure that the algorithms are the secret sauce or the machine learning back-end or so forth. I feel like some of that is always going to be able to be reproduced by someone else. But the data set I believe is gonna have tremendous value and the impact that it has going forward. So, on that note I want to thank you very much for joining today, and look forward to continued dialogue and updates in the future.

Dekel Gelbman: Thank you very much very, Harry.

Harry Glorikian: Take care, and that's it for this episode.

Jason Bhan, co-founder and chief medical officer of Prognos, joins Harry to talk about how machine learning is being used to dig into multi-sourced clinical diagnostic data to improve health by predicting disease early.

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Transcript

Harry Glorikian: Welcome to the Moneyball Medicine podcast, I'm your host Harry Glorikian. This series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious value-based healthcare economy. We look at the challenges and opportunities they're facing and their predictions for the years to come.

I'd like to welcome our next guest Dr. Jason Bhan, who's the co-founder and chief medical officer of Prognos. He's a family physician and serves as chief medical officer of Prognos. He's regarded as a national expert in the applications of technology and medicine, a topic on which he speaks regularly at institutions and conferences, such as health 2.0, M-health, E-health collaborative and health data palooza.

He's also done extensive strategy consulting with different companies including pharmaceutical companies and others. Welcome to the show Jason a pleasure to have you here.

Dr. Jason Bhan: Thank you.

Harry Glorikian: So, tell me a little bit about Prognos, maybe a little bit about its history and what you guys are really doing.

Dr, Jason Bhan: Sure, so we have an incredibly ambitious vision which is to eradicate disease. And you might look at that and say, alright well that seems pretty incredible how are you gonna get there. And that is you know it's sort of like our 20-year vision out and we've got a mission which is to you know find and predict disease even earlier than it is today. So, when we started the company, probably seven or eight years ago you know it was -, we were trying to figure out how we could take data that was available in the system and use it for improving the lives of patients.

So, we looked at different data sets that were out there, we looked at you know claims data and prescription data. And what we really knew, I would really know from my years of practice was that sitting there in my office seeing you know 30 patients a day and ordering lots of blood tests on them and lab tests on them, and then going back and seeing. You know going back to my bat back to my desk and sitting there and looking at all of the test results that had been ordered from the prior days. I would sit there and I would make more relevant clinical decisions based on the lab test results that I was seeing that I did during the whole day of seeing patients.

So, we knew that you know this concept of lab data was really important. And in fact there's some studies out there, they show that more than 70% of all clinical decisions are based on lab test results. It's even more in areas like oncology and rare disease or it can be up to a hundred percent. So, we figured that we would start to work with lab data, and that was not easy because the lab system is fragmented. There's something like five or six thousand labs in the United States alone but many of them, but there's a you know that the top probably thousand handle most of the lab testing in the US, aside from acute care settings.

And so we started partnering with labs like LabCorp and Quest and working with them and helping them with their data, which was sort of the second problem is once you can start to aggregate all of that data, you have to clean and standardize all of that data. Clinical data is not easy to work with; it typically has a lot of unstructured aspects to it. So, we spend a lot of time just figuring out how to collect it and organize it, more than anything else. Once we were able to do that, we saw that there's a tremendous amount of value in it, both in the pharmaceutical space and in the payer space where we offer products today.

So, while we aggregate and collect all of this data and standardize and clean it, we actually turn it into products. And those products are what services are for clients and the idea was that you know early on, we were able to find patients that had particular diseases and we were pretty good at doing that. And then you know the sort of the next phase that we moved into was to predict which patients were going to develop X Y or Z.

It could be which patients were going to fail a particular therapy, predict which patients were going to go on to a particular therapy, which patients were needed to be tested more regularly or didn't need to be tested or we're missing tests in order to clinch a diagnosis. So, as we move into the prediction phase which has been over the last couple years, where we've really beefed up our computational expertise and AI and engineering. We've kind of understood more about how to predict these events. And then the idea is once you can predict something, you have to figure out what are the bright points to intervene.

And once you can intervene before something happens, then you've potentially moved towards the potential of eradicating that disease. So, that's sort of how we have our vision and how we've been moving towards it for the last couple of years.

Harry Glorikian: What have been some of the challenges that you've faced along the way to implement or make this a reality?

Dr. Jason Bhan: Yeah, there's a lot of challenges paired in this space. One I talked about was the fragmentation of the data, the data set itself. Two was obviously organizing and making that data fit for purpose, but there are a lot of other things that were challenges, right. So, in the sort of the standard healthcare data world, there are claims data and prescription data. And both of those are fairly commoditized there are a couple of you know players who have organized and brought all of that data together.

So, it was pretty well known but you know clinical data is different. And you know one of the things that we faced was it was new and just working with labs was new and labs were you know, it wasn't their business to be in data, their businesses running tests. And so anything new is you know foreign and guilty until proven innocent. So, we had a lot of work to do with just the labs in order to get them comfortable with the fact that we had all the safeguards in place for managing data.

 All you know that we were compliant with HIPAA and that you know that they weren't going to risk by providing all this, and that we were providing them something of value with what we were doing. So, that was a big hurdle, just in accessing this data and that took a long time just to get there. And then the other side of it is proving the value, right. So, everybody's used to using something on the far end of things right, as pharmaceutical companies are used to using claims and prescriptions, and so are payers.

So, convincing them that lab data and this clinical data was good enough or better than what they were currently using either to augment what they were currently using or replace it altogether. So, that was another challenge and anytime you're new to the market with something it's always a challenge. So, that's probably the biggest.

Harry Glorikian: So, where have you seen something that you weren't expecting, and where do you see this having the biggest impacts?

Dr. Jason Bhan: Sure, so one of the one of the things that we didn't expect was in the diversity of care that the patients receive. You know there are clinical guidelines that are published and doctors are supposed to follow clinical guidelines and patients are supposed to present themselves all the time and they're supposed to do. So, that they can, the doctors can follow the clinical guidelines. But you know the real world is different and doctors practice differently and patients aren't always as accessible as you would hope them to be, and even when they have major illnesses they don't present themselves as often as they should for care.

