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.
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Our Insights on Real-World Evidence and Behavioral Health
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.
This is an excerpt from a longer post that originally appeared on Going Digital: Behavioral Health Tech.
Real-world data (RWD) are becoming increasingly critical to clinical research. The FDA has put forth definitions surrounding RWD, as well as issued guidance around its use in research emphasizing the principle of data being “fit-for-purpose”—selecting the data needed to answer the question at hand. Meanwhile, stakeholders engaged in clinical development have increasingly recognized that RWD will enable them to conduct studies faster, at a lower cost, and often, with a more representative and diverse population.
However, not all RWD is fit-for-purpose—that is, captured and stored in such a way that the data is ready to address the question at hand. In order to move forward with using RWD in a way that is efficient and effective, we need to build a shared understanding of the different types of data within the broad umbrella that is RWD and make clear which type of RWD is fit-for-purpose for a specific question.
The HM Treasury in the United Kingdom recently announced an exciting and truly forward-looking initiative for healthcare: a package of government funding, called the Life Sci for Growth package, to fuel research and bring treatments to patients faster. There are many impressive projects being funded, from clinical trial improvements to preparation efforts for future public health emergencies.
Capitalizing on untapped value: Extracting environmental stressors from clinical notes via natural language processing
Consider an emergency room doctor who is caring for a patient who has been hospitalized after a suicide attempt. When she consults his chart, she can see that he has been diagnosed with depression, but the structured data that is immediately available does not provide much additional context.
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).
Imagine that you are a therapist seeing a new patient for the first time. The patient says that while she has no history of clinical depression, she has been feeling really sad lately and has lost interest in the things she normally enjoys, such as cooking and creating new recipes.
When you start to ask questions, however, you begin to uncover the factors that may be impacting what your patient is currently experiencing. She tells you she has recently moved across the country and has had trouble finding a new community where she feels connected. Because she feels so sad most of the time, she has stopped calling anyone from her previous home, telling you that she “doesn’t want to bring them down.” You hypothesize that the recent drastic changes in her environment may be contributing to the symptoms of depression she describes.