The Hol Picture

The landscape of mental health research is evolving, and tools like Natural Language Processing (NLP) hold immense potential to bridge the gap between clinical trials and real-world evidence. As we strive to enhance our understanding of mental health outcomes and treatment effectiveness, a strategic implementation of NLP tools becomes pivotal.

This blog explores a high-level overview of how we developed NeuroBlu NLP - Holmusk’s NLP models specifically tailored for extracting disease-specific clinical features from unstructured clinical text. These NLP-derived clinical features are then integrated into our structured data and made available in NeuroBlu, Holmusk’s powerful data analytics software for behavioral health. The incorporation of NLP-derived features into structured data provides a more comprehensive, easily accessible estimate of patient phenotypes in the real-world, ultimately benefiting patient care.

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.

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.

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).

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. 

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). 

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 led a team of researchers in a large-scale observational study, which was accepted and published by The Lancet Psychiatry. To our knowledge, our study was the first to study the impact of early clinical trajectory across multiple psychiatric diagnoses.