When most people think about clinical research and development, they consider the years of lab work, clinical trials, and FDA examination that go into a drug’s approval. But once a drug makes it past the long and rigorous development and commercialization process, there’s a lot of work that can be done to measure the efficacy and impact of that drug in the real world.

Health economic and outcomes research (HEOR) teams play a critical role both in designing effective clinical trials, and then understanding the post-approval impact of interventions and how they are functioning in the real world. This real-world evidence can deepen the healthcare community’s understanding of therapies and inform new guidance to improve care. 

Predictive analytics for HEOR have long promised to streamline and improve these critical functions by delivering a more accurate projection of adherence to prescriptions, optimal pricing in the marketplace, and real-world efficacy. Yet, for all the technological advances in this space over the past decade, most AI-powered solutions for HEOR are not equipped to answer these questions. 

The reason: fragmented, incomplete datasets that don’t take the complete patient experience into account.

For example, imagine a standard HEOR project to investigate a potential label expansion for an antidepressant commonly used off-label to treat ADHD. The HEOR team needs real-world prescribing data of the drug as well as data on reported ADHD symptoms. Historically, that HEOR team would use a legacy insurance claims database as their primary research data source. That dataset often suffered from data lag, with most data points being a year or more old and failing to represent the most current information on each patient. Perhaps most limiting, these legacy data sources would provide only a narrow snapshot of the patient’s in-network healthcare experiences and exclude the many key touchpoints with the healthcare system that are not picked up by insurance.

This approach – while carrying the veneer of advanced data science – leaves many gaps and, in turn, provides insufficient answers to the important questions HEOR teams are asking. 

That’s why we’ve been so obsessed with closing those data gaps at Komodo Health. For example, our Prism and Sentinel applications allow stakeholders across healthcare and life sciences to leverage our Healthcare Map™ alongside their own proprietary data to develop algorithms and uncover insights based on a complete view of real-world patient behaviors and treatment patterns.

Unlike legacy claims databases that provide an incomplete, one-dimensional view of healthcare encounters, these solutions pull in data from multiple sources to incorporate the entire patient experience into the analytics equation. With visibility into healthcare encounters for more than 325 million patients from our Healthcare Map, these solutions also allow clients and partners to bring in any of their unique data, linking it to Komodo’s data for even richer insights. Partners can pair our Healthcare Map with anything from lab data to patient-reported outcomes to grocery store shopping data.

For example, in our partnership with Kantar Health, we’re able to seamlessly pair our clinical encounter data with their patient-reported outcomes databases. By creating a patient cohort in our Prism application and then layering in disparate data sets from Kantar, we instantly add an entirely new dimension to the data, giving HEOR teams the ability to link real-world encounters with the healthcare system and correlate those encounters with self-reported outcomes.

This helps them understand the nature and the nurture of the patient journey, capturing not only what conditions specific patients have, which doctors they are seeing and how their disease is progressing, but also what they are feeling, whether or not they are exercising, and how they are eating. From there, the data can be analyzed further in our Sentinel application to better understand projected outcomes for a particular patient cohort.

These types of insights are just the beginning. By teaming with specialist data providers that capture everything from prescription refill data to lab results to consumer behavior, we’re making it possible for HEOR teams to develop a nuanced, high-resolution view of real-world patient experiences that can fill the knowledge gaps that have plagued the industry for so long.

The drug development processes that take place in lab and trial settings are highly controlled and precise. Now, the technology is in place to make our approach to the real-world, human side of the equation just as scientific. It’s up to us to leverage these breakthroughs to unlock the healthcare industry’s fullest potential.

Learn how HEOR teams are leveraging Komodo’s Healthcare Map to answer data-driven questions with the depth and accuracy that they need: learn more.