For years, we have known that we have an abundance of real-world data and evidence that could drive real insights and enable much more personalized medicine — in which a drug is more precisely matched to a patient rather than a more generalized population — but that it is largely hidden in clinical narratives and unstructured electronic medical records preventing the data from being analyzed at scale. Recent innovations in artificial intelligence and natural language processing technologies, however, are removing these barriers and helping to realize the promise of real-world data and personalized medicine. Stefan Weiss, M.D., FAAD, Managing Director of Dermatology at OM1, a real-world data, outcomes, and technology company with a focus on chronic diseases, discusses the promise of these data and how OM1 is working to overcome the inherent challenges, with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D.

David Alvaro (DA): Can you introduce OM1 and the mission to which the company is dedicated?

Stefan Weiss (SW):  OM1 focuses on finding new ways to drive improved patient outcomes. All of healthcare and medicine ultimately aims to extend the lives of patients and at the same time offering a better quality of life. OM1 approaches that broader mission from the standpoint of data to determine how best to leverage healthcare data to optimize outcomes. How does data drive our ability to make healthcare more efficient? How can we improve clinical trial design by understanding the patient population? How can we identify patients who tend to be underrepresented in clinical trials and incorporate those groups more into clinical investigation so that we develop drugs for the entire population that suffers from a given disease rather than only a portion of that population ?  How do we use data to understand where certain therapies work better or worse, either intrinsically or within different sub-populations?

Over the last 50 years or so, we have seen that different patient populations — whether classified by race, gender, or other discrete categories — have different responses to drugs, including immunology. If, for example, certain population groups have better responses to a particular mechanism of action for psoriatic arthritis, we want to ensure that we can explain those data and present them to the greater scientific community so physicians know that the corresponding product would be a better or worse choice for patients within these groups.

Real-world data are also critical to truly understanding the safety of a particular product, because the patients enrolled in a given clinical trial may not always be fully representative of the patient population that will ultimately need that drug. It’s important to investigate whether we find the same side effect profiles and safety outcomes in large community-based populations that we do in the clinical trial setting.

All of those areas of big data analysis have become critical to driving improved outcomes and improved health status of populations. At OM1, we focus on three of the four primary areas of drug development — immunology, cardiometabolic medicine, and mental health / neuroscience — and all of the disease states within those areas, representing chronic conditions with significant unmet needs. If you can intercede appropriately, and drive better outcomes in these areas, you can have a real impact on people for a very long time.

DA: What are the foundational technologies that OM1 leverages to unlock all the secrets within all of those healthcare data?

SW:  We have a number of different technologies that play and build upon each other. The first task for the technology is to efficiently extract data from disparate sources. Community physicians use different EMRs (electronic medical record), hospitals typically use EMRs that differ from those community physicians, pharmacies and pharmacy claims providers use their own systems, and medical payments come from yet another system. Until you can get all of those systems to speak to each other in a language that everybody understands, those data remain siloed, which prevents generating a full picture of the patient. A key differentiator for OM1 is that we center  on the patient, whereas many other data vendors center on the  claim.

We see the world of medicine as driven by the human beings who suffer from disease. If you start at that point and focus on the clinical description of the patient journey, you can then bolt on data from a host of other sources using a process of tokenization. Our technological innovation focuses on consolidating data from different EMRs into a usable format; with that foundation, it is possible to layer on a variety of different data sources.

Central among these technologies is natural (or medical) language processing to allow the “machine” to read the notes. Certain medical specialties are very structured in their data — think cardiology as it captures  blood pressure, cholesterol levels, etc. — simple, consistent, and repeatable numbers. On the other hand, notes from a psychiatrist typically take the form of an hour-long re-telling of a patient’s story without much structured data. Somebody — or something — needs to read that note and pull out the important parts. I’m a dermatologist, and for dermatologists, it’s very much a clinical narrative. “Red, itchy rash on elbow” describes the patient’s story and provides a huge amount of information to support our understanding of how that disease impacts the patient. In dermatology, that becomes critical, because as we think about treatments, we want to know where the individual is being impacted.

When converting that narrative into something more structured, disease presentation and disease location are two of the most important fields. The next priorities would be the severity of the disease and how it is measured in a standard outcome. Sometimes, we record it in a structured field, but more often than not it’s recorded in that unstructured narrative. If a person has moderate psoriasis or a BSA of 10%, we won’t be able to use those data unless the machine can read them. But if it can, we can track disease severity, and then bring in AI on top of that to apply big data models to create disease estimations. 

In mental health, we have a whole collection of PHQ-9s (patient health questionnaires), which is a disease severity score for people with depression, but it isn’t always recorded consistently. However, if there’s enough information captured in the notes, the machine can process and understand the patient’s overall mental health status and assign a category level, or ePHQ9. The ePHQ9, or estimate PHQ9, has a high level of concordance with the recorded PHQ9 and thus becomes a very useful tool for research.  

Understanding what disease severity looks like at a single point in time allows you to track the severity over time, which provides a better understanding of the response to a given drug. Is the patient’s disease severity improving, worsening, or staying the same? What side effects is this person having (weight gain or loss; r more itching; sleeplessness)? How do those symptoms correlate with the severity of the disease and/or the improvement of that individual’s disease severity with a change in therapy?

