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).
The Hol Picture
Our Insights on Real-World Evidence and Behavioral Health
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.
I was honored to take the stage at the National Medical Research Council’s Awards Ceremony and Research Symposium in Singapore recently to speak about ongoing challenges in behavioral health, and the momentum we have in the market to address these challenges.
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.
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.
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.”