So, one of the things that we looked at, one of the diseases that we looked at was CML or chronic myelogenous leukaemia, which is a type of blood cancer. And what we found was that this is actually a great disease for lab testing. Because there's been a sort of a new, you know a test that's been around for a little while that looks at a molecular marker and you're able to track the course of the disease over time in the blood, which is you know that's like the holy grail for cancer.

And so basically there's this blood test and once you use it to diagnose the disease and then you use it to track the course of the disease. And so you're supposed to continue to test you know once a quarter until the person is in remission.

So, that's four tests a year and therapy has changed based on the results of the tests. If you are driving the tests you know they're driving the presence of the mutation down, then you know you can stay on your therapy if it's changing then it changes. And so what we found was that number one, patients on average we're being tested like 1.5 times per year instead of four times per year.

And those patients who were tested more frequently were having better outcomes, and so what we were able to, we actually did the work with the professor who came up with helped write the guidelines.

And he was just as shocked as we were that this testing frequency was so low. And what we kind of found was that look, you test people more then there are better outcomes and you drive them more towards remission. And I think that was sort of a shocking thing to find, not that it occurs but that it occurred at such a high rate and was such a discrepancy from the clinical guidelines. And you could certainly make many arguments on that, you could say that you should be educating providers about the importance of testing and not under testing, but making sure you're doing the appropriate amounts of testing.

You could educate patients with the disease on the importance of going to your providers and getting tested regularly. You can give heads up to payers on patients who aren't getting tested as frequently, as they should and getting them higher and more engaged with the patient's, so that the outcomes are better and the costs are lower. And you can work with pharmacy companies on educating providers, educating patients and figuring out how to even figure out how to pay for some of this testing, which you know I didn't occur.

You'd almost want it all to happen, you do want it all to happen it's you know ideally that's what would happen. But all we did was find all the correlations and then pass out the information to folks and hope that they can power some of their resources towards.

Harry Glorikian: So, when you guys are looking at the data Sciences side, you know sure that in the beginning it was much more simple analytics. You know actually probably the majority of your time was cleaning and organizing just to get it useful, but now it's now that it's sort of in a better State let's say or in a much more usable State. What are the challenges around you knowing hiring the right people, you know when you decide what sort of data analytics do you use? How complicated is it you know? Do you settle on a platform where you're constantly evolving to keep up with this constant set of change?

Dr. Jason Bhan: Yeah, so you know there are a number of different questions in there. One is just finding the right talent, that is not easy. It does make it easier that we have a very large unique and interesting data set. And it makes it very interesting that we are in the healthcare space. So, the people that we tend to find are those who want a new challenge with lots of data and want to make a meaningful contribution to the world. So, we know, we have recruited people out of the medical space and usually those data scientists are the ones who were recruited by a hospital system or by a company that had great science, but no data.

And so they were kind of tired of not really doing anything and just you know kind of theorizing all the time. And then we recruited folks out of industries like Ad Tech where their mantra was you know the right Ad to the right person at the right time, where we were saying things like the right drug to the right person at the right time, and that's very enticing to people. So, you know we made a hire a couple years ago and found our chief data scientist who came out of the Ad Tech space. And he's been great, he's a mathematician, peer scientist and loves the theory and you know and it keeps us all on our toes and pushes us to great new things.

He's also recruited an amazing top-tier group of AI data scientists that help us do what we do. And you know they are, the way they work is it's almost like they're playing with toys, and there's a new shiny one and they go and grab it. And that's great because that's the way you want to approach this space. You know something changes every week, there's a new platform that comes out every week or month. And it may actually be better than the one you used before.

You know I think actually this week, we're presenting alongside Amazon at the Jupiter con for being one of the biggest utilizers in the healthcare space of sage makers, which is their new platform that's AI base that's kind of going up against Google and Microsoft and others. So, you know we're experimenting with new technologies all the time, and you know AI technology is really a commodity at this point. You can, as long as you have the data and you have it formatted in a way that it can be absorbed into a system, then you can use just about any application out there. And oftentimes multiple applications in order to get the right answer.

Harry Glorikian: So, you know we were chatting on the way up here and you mentioned one of the people that's benefiting from the data that you're giving them and how they are shifting from sort of looking at the world from an actuarial perspective to actually predicting. Can you walk the people you know someone through that, how that evolved over time?

Dr. Jason Bhan: Sure, so we know that with clinical data, especially lab data you can infer a tremendous amount of information about an individual. How sick are they? What comorbid conditions they have, whether they have a disease or not but also where they are in their disease state? So, from a payer’s perspective someone with diabetes is interesting, but someone with diabetes that's poorly controlled who also has high blood pressure, high cholesterol is much more interesting from a number of different perspectives. One, because they're more likely to get sicker and two because in the world of insurance especially government you know either Medicare Advantage or the ACA population or Medicaid, the payer actually gets reimbursed more to care for that individual.

So, one of the products that we have out there is, both a sort of an identification and predictive product for payers. And what we do is we help them identify in a population of patients either that is existing for them or a new population that's coming in for them, where their risk is. And which patients are going to get sicker over the next 12 months and which ones are going to cost them more money or have more disease burden. And they're using that for two things, one is to direct resources towards those patients. So, that they can either impact their cost before it gets out of control and improve their health.

And second in the reimbursement from the government, because the more complex a patient is and the sooner you know about that complexity, the more money you can recover in order to take care of that patient. So, that's one and then the other is really around where you know predicting costs and traditional methods are in actuarial tables, right. Looking at demographics, slopes of an individual, where they live, what their age range is, what their occupation is sometimes, zip codes and other things. And then using that and sort of the law of large numbers and predicting what sort of bucket of cost that they'll fall into over the coming 12 months.

And what we've discovered is that, if you take lab tests and cost and look at that retrospectively and build, and let the Machine sort of go at that, that matrix of data you can then, based on lab data alone you can predict the future 12 months of cost or disease burden that's coming down from that patient population. And then what would you do with that, well you could do anything from directing resources towards it to correctly predicting your cost for that group of that population of patients and pleasing your investors as well as your bottom-line.