Gathering all of those data can help us understand why people stop taking a drug, which allows us to create discontinuation reports. We’ve all been prescribed medication for one condition for another, and then at some point that medicine changes. But why? Is it because we weren’t responsive, because we were experiencing side effects, or because we suddenly developed another illness for which that drug may have been contraindicated? All of that is typically documented in the clinical narrative in the EMR, but there’s no structured field for it to be easily recorded and extracted.

Once a machine structures those data, it can create models to characterize this phenomenon, which enables insights. Hypothetically, we know that 32% of patients with a particular immunologic disease (i.e., psoriasis) stopped drug A because of a contraindication or because of a lack of tolerance. We can even drill down more precisely to weight gain, weight loss, or diarrhea. Researchers can then understand — at scale — why patients are stopping a particular drug or why providers are stopping patients on that drug. This type  of data is much better than what was formerly possible: doing market research of 15 physicians, hoping that they can recall why they may have stopped drug X for disease Y, and then extrapolating across thousands of physicians the insights from those 15.

The AI model also enables our PhenOM^TM platform, which is focused on phenotypically fingerprinting a patient to both help make better sense of clinical and real-world data and enable more personalized medicine. Are there particular patients who would respond better to drug X for rheumatoid arthritis, for instance? The best way to understand that is to analyze patients who have done well versus patients who have done poorly on that drug. Once that has been established, how a new patient corresponds to those subtypes of patients can be linked. For example, it becomes possible to infer that, based on the phenotypical fingerprint of the patient, that patient would have an 80–90% chance of responding well to drug A but only a 30–40% chance of responding well to drug B.” This personalization facilitates an unbelievable shift in the arc of medicine in terms of getting the right patient on the right drug at the right time.

DA:  Before natural language processing and AI became as advanced as they are today, discussions about leveraging real-world data and evidence often emphasized the need to build more of that structure you discussed directly into the EMRs. Have you seen much progress on that front, and is that still critical?

SW: It will always be easier to capture data that are already in structured fields, and having those structured fields within an EMR serves as a reminder to providers to document those individual data points, which is important. However, a really transformative change in structuring and standardization of data elements would need to come from the EMR companies. However, because there isn’t a single EMR used even within a given specialty, buy-in would be required from every EMR and every provider. As providers have fought technological intrusions into clinical medicine for decades, this would just add more fuel to that fire.

DA:  I wanted to speak more about the Reasons for Discontinuation (RfD) reports that OM1 has published. Can you discuss your vision for how these data can be used and the impact the reports can have?

SW: The RfD reports present very important information that could never before be analyzed at scale because most of the reasons for discontinuation of a drug have always been captured in narrative form. Without a way to aggregate all of this EMR information and validate it comprehensively, the industry could only do market research in focus groups to understand the perception of a drug and why it was started or stopped in very small segments of clinicians. With these RfD reports, discontinuation at scale across broad swaths of a population, assessing both a diversity of providers and a diversity of patients can be investigated. The reason why those using the drug then stopped can now be identified.

The first reports that have been launched focus on treatments for chronic inflammatory diseases because of the breadth of data within dermatology and rheumatology already in the system. This allowed the models to be tested and validated more easily. After the process was established, it became possible to expand into mental health diseases such that  an enhanced appreciation of how drugs targeting depression or schizophrenia are being utilized in the marketplace could be obtained, as well as why some of those therapies have better or worse persistence than others.  

The RfD reports are also expanding our understanding of how subtypes of patients respond to a given drug. This will enable clinicians to make better determinations about whether a given patient from a specific sub-population of patients is more/less appropriate to start on a given drug. The RfD report can either confirm or dispel myths: a side effect that may only have been experienced by a handful of people could have been assumed to be more prevalent (i.e., a sampling bias). If a physician only treats a small number of patients with a condition and two of them randomly experience a side effect, and the physician publishes a case report, it may be incorrectly extrapolated that the side effect is far more common than it is in actuality. With more data and more evidence, a much better understanding of which side effects are actually widespread versus those that are just amplified anecdotes can be achieved.      

DA: To what extent do you see — now or in the future — an intersection or synergy between the work you are doing with the RfD reports and PhenOM?

SW: For now, the reports are separate, but it is easy to envision how the analyses could work together as each expands. It’s typical to launch new products in silos and then watch the inevitable intersection. Imagine being able to design a clinical trial that specifically recruits patients who have failed or had specific side effects on a given drug because the trial is investigating a drug that isn’t believed to have those side effects. Ultimately, clinical trial insights, PhenOM, and RfD all drive toward the same central peak: improving patient outcomes.

DA: In the long run, how do you see real-world data and evidence intersecting with and impacting traditional clinical trials models?  

SW: I see real-world data as complementing rather than supplanting what we’re doing in clinical trials. For example, large real-world data sets enable clinical trials to have better control arms. Clinical trials typically compare investigational drug A versus placebo. However, in certain disease states, do you really want to deprive any patient of treatment? Obviously not. Thus, the answer is to collect real-world data on matched controls.

