Real-world evidence has been increasingly recognized as an important complement to clinical research. In clinical research, randomized controlled trials often have stringent inclusion criteria which may exclude certain patient groups, such as those with comorbidities. When clinical trial populations diverge from patient populations seen in a real-world care setting, insights gained might not be widely applicable to all people. Analyzing real-world data, such as clinical information recorded in electronic health records (EHR) as part of routine clinical care, can provide insights that are generalizable to real-world populations.
The Hol Picture
Our Insights on Real-World Evidence and Behavioral Health
In the realm of behavioral and mental health, electronic health records (EHR) data is a goldmine of information. However, the challenge lies in the fact that these data often need more density for comprehensive analysis.
Innovation in behavioral health has long been limited by a lack of available data to support evidence-based decision-making. This lack of data can be attributed to many factors. Some of these factors are impossible to change, such as the heterogeneity of how the same diagnosis presents differently from patient to patient. Others could be changed but not without cooperation across behavioral health stakeholders; for example, a lack of consensus regarding how to define patient improvement, in addition to which tools to use to measure improvement, has resulted in a lack of standardization unseen in other areas of health.
As biopharmaceutical companies and regulatory bodies look toward adopting EHR-derived real-world data to complete studies more efficiently and affordably, one major concern that often arises is what has come to be known as “data missingness.”
The treatment of behavioral health conditions has historically been challenging. Despite the prevalent use of the DSM-V in clinical settings, clinicians lack a detailed and standardized vocabulary to discuss these conditions. This is due to a range of factors, from wide differences in disease presentation to stigma surrounding mental health conditions. The lack of standardized vocabulary has led to a subjective approach in treating these conditions, with each clinician relying on his or her own experience.
However, as more and more patients who are treated for behavioral health conditions are documented within an electronic health record (EHR) system, researchers now have a valuable tool for studying and improving the treatment of behavioral health conditions. By bringing together vast quantities of real-world data to understand how care and treatment are delivered in clinical practice, we can start building a set of standard definitions and objective measures for mental health conditions.
This is why we have created the NeuroBlu Database, in which we have extracted and organized EHR data from behavioral health clinics across the U.S. Our NeuroBlu data has thus far been leveraged by 5 of the top 15 biopharmaceutical companies with a behavioral health pipeline. These companies can benefit greatly from EHR-derived real-world data, particularly in the areas of research and development, medical affairs, and health economics and outcomes research (HEOR).
Previously on this blog, my colleague Alex has called for a paradigm shift to clinical data derived from the EHR as front-line evidence. He also alluded to some of the limitations of claims data, which have traditionally been used as a primary form of real-world data.
Huge amounts of data are collected throughout a person’s journey through the behavioral healthcare system. From demographic data captured during intake to in-depth conversations between patients and clinicians about symptoms, daily activities, and goals, most of the information captured about a patient becomes part of their electronic health record (EHR).
As a mental health clinician and researcher, I have seen firsthand how our understanding of the role that biological and socioenvironmental factors play in mental health has evolved over time. For many years, the debate over nature vs. nurture dominated discussions in the field, but more recent models have focused on the interaction between genes and the environment, such as the diathesis stress model.
I recently had the pleasure of joining Real-World Wednesday, a conversation hosted weekly on Clubhouse covering various topics about real-world data. First off, I’d like to thank the hosts of the RWD-RWE Club, Matt Veatch and Aaron Kamauu, for the invitation and the engaging discussion.
At Holmusk, our vision is to provide fit-for-purpose real-world data that fuel research and innovation in behavioral health. With tons of data captured each day as patients move through healthcare systems—much of it in unstructured data fields—a lot goes on behind the scenes, as we ensure that this information is available and usable for research. Below, you’ll find a quick overview on the types of data that are included in the NeuroBlu Database—as well as the process data go through to ensure they are fit-for-purpose.