Harry Glorikian: So, where do you see this capability going in the future? Do you add other data sources to it that really changed the paradigm, like instead of just looking at lab tests you take on wearable data, so you monitor people in between. Where do you see the future going and what you guys have built and where would you like to see it be?

Dr Jason Bhan: Yeah, that's a great question. I mean, I think the Holy Grail is as much data on as many people as you can get. Health is so multifactorial, there are so many permutations of why a person goes down a particular path. That I think unless you have millions and millions of patients with millions of data points, we're really not going to understand what and why. Even the predictions that we're making now are inaccurate, because of that. We're more accurate than the old ways, but we're still inaccurate because of all the different things that can go into a person's health.

And to your point, I think adding more data sets is a great way of improving that, right. There is wearable data, there's that sort of whole healthcare data layer of information that's collected on patients either through passive or active sensors. The challenge with that is they're not mainstream yet, I mean the people who are using fit bits are the ones who probably don't eat it as much they're generally healthy and walking and other things. So, it'll be great when you know you know Apple kind of gets a critical mass of users using their systems, more people are using fit bit’s and so on and so forth.

But that's a layer, I think, so seeing like the spending behaviors of people is really impressive. I mean you know location information at any given time, you know if you could imagine you see someone hop from McDonald's to McDonald's on a day-to-day basis. And then correlate that with the amount of money they're spending there and then with their lab test results and their claims history, I mean that's incredibly powerful. And then I think adding in genetics data to that once we sort of know what to do with full genome sequencing, is a really powerful addition to the set.

So, you know, continuing to add data and add data, and ideally the cost of computing all of that and continues to drop, so that it doesn't become prohibitive. Because that is right now even so sort of an issue, it is sort of the cost of crunching all this data. And then where does it go, I think ultimately it goes to the individual. I believe that you know strongly that health care reform in the last ten years has been about empowering individuals, educating them, driving cost down and improving care and improving access. But I do believe that empowering people with the information will help drive change.

And we do that now through Pharma and payers, but ultimately I think you know you drive it to providers and give them the information that they need and ultimately down to patients. And give them the information in a format they can consume and with recommendations that make sense. And I think that's when you really start to drive like the disease curve and the cost curve down.

Harry Glorikian: So, what do you see is the next set of hurdles, either for you guys or for companies like you to sort of move the needle on the, what you guys are trying to do?

Dr. Jason Bhan: Yeah, I think you know the sort of the four-letter word and in the industry is interoperability. There is a lot of data available on a lot of people out there from a healthcare standpoint. Unfortunately, it's in a lot of silos and those silos are, they're not just from a technical standpoint but they're from a process standpoint and just a business standpoint. If you can imagine if you're an electronic health record company, who makes your money on you knowing your subscriptions to your EHR, and the server that sits in the doctor's office. And the doctor says you know, hey I want to port my data out of this to somewhere else. What's my incentive to do that as an EHR?

I'm just gonna lose that doctor as a client, because they're gonna go to the next EHR that's just as easy. So, you know and it's certainly not like a judgment on these folks, these are all businesses, but there's no incentive to share data. So, the government's been working on that for years and has really yet been able to, as have yet been able to incentivize all this or create a standard that the industry can use. So, while the Holy Grail is collecting all of the data on all of the people. I still see we're a long way off from that, so we're just kind of attacking it in a piecemeal way.

Harry Glorikian: So, that begs the  question of like, Apple making EHR portable on its platform. It's not everything in the record, but is that a bridge are they disrupting the interoperability of these systems and sort of almost usurping, the players not wanting to play.

Dr. Jason Bhan: Yeah I hope so, now they're still using the standard which is called fire and so all the EHRs need to play nicely with fire, which they may or may not. And in all honesty the most valuable piece of information, the most immediately valuable piece of information coming out of the EHR as biometrics. It's like blood pressure, height, weight and some basic social information. So, that I think the Apple software will be able to pull relatively straightforward, and you'll still have some issues with it. But it's a lot easier than saying trying to pull out a note and deciding, whether I met Michigan or myocardial infarction.

So, there's a lot of work that has to go into that, but I think there's some immediate wins that can come out of apples play here and make some data available to the system which has not really been at scale available.

Harry Glorikian: Yeah, and I mean that I think the next version is that the user will be able to share that data with an app that they could want to share with.

Dr. Jason Bhan: Yeah.

Harry Glorikian: So, if Prognos had an app available that could interact, that would be another way to have a data ingestion engine with a standardized set of data.

Dr. Jason Bhan: Yeah and I presume that Apple will probably allow anonymized versions of that data so that consumers will be able to consent and non-anonymize versions of their data to go out without someone having an app that'll be available. And I think that's actually, that's how we function. Our registry which you know has 18 billion records on 180 million patients, is actually all de-identified. But that's our training set, that's where we build all of our algorithms from. And then once you have an algorithm built you know, it's all these little fragments and pieces of patient journeys.

Once you have a person come in as an individual, you then map them to whatever point along the journey that they are and then you can give them their individualized ideas, so that is, right. So, if Apple would make or if anyone makes de-identified data available more broadly, then you can use that to create those algorithms and then create the app that an individual would then get mapped against and it would tell them what their health is or what they're you know predicting what their future looks like.

Harry Glorikian: So, it sounds like a very promising future. I know the majority of your clients are you know insurers and pharmaceutical companies, but it sounds like you guys are slowly moving towards you know eventually getting to the patient.

Dr. Jason Bhan: Yeah, I mean ultimately that's I do believe that that's where the biggest impact will be made. And interacting with patients is very different than interacting with Pharma or healthcare systems. You have to have a very tailored approach to that. And you know as a physician I definitely know how to work with patients and how to get them to do what you're trying to get them to do in order to improve their health. But it's tough, I mean there are entire companies out there that are focused solely on how do you get a patient to engage with their own health. And that's not an area of expertise for us, but we will certainly piggyback on that and power whatever we can to help them figure out how to get those patients engaged.