For example, we can design a clinical trial investigating a new drug for a rare immunologic disease versus methotrexate, a common therapy used across immunologic diseases. Methotrexate may work reasonably well. Is it appropriate to deny a patient  methotrexate and replace it with a placebo, in order to understand if and how the investigational  drug works? We are better served by comparing the investigational drug to a control arm of similar patients on methotrexate in the real world.  

Real-world data also again offer the opportunity to meet some of the FDA’s initiatives that seek to ensure that a diversity of patient populations is being studied in clinical trials. Without diverse representation in trials, it will be challenging to assess whether the drugs are going to work in the overall population versus only in a specific population that is located geographically near more traditional research sites.

DA: I also understand that OM1 has been working with the American Academy of Dermatology (AAD) and DataDerm to explore drug safety implications and personalized medicine in dermatology? Can you discuss that?

SW: DataDerm is a registry created by AAD in 2016 — with data extending back to 2013 — that tracks data across patient populations that are being seen by a large representation of the community of dermatologists in the United States. It was designed largely to facilitate dermatologists qualifying for the meaningful use requirements that were set up by CMS (Centers for Medicare & Medicaid Services) as part of the Medicare value payment structure. However, in so doing, it became a  rich source of data on dermatology patients that can be used to explore many of these research questions.

AAD is the voice of the dermatology community, patients, and physicians.  Partnering with specialty societies is an excellent opportunity to work with established authorities and leverage knowledge gleaned outside the specialty  to support dermatology. A similar relationship exists with the American Academy of Otolaryngology to focus on diseases that are relevant to that patient population.  

DA: Beyond everything we have discussed and what is forthcoming on those fronts, is there anything else you can share or tease about what else may be coming in next few years for OM1 or from this sector more broadly?  

SW: The overall focus will continue to be rolling out more and more products that focus on the personalization of medicine. Personalized medicine has been a goal in medicine for more than two decades. How is it achieved? When a patient walks into a doctor’s office with a particular disease, how can the doctor know if that patient is really well suited to secure the desired outcomes from a particular drug? I really do believe that this long-sought goal — the right drug for the right patient at the right time — is closer than ever.

Everything builds upon everything else. As has been suggested, the RfD reports lead into PhenOM, and PhenOM leads into clinical trial insights. All of that is predicated on having really good models of AI to understand disease severity, which can only be accomplished when the technology to obtain and “read” the clinical notes has been optimized.

If all of this is considered as a series of building blocks, much of the foundational work has been accomplished. The question now is how best to leverage all of those learnings to create an environment where that level of personalization can be achieved. After that, the focus can broaden: start with a particular disease like psoriasis or therapeutic discipline like immunology, and then take the lessons learned and bring those insights into heart failure or depression. From there, move into epilepsy or obesity.

Working one step at a time allows skills and capacities to be built, tested, and validated. Most of the immunologic diseases are similar, so the lessons learned in rheumatoid arthritis (RA) can play out in psoriatic arthritis (PSA), which we can then play out in psoriasis, and so on.

It’s not really different than how drugs get developed. Take Humira, which was the best-selling drug in America for almost a decade and addressed 10 indications. All 10 of those indications were not pursued at the same time. First, it was shown to work  in RA, and then in PSA. Seeing that it worked really well in PSA, psoriasis was studied, and so on. I think that we are following a model of product development that is very similar to the way that our partners on the pharmaceutical side approach complex problems. By validating and testing the models and proving out the research, we can jump from one therapeutic area to the next one, as was evidenced with the RfD report. The approach was validated in rheumatology and dermatology, and now it is being launched into mental health. If that works, it can be then applied to neurologic disorders, cardiac disease, and beyond.

DA: Do you think that the underlying AI technology is sufficiently advanced at this point to achieve those aims and it is more a question of training it on more data, or is more technological evolution still critical?  

SW: I think the evolution will continue, and we will keep tweaking what we are doing as things evolve. Had we not demonstrated the ability to develop a disease estimation model in RA, where we had tremendous quantities of data to train and test the model, we would have never figured out that the technology was applicable to hidradenitis suppurativa (HS). Staring with HS would never work, however, because there just are not enough data points to say with confidence that such an approach works. But it is possible to transition from RA to multiple sclerosis and then to Crohn’s disease, PSA, and ankylosing spondylitis and eventually refine the models to approach a more rare disease like HS. All of the work builds on what came before and enables what comes next.

DA: Is there anything you’d like to particularly underscore as a closing thought?

SW:  Everything reduces to one central theme: how to drive better outcomes for patients. At the end of the day, that’s why those of us in our respective areas of medicine and healthcare do what we do. It’s a mission-driven activity. I began my career as a physician to help individual patients who walked into the office. I’ve spent time in drug development, and I’ve brought multiple drugs to market for patients who suffered from psoriasis and atopic dermatitis but needed a better therapy than what was available.  Now, with big data,  I can take a larger perspective, but the real focus remains the same: how we can deliver the right drug to the right person at the right time.