‍

Psychiatric care is seen as less evidence-based and data-driven than other fields, largely due to the lack of data available to examine treatment and outcomes in routine clinical care. Measures are administered inconsistently, with different assessments existing across different sites, and valuable data contained in clinical notes is inaccessible without the assistance of technology implemented at scale.

Holmusk has developed novel natural language processing (NLP) models that extract and translate key information from clinical notes into structured, analyzable, and actionable insights. These NLP models are applied to unstructured data, and the resulting insights are made available in NeuroBlu, a powerful analytics tool built to explore the world’s richest real-world database for behavioral health. NeuroBlu NLP enhances clinical research by increasing data accessibility, data density, and cohort sizes.

Read the use case to learn about how NeuroBlu NLP generates analyzable data on symptomatology, such as the presence of anhedonia in patients with depressive disorders—thereby increasing cohort sizes by up to 25%.

{{cta}}

‍

Interested in learning more? Contact us to schedule a demo.