Harry Glorikian: So, it sounds like you know maybe a partnership within Amazon or a Google, that has a tremendous level of data on the consumer and what drives different behaviors might make sense for an organization like yours.

Dr. Jason Bhan: Yeah absolutely, I think that does make a lot of sense and looking and you know those guys do have great understanding of consumer behavior. So, what if you were to add in a layer of medical information or clinical data on top of that, what could you understand or predict or influence. I think what you want to do at the end of the day is, how do you get that person to not walk into McDonald's?

Harry Glorikian: Great. Is there anything else that I didn't ask that you think would be critical for people to hear about you, the company or where you think the space is going?

Dr. Jason Bhan: Look these are exciting times and it is the AI and healthcare space is evolving, so quickly. I do get a little concerned when I see you know these small startups with no real business models. Because we do need businesses  that are able to sustain themselves and you know have models that allow them to you know companies accompany them, generate revenue and that will last. I've been in the health 2, 0 and health tech space for a long time now, and I just see too many companies start and fail.

So, you know spending time on a really good business model, figuring out how to take incremental steps towards solving problems instead of like trying to just leapfrog, now if you're, you know if you've just sold your startup and you've got money to burn. And that's great, go solve the biggest problems but not everybody has that opportunity. So, it's really about you know we would all love for the healthcare system to free all its data and for everything to flow and work perfectly together.

But it doesn't work that way, and I think you know the more companies that are taking bite-sized chunks out of it, towards moving us all towards that solution and then cooperating and collaborating between them. I think that's sort of the way that the thing the industry will go, will succeed.

Harry Glorikian: Yeah, I think of it like when I know one person makes a scale the other one makes the blood pressure cuff in it. But they have API's that allow, so one app to sort of aggregate that data into one place. And so there seems to be a free flow of data if you're on the, keep yourself healthy side. It's once you cross over into the well you are sort of sick or in the healthcare system that everything gets locked down.

Dr. Jason Bhan: Locked down and also, unfortunately the people who are sick and unhealthy are not the ones who are big consumers of the wearables and the scales and all of this. And that's really the challenge, when we get good at passive data collection, I think we will have a kind of a breakthrough on data on the on the sick population, not just the well population.

Harry Glorikian: Well on that note, I want to thank you very much for joining today. Hope everybody listening enjoyed it and look forward to continuing the conversation in the future.

Dr. Jason Bhan: Yeah great, thanks for the opportunity.

Harry Glorikian: And that's it for this episode. I hope you enjoyed the insights and discussion. For more information, please feel free to go to www.glorikian.com. Hope you join us next time, until then farewell.

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Harry interviews Niven Narain, the co-founder, president and CEO of Berg, a Boston-based biopharma company driving the next generation of drugs and diagnostics by combining patient-driven biology and AI to unravel actionable disease insight. Narain has overseen development of Berg’s clinical stage assets and pipeline and forged strategic partnerships with industry academic and US and UK governments. He says Berg's philosophy is to combine a systems biology architecture with patients' demographic data and clinical outcome data, and then apply Bayesian artificial intelligence algorithms to drive better understanding of diseases.

To learn more visit glorikian.com/podcast/

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Transcript 

Harry Glorikian: Welcome to the Moneyball medicine podcast I'm your host Harry Glorikian. This series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious value-based healthcare economy. We look at the challenges and opportunities they're facing and their predictions for the years to come.

Okay welcome to another edition of Moneyball Medicine. Today I have Niven Narayan who is co-founder president and CEO of Berg, a Boston-based biopharma company driving the next generation of drugs and Diagnostics by combining patient driven biology and artificial intelligence to unravel actionable disease insight. He has overseen development of Berg's clinical stage assets and pipeline and forged strategic partners with industry academia and US and UK government's.

Niven is most passionate about improving patient care and enabling increased access to innovative medicines to improve healthcare outcomes.

Niven welcome to Moneyball Medicine podcast, it's great to spend time together again.

Niven Narain: It’s great to be on again, Harry, it's always good to catch up and I think it's such an important continuous dialogue you know given how quickly technology is moving in healthcare. So, again happy to be on.

Harry Glorikian: I had the pleasure of learning about Berg and coming in and taking a look at your systems and being brought up to speed, on what you guys are doing during the writing of Moneyball Medicine. But since then you know and maybe for the people listening for the first time and who don't know the company. Can you tell me a little bit about you know this whole concept that you have of a artificial-intelligence, drug discovery model engine and where we were back what two plus years ago and where you are now?

Niven Narain: Yes, sure you know, so the company was really founded on this the philosophy that we should at this point in developed and this is about ten years back. We took a good hard look of how could we use biology in a more fundamental sense to drive a greater understanding of diseases. But importantly how our disease is different than a healthy, an otherwise healthy individual or a healthy cell or a healthy tissue. And the approach that we took at that time was really to combine a systems biology architecture with a combination of a patient's demographic data, their clinical outcome data.

And then we wanted to look at a novel way of how do we analyze this data, because obviously this is in the late 2000s, you know early 2010's. And our decision at that point was to take an agnostic approach to not bias ourselves by what was known already, so looking for example that you know Jiwa studies and the to known or traditional pathways. And our approach is really to bring a new data topology and new data ecosystem together, where one could look at genes and proteins and demographics and a patient's, clinical story overall and then feed this data architecture into a Bayesian artificial intelligence system.

And this Bayesian AI system is really well positioned to analyze this type of data, because what we're trying to get at is not just a correlation. So, a lot of analytical methods look at how A is correlated to B, and how that correlation may you know may predict a greater depth of understanding. But what we're really after is, how do we understand the elements within a patient's biology to link a causal inference between a mutation of a certain gene or a dysregulated expression profile of a protein in a given pathway.

And then using that as a pivot to correlate that you know, wow this is what is it could be responsible for the onset of prostate cancer or Parkinson's disease or why certain individuals don't respond to a certain drug. So, this entire, you know this whole approach was really it was really novel at that time in the sense that, we were allowing the data to guide us to the hypotheses instead of you know the traditional sense of taking hypotheses and going through a lot of data generation processes.

