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.

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.

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).

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). 

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.

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.

With so many types of real-world data available, it can be difficult to select the source that will meet your needs—that is, the data that will help you accurately and efficiently answer your research questions.

This week, we shared the exciting and humbling news that we’ve been named as part of Digital Health New York’s New York Digital Health 100 list, a recognition that highlights the most exciting and innovative digital health startups in the New York region. Co-Founder and CEO of Digital Health New York, Bunny Ellerin, describes the list as “an incredibly diverse, innovative and forward-thinking set of companies and leaders that are making an impact on the future of healthcare.”