Real world data (RWD) is as old as data itself. It was relatively useless, however, until it was digitized. And then once it was digitized, we struggled to analyze so much data at scale with any kind of efficiency.
Enter our ability to store and analyze large volumes of data and advancements in data science, algorithms and technology. With the help of AI, it’s now possible to truly leverage RWD and begin to understand patient populations in holistic ways that have so far eluded pharmaceutical companies. It’s accelerating the use of evidence across healthcare to support the focus on patient health outcomes.
To better understand the relationship between AI and RWD and its potential, I spoke with Principal Asheesh Shukla, who leads ZS’s real-world data and patient insights practice and has worked extensively with AI.
A: RWD is any information that’s generated by the healthcare ecosystem as healthcare is delivered to patients. That includes data from outpatient settings, hospitalizations, clinics, pharmacies, labs and increasingly wearables and other devices. Regulatory and service authorities like the FDA or CMS have adapted well to the digitization of healthcare and they acknowledge the many opportunities that RWD has opened up for healthcare delivery.
I think the overhype behind RWD has died down now and we’re shifting from post-disillusionment to becoming more informed. We're aware of the problems we may encounter with RWD. For example, we lack universal identifiers to help us distinguish patients so we can match them between data sets. We need to improve data capture accuracy and consistency. We need better and more consistent population coverage. And that’s where the story converges with what's happening on the AI side. As the storage and computing costs continue to shrink, many of these AI methods that have been around for a while are making a resurgence. We’re working around the known issues with data quality and still generating relevant insights. We’re predicting events with increasing precision and relevance.
AI methodologies are hungry for data. Machine learning requires us to deal with a large representation of what happens in real life, and there are very few examples of such data sets in the marketplace. So machine learning has to discover connections across data sets and corelate events in patients’ treatment journeys, continually making refinements. That’s where AI is most effective and RWD offers the best fodder for AI methods.
A: I think it's worthwhile to first look at where RWD can be leveraged. Historically, real-world data was used to develop what we call “potential value.” For example, which disease areas should we invest in? What kind of product profile should we target? It was also used to validate and prove the value of a product to various stakeholders such as regulatory authorities, payers and providers.
Now we see how AI can be used across the pharma value chain. We started with potential value in research, then we moved on to proving the value during product launches. Now we’re seeing value prediction in clinical development. We’re seeing AI in trial design, feasibility and execution. Everything we’re doing on the clinical R&D side incorporates more real-world data, like site selection, enrollment predictions, protocol design and so on.
The most exciting use of RWD and AI is in providing value to patients. In pharma, we’ve been hearing about value-based contracting, targeted patient services, and looking at identifying untreated and undertreated patients with slow disease progressions. You can use RWD and AI to make better patient event predictions and provide more timely, targeted patient assistance. The patient journey is analyzed to make next best action and next best customer suggestions to the field, so that reps’ conversations with physicians stay relevant and value oriented.
Where AI really proves its worth is with prediction and providing value across healthcare including with clinical decision support. Prediction problems work nicely with AI methods and techniques. AI is also used for tracking value over a longer period in longitudinal studies, looking at longer duration and demonstrating therapy outcomes to the payer population, providers and regulatory authorities. That typically requires tracking a large volume of data over a long period of time. And those are the two areas where AI is playing a critical role because the traditional techniques for data analysis just don't work as well in terms of the base they can operate from, as well as the cost and the time it takes to deliver results.
A: I think the answer is both. Pharma has always run campaigns for physicians and detailing for physicians. We’ve had campaigns for patients. We’ve run patient services. There are these existing processes, services and functional areas that have been around for a long time. We believe you can improve and reimagine these areas from multiple dimensions by infusing AI and real-world data at the same time.
For example, in clinical trial design, we’re not only seeing RWD and AI methods that process and mine patient data, but we’re also seeing clinical trial feasibility predictions based on historical and current patient data available around selected sites, recruitment viability, coverage and other parameters.
On the commercial side, next best action and next best customer recommendations for the field are becoming prioritized programs, where we use AI and RWD. We take real-world data and use AI methods to track a patient’s journey and make predictions about potential events and confidence associated with the predictions informing sales and marketing decisions. In the pilots of these solutions, we’re seeing companies have a lot of success with very good, even double-digit ROI, both in terms of sales as well as significant cost savings from targeted efforts of up to 30%.
I also believe we should look at ways that real-world data and AI can create disruption outside the boundaries of existing processes. For example, historically, post commercialization label expansion triggered a post-marketing clinical trial. These are expensive and difficult to execute. Now we have trials generating real world evidence that can complement or in some cases replace them.
Reimagining existing processes and disrupting old processes should happen in parallel. We always recommend that clients focus on existing processes first because that’s what fuels and keeps the momentum going in the organization and gets the funding required for them to experiment on the disruption side.
The future as I see it is in the ongoing expansion in healthcare from disease management to wellness management. RWD is population focused right now, but can it also be more personalized? So far there has been an either-or mentality, partly driven by privacy concerns, lagging regulations and data veracity. As we get precise and consistent in our ability to leverage real world insights for key decision making, the trust in RWD and AI methods will increase. As we’re able to combine determinants of health beyond medical data and be inclusive of genomics, social determinants, environmental and other data sets, our ability to personalize prediction will improve. Can we shift to a disease management model where we’re not responding to illness but predicting it and prescribing therapies to avoid the onset of disease? To me, that’s always been the true promise of AI and RWD.