So, since we've last had you know such a forum, two years ago. We've advanced significantly on our pancreatic cancer drug, which was then, we were still wrapping up our phase one solid tumor approach. And you know since then we've now embarked into a face to pancreatic trial, that trial is really a precision oncology trial. So, we were collecting tissues and samples and you know blood your own etc. on these patients, were able to build a biological profile on these patients. We're able then to map that profile against whether or not the patient has a response or not.

And that's really important because that then allows us to truly engage with patient stratification modules or so, as we go into late stage registration on pivotal trials, we would then be able to create you know protocols. Where we can engage companion diagnostics or engage the molecular profile analysis, before allowing a patient to come into the trial. So, it allows us to be more precise, allows for more predictive you know modeling in the drug development process. But you know something I care about it also allows us for patients who are at the end stage of their lives to for us to conduct more ethical clinical trials.

Because if we know that our drugs probably not going to work for that patient, it's in the best interest of both parties to not offer that patient that drug. So, in pancreatic cancer we've made significant strides both on the drug development and a diagnostic component. We've advanced a really exciting technology and epidermolysis bullosa where in the end stages of wrapping up of phase 1, trial down at the University of Miami and we're now in the planning stages of a phase 3 registration trial, in that indication which is a rare a childhood disease of the skin. It really creates a lot of blistering and postures and impaired wound healing.

So, an extremely deleterious disease to the skin and otherwise the psychosocial effects and kids, on that realm also for the psychosocial component we have a drug that's now in the phase 3 planning phases for chemotherapy induced alopecia. We've just wrapped up the trial, it early in a year at Cedars-Sinai and Memorial Sloan-Kettering that asset is, it really is gonna seek to fill an unmet need in cancer, we're for most almost 60% of chemo therapies induced alopecia which is hair loss. And that really gives a patient of stark awareness a stark, acute reminder that they have cancer.

They can feel it, they can see it and that's psychosocial component I think is so important. So, advancing this clinical asset into an enabling trial we're extremely excited about that. So, really you know late-stage plans for these three assets in pancreatic cancer, chemotherapy induced alopecia and EB. And then on the heels of the clinical development we then also have made, you know pretty significant progress on a pipeline. So, we have two more second-generation cancer drugs and development that are now marching towards IND-enabling trials.

We have a really exciting a novel drug target for lark to meet mutated Parkinson's disease, and we've now seen from a recent publication that came out of about a month ago that, some of these mutations may behave like the idiopathic kind in other parts of Parkinson's. So, the company has made strides you know clinically but also developmentally in the cancer and neurological diseases. And so really this platform which is interrogative biology has really helped to fuel and guide late stage developments in our clinical assets, reposition, I'm sorry reposition some of the known assets and then really fuel a de-novo pipeline of drugs.

Harry Glorikian: Tell me with the platform and this approach of using artificial intelligence, and your Bayesian AI system basically, does it shorten the timeline? Does it identify new pathways; can you do it with a lower you know with that with lower number of people for lower cost? What are all the, why do it this way? What are the benefits of this?

Niven Narain: Yeah, so if I I'll answer your question in a three-prong sense, Harry. One philosophically and scientifically, I think doing it this way allows us to not throw away the data that doesn't you know necessarily satisfy a statistical significance or alpha. I don't think disease you know cares about what satisfies statistical significance or traditional ways of looking at data. We only you know, we for the most part include the data that that satisfies this point of five significances. But there are lots of data and I think the point I'm trying to make is that disease is not very neat, it's very complex it's very messy.

And when you look at it from a mathematical in a statistical perspective we have to allow all of the correlations and all of the implications of that data to have a voice. And so this approach allows but you know by taking a Bayesian AI approach, which there are really no cut offs. There's no preconceived hypotheses to say well we're gonna just have a cut-off of 80% of the data or 60% of data, we feed all of the data into the system. Clinically it's important, because we're putting literally when you know big hot button term is patient-centric. What does that really mean you know how do you really define that?

And I think for Berg it's being a patient-centric by starting the process of drug development with human tissue samples. Learning as much as we can about the clinical records, learning as much as we can about the components of the biology within those samples, and allowing the math to give power give rise to that biology. So, he can teach us more about what's going on in the medicine. So, dynamically we learn about the disease much more fundamentally. Scientifically we take a much broader unbiased approach. Clinically we're allowing for more fundamental insight into what's going on into disease.

And then when you add on the business perspective of it you know because you're learning more about the disease and the patient profile that you're studying, you're probably gonna you know produce drugs that are much more predictive towards a given population. Which really is defining and exemplifying what precision medicine is from a pure business operational excellence perspective, we don't need a thousand people to discover a drug. We don't need five to seven years and the average 150 million according to the Demasi, you know the recent Demasi numbers.

We're able to really drive lean operationally efficient discovery programs, because it's very data heavy it's very technologically heavy and you know our scientist or our operators that are on every disease or every target. They're able to really dynamically interact with this data in a sense where, they can you know concede and touch it and feel it in a way that it allows that data to really come to life. So, we're able to of course spend a lot less money on a traditional discovery program. We are reducing the trial and error.

We're allowing the data to guide us to where we need to focus in on, and then very quickly the discovery teams you know work with development teams to determine what is the best platform, a development platform to put this and should it be, you know a protein base drugs, is it a biologic. Should we look at you know RNAI or CRISPR based technologies should we you know look at a small molecule screen very quickly. So, all of this is done in a modular sense very quickly and I think that's just been a huge advantage to how efficient predictive and cost-effective we can get from a pure concept to a validated drug target or a validated diagnostic.

Harry Glorikian: So, if you were to put some sort of rough percentage increases or time savings or people savings. Like, what would you sort of give it a rough estimate of compared to the traditional model?

Niven Narain: Yes, so I'm just gonna use really generic you know numbers and I'm gonna just use the VC model. So, the average series A, in the VC is you know from a VC back company from concept to proof a principle, you know let's say proof of principle to the IND, average is about 22 to 25 million, and that takes about two to three years. Berg is able to cut that in more than half and build a model from concept to a validated disease target or a validated you know diagnostic in about six to nine months. So, that's even more than 50% and that's just using a VC model as you know as a denominator or predicate.

Some may say that's an unfair model to use, if I can use an academic model which of course numbers are lower, but the time is longer. So, the two levers are time and cost if we use a Big Pharma model the infrastructure is bigger, the cost is being a because of a measure of that infrastructure that the cost is higher, but the time doesn't change that much. So, you know when you look at the lean and the rapidity of the lean nature of what we're doing in the rapidity to the validation. It's a stark contrast from what's or traditional senses and even with the advent of technologies over the past three to five years.

Because to our listeners you know some may say, well gee is hey you know biology has come a long way and it has the emerging technologies have enabled like CRISPR Cas9 and sort of enabled more rapidity and innovation. That's true but we still have to then validate all those models as a measure of what these validated phenotypes are, because at the end of the day these discoveries have to then go into a funnel and either creating an IND to do first in man trials, reposition an asset. Whether that's a phase two or phase three or a diagnostic asset, where we now have to go back into retrospective or clinical prospective trials to validate this this biomarker in a patient population.

So, the way that we're going to validate this is not changed, it's still the clinical trials. How do we either make the clinical trial more predictive more lean and effective, or how do we get as much information upfront? So, we know we're triaging the biology against the disease phenotype, the population against the outcome the proposed and desired disease outcome, and then the market size relative to my up for an investment in cost. So, it's you know I think these methodologies allow also, I think Harry you know one of the points I've appreciated over the past couple of years. It allows companies like Berg to go into diseases that are ultra-weir or rear with a higher degree of confidence you know knowing that, these methodologies allow us to get to a go or no-go decision much quicker.

So, in diseases like EB or other rare diseases that triage process allows us to study these types of diseases, where in other cases it's a you know the investment is a risk.

Harry Glorikian: From what I'm hearing from you, do you believe that this sort of technology trend and I have seen many come and go over time this fundamental approach of utilizing machine learning and AI for drug discovery is going to be, how things are done in the future?

Niven Narain: I think absolutely, I think what's gonna calibrate and position how AI machine learning is going to be used most effectively is outcome. Until we don't develop the first drug to be guided or the first drug to be developed with AI, either is a repositioned drug which is you know like our BPM three, one, five, one, zero or a de novo development that's just flat-out protein or a small molecule that has come out of a machine learning or an AI system. That then is the world's first pivot to development. Berg is if I'm not mistaken has validated the world's first clinical Diagnostics and in prostate cancer.

So, we worked with the Department of Defense to just you know literally from Ground Zero to take the health records and the biological records you know predicted. We have found some markers that show the separation between benign prostate hypertrophy and prostate cancer, you know less aggressive versus more aggressive prostate cancers. We validate this is now in retrospective prospective trials and over 1500 patients. So, this really shows that this process can work. I think that if we take a step back and think about the journey of the drug, the drug developer, the physician and the patient. How is this technology going to help each stakeholder, and what is the pathway to commercialization governed by? And it's governed by payers and regulators.

So, I have seen firsthand, I think all of us should be able to widely accept that the FDA are the regulatory agencies have made leaps and bounds of trying their best to try to understand these technologies keep up with them, engage workshops, engage these conversations to say, okay how did it really work. What changes do we have to make? What do we need to teach within the agency, there's new awareness of how we review a review process works? Scott got leave has just, he's amazed me, because he's a physician but he's I think he's demonstrated in a really short time that he's not gonna allow yesterday's biases to carry over into tomorrow's approval process. And the payers, payers are paying in making investments in technological companies to really try to figure out, okay if this is really true how do you help me make my process more efficient. Because right now approximately I'm spending about sixty to eighty percent of my reimbursement monies on approximately twenty percent of those who recovered. So, when you look at the pressure points within the system which the two pressure points and the levers are, how do we engage the regulators to help us get these products approved. Because if the products are not approved this is just a bunch of fancy science.

It sounds harsh, but it's true and if the payers are not gonna pay for it, then you can still get a drug or technology approved what's gonna be adoption and implementation. So, those two big levers have made such tremendous leaps and bounds in the past three years, that it allows folks like me folks like you know, companies like Berg's to really have a lens of hope that the investment in the technology and the investment in a time, the investment in these types of approaches. If you can create the right products that show that you're safe, it's safe, it's validated you have a process of showing that these diagnostics or these drugs really gonna create a step change.

Unlike five years ago Harry, if you remember the conversations at the conference's, there were whole sections of conferences that dealt with, well how is the FDA gonna look at it, how are regulators gonna look at it or payers gonna understand it. You don't see those tracks at conferences anymore, you see FDA representatives or representatives from pairs speak on panels, right next to CEOs, right next to leading scientists or clinicians. The conversation is here; I think the future is really exciting. I think we need to continuously educate each other. We need to, I don't think we're all speaking different languages anymore I think we've actually found a language of machine learning in AI.

I think what we really need to do is now you know bring together a lens in a concentration around how do, we all together advance these technologies as safely, as quickly as responsibly and ethically as possible. Because the next generation of healthcare is absolutely gonna be based on using mathematics, using machine learning analytical methods, artificial intelligence, virtual reality, augmented reality to you know to allow the patient story to be told in a way, that allows drug developers to create drugs that we can't even imagine today.

Harry Glorikian: So, there I would say let me challenge you on that, so I'm not challenging the payer the regulator there's always struggling to keep up with everything that we're doing. But you know we're gonna create a new company using machine learning AI and so forth. The hardware is advancing at unprecedented rates, right. The software is improving every time you turn around. So, what do what do we need to do to? I mean totally different set of employees in my mind right and a hybrid, I need somebody who understands the biology.

And then I need somebody that can actually write the code, and then I need that upgradable on a regular basis. Because otherwise if NVIDIA is new chip is ten times more powerful than the last chipset, well the guy who comes after me leave me in the dust, because his processing capability is that much better that much faster. Now I know the fundamental data is what drives these systems, but you know I'm just where do we need to be what do we need to be doing from an implementation hiring perspective, capabilities perspective in your mind.

I remember when I interviewed you the last time, you said you know at one point we needed to go back and rewrite some of the stuff we were working on, because we got some new blood that came in and showed us a new way to look at it. So, how do you balance those things for companies that are coming up that want to be the next Berg?

Niven Narain: I think you have to say, look we've made our very healthy share of mistakes along the way. It's not as you can imagine not been an easy road, in anything it's never an easy road but it's never an easy road when it's uncharted and innovative you know territory. So, if you just take I think the only analogy I can think of in my mind to, when you think of the future is you take a piece of paper and a pencil. And a piece of paper and a pencil, makes a note. Now you upgrade that and there's a typewriter, you upgrade a typewriter you got Microsoft Word.

You upgrade Microsoft Word, you have these technologies and machine learning that has a speech recognition capabilities. We've just gone through four platforms of simply writing and that's just simply writing, just putting a word down to a recording, a recorded piece of instrument. That instrument went from a paper to a typewriter, to a software to now an Augmented software, and but it empirically has changed and has been altered over time. Because it started out with the hands and the eyes and the brain. But then we added in the mouth at the end now and now with speech recognition is, it's using you know language in a different way.

It's combining more empirical components, that's exactly what we're doing in biology. Because we started out you know looking at you know an individual genes as we looked at gels, we looked at you know animal models, now there's AI and machine learning and how is it all gonna keep up is, I would submit to you in your challenge that it's not gonna be easy. But what I would also you know balance that recognition of that challenge is that, unlike where there were only a few companies you know who would create word processors, you know whether it was word or other processors.

There's so many companies did the critical mass of individuals and entities there to dealing with the issue is whether it's software hardware or education. And I should really emphasize the educational component because I think it was a nature commentary a few months back, where I said the PhD programs of the future they can't be just you know, I think the days of just getting a PhD in computer science or a PhD and molecular biology. The individuals we're gonna make the biggest change in the future, those individuals who really know math and biology or know CS and biology or know CS and medicine, but it's gonna be a hybrid system.

I agree it's gonna be biology plus or it's gonna be math plus, and that's really what the employee of the future is gonna be most successful. And I think that is gonna take, I mean I think we're aware of because we're having a conversation. So, that's people check the box on that.

Harry Glorikian: I'm not sure it's everybody .

Niven Narain: That's fair, but the educational process has to change. I think you're seeing, I mean unfortunately right now it's you know I named kind of the same names, and they're really the leading institutions you know Stanford, Columbia, Oxford, Harvard you know Carnegie Mellon etc. There many others, but we still have not met that mass, you know critical educational sea change that is bringing together this hybrid, this fusion of Technology if you will. So, I think that's one extremely important component.

But having said that I don't think, it's we're out doing Moore's Law in so many ways we've outdone it in software, we're all doing it in hardware for sure. And I think on the educational component since the forums and platforms and the access entry points to education have been completely revolutionized. Because of things like the Khan Academy, because of it you know things like AI, you know some of the platforms that the Gates Foundation and others. And there are many others those are just you know some of the ones that come to mind very quickly, but you need not go to a classroom anymore to learn. You need not be a part of a formal community anymore to learn, you literally can learn off of a computer-based interchange.

Now the practical components of that have to be played out you know obviously within the community. But I think since that's changed so much Harry, uh the point of bringing this together the enhancements, the Corrections, the course changes or the course Corrections that are gonna be inevitable. I think it gonna happen much quicker in the next few years and they would have happened ten years ago. So I think, I'm a bit more hopeful that folks being able to learn from the mistakes, the mistakes you know frankly that the company is like Berg's made and others, which I think we need to be very transparent open and frank about things that we've done well, things we haven't done well.

You know I think one of the big mistakes we performed early on is we were so tunnel vision into the technology, that we didn't bring in some of the endpoint stakeholders. I think we brought him in a bit too late, if we had brought them in earlier like some senior members of the pharmacist societies, some you know you know doing a partnership with Pharma earlier. You know speaking to payers earlier, engaging folks like you know like Medicare or you know the NHS or you know providers help us really understand what really matters, how do we develop technology is in a much broader sense.

I think we would have potentially you know gotten there faster or had more robust data. But having said that it was a first you know we were doing things for the first time. And you know looking back on the ten years I think what's gonna help the next ten years, be more effective for our company and for many other companies and groups is that, we have to have these conversations and share -. It's so important to share what we all think is the right thing to do. It is gonna be even more important to share what we think is not the right thing to do or frankly just a wrong thing to do. And I think we have a moral responsibility to speak up more about that.

It's like you know people don't like to publish bad data. Well, we need to start to talk about bad processes or wrong processes, because it's just gonna help the community get there faster. And of course there's competitive intelligence and you know companies are competing against each other, but if you think that longview you're only helping yourself.  Because of you if the payers and the providers and the regulators, they get it more effectively and they get it in a less you know timeframe, it helps everyone that's charging for it in that same direction. It helps the entire community. So, I think that's the way we need to look at it.

Harry Glorikian: Well, if you look at technology companies right, they come up with standards. All the AI research is being published by all the players and they're competing more on the data that they have that's proprietary to them, but not the algorithm not the code, that's really reducible. So, that's not the necessarily the protectable asset.

Niven Narain: No, I think the algorithms are, I mean they're there many companies and groups who are just frankly using you know open source software, sharing their software you have great academic groups like Atul Butte, group at UCSF Eric Schadt at Mount Sinai, Andrea Califano, Chaz Boudreaux Oxford. They, I mean these guys literally share all their data and they're very open about how they do processes. And I think those are four names that I admire, because it's showing that, you know intellectual property is really important you know you know patents help to preserve your right for a defined period of time to sell a product.

And that's really important for commerce, but in order to move the needle significantly and create a sea change in innovation, I think there's a key difference between the innovations that's necessary to make big steps and big changes towards the scientific discoveries. Because that alone if everyone can share and get that part of it right, then now it’s incumbent on a company or a group to then innovate how do they create novel products that are protectable around that. And those are two really different layers of innovation, they oftentimes get lumped together and that's where a lot of issues and problems come out.

But if we can understand that you know this is really a multi-layered process of innovation, where it's like a pyramid and at the bottom, everyone's got to play well together and be open and be transparent. And that allows us all to be better and then out of that funnel of that initial baseline of innovation, now it's incumbent on individual groups to productized when you productize and of course you can have IP and patents around that. That's really important because otherwise where's the incentive and then the top layer above that is then the commercial and the reimbursement and the proliferation of the business models that actually have a repetitive and a sustainable model of revenue to fuel the ongoing second, third, fourth generation products of that initial innovation. And if we could think about it in those layers I think you know we can make a hell of a lot more progress.

Harry Glorikian: So on that note, I want to thank you for joining me today on the podcast. And look forward to future interactions and hear more updates on Berg and where it's going and how you're changing outcomes for patients and driving technology forward. So, thank you very much for spending the time today.

Niven Narain: Well, thank you Harry and I know in closing I just like to say, I think you've done a fantastic job of allowing the voices, you know multiple voices to be heard. Because I think that's really important, I know every time we talk or every time you make an introduction to someone else. I always get a different lens and that's really important for me as a scientist, as a CEO, as a human being. So, really I think your podcast and you know obviously your books that you put out and the narrative that you were helping to create within this industry for all of us.

I think is really unique, because you're touching CEOs, you're touching the senior academicians, you know pairs you know folks from government and you're bringing that conversation together. So, I think this is a really cool outlet to make us really think about what we're doing, so we can be better at it. So, thank you Harry.

Harry Glorikian: Thank you very much. And that's it for this episode, hope you enjoyed the insights and discussion. For more information, please feel free to go to www.glorycamp.com. Hope you join us next time, until then farewell.

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Quality control is one of those things that only a select few people pay attention to—until something goes wrong, then everyone cares. That's especially true in the drug manufacturing industry, where episodes like cross-contamination in a drug factory can shut down a production line and create instant shortages of important medicines. And if a contaminated medicines ever does get shipped out to clinics or stores, people’s lives can be at stake. So drug makers are usually pretty receptive toward any new technology that can help them detect manufacturing problems before they get out of hand.

That’s the market opening that Harry's guest Taylor Chartier says she saw back in 2020, during the coronavirus pandemic. Chartier watched the stories about the Baltimore company Emergent BioSolutions, which was manufacturing vaccines for Johnson & Johnson and AstraZeneca and had to throw out millions of doses of both vaccines due to suspected cross-contamination, and thought: there has to be a better way. So she started her own company. And today her startup Modicus Prime is partnering with top pharma companies to use new machine vision and AI capabilities to catch drug manufacturing problems faster.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

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Host Harry Glorikian talks with Dr. Glenn Steele, chairman of G. Steele Health Solutions, which helps healthcare organizations improve quality, and vice chairman of the Health Transformation Alliance, a cooperative of self-insured employers. Dr. Steele is the former chairman of XG Health Solutions and former president and CEO of Geisinger Health Systems, and he shares his views on how data and analytics are changing every aspect of healthcare.

To learn more visit glorikian.com/podcast/

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac.

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

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There’s a lot of talk out there about how artificial intelligence will change the way doctors and nurses take care of patients; you hear some of it right here on this show. But all of that still feels like a forecast rather than a present reality. When you look really closely, it’s hard to find concrete examples where AI is already helping healthcare providers make better decisions that improve patient outcomes and take costs out of the system.

That’s why Harry wanted to have Nassib Chamoun on the show. Chamoun is the founder and CEO of Health Data Analytics Institute (HDAI), which has been working with a major healthcare system, Houston Methodist, to test out a working platform called HealthVision. It's a collection of AI-driven models that use huge amounts of data, both from Medicare and from Houston’s own electronic health record system, to make predictions that help doctors and administrators spend less time poring over records and data, and more time interacting with actual patients and making good clinical and management decisions.

Nassib has a way of talking about HDAI and HealthVision that leaves out the hype and focuses on the real-world problems AI can solve for doctors and administrators—like how to identify the patients discharged from hospitals to their homes or to skilled nursing facilities who are at the highest risk of complications, and which interventions could help keep them alive and prevent readmission. Nassib tells Harry that “AI is not magic" and points out that even the most famous large language models, like ChatGPT, are just massive statistical representations of data created, collected, or curated by humans. And while these models are powerful, Nassib argues they’ll need guardrails around them to guarantee transparency and explainability and to prevent bias, before they can be useful in high-stakes fields like healthcare.

HDAI has raised tens of millions of dollars of capital and spent seven years developing HealthVision, and now the company is getting ready to grow beyond Houston Methodist and deploy the system at other big healthcare institutions like the Cleveland Clinic and the Dana-Farber Cancer Institute—so more providers will get a chance to test whether AI can keep patients healthier and make healthcare delivery more efficient.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

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There’s a good chance that we’re all going to live a lot longer than we think. Or at least, that’s what Harry's guest Sergey Young argues in his book The Science and Technology of Growing Young. Young is an investor who leads a $100 million venture capital fund called the Longevity Vision Fund, and through his investing, he says he meets innovators who are coming up with the technologies that will extend our healthy lifespans not just by years but by decades. Those technologies include better drugs, of course, but also gene editing to rejuvenate our DNA and methods for regenerating or replacing old organs, just the way you’d replace the worn-out parts in an old car. All these technologies are coming faster than we think, Young says, and the big question is how widely they’ll be available and whether everyone who wants them will have access to them. That’s the theme of Young’s work at the Longevity Vision Fund, which focuses on companies creating affordable and accessible life extension technologies.

For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast 

Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:

1. Open the Podcasts app on your iPhone, iPad, or Mac. 

2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.

3. Scroll down to find the subhead titled "Ratings & Reviews."

4. Under one of the highlighted reviews, select "Write a Review."

5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 

6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.

7. Once you've finished, select "Send" or "Save" in the top-right corner. 

8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 

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That's it! Thanks so much.