Text-Prompted Cohort Retrieval: Leveraging Generative Healthcare Models for Precision Population Health Management

For population health managers and care management teams, segmenting high-risk patients into cohorts based on their clinical characteristics and history is desirable.

This segmentation not only allows for a better understanding of risk patterns within an individual patient, it also contextualizes these patterns across the broader patient population. Insights from the segmentation could pave the way for crafting intervention strategies tailored to address the nuances of the population.

Using John Snow Lab’s Generative Healthcare models, the ClosedLoop platform enables users to retrieve cohorts using free-text prompts. Examples include: “Which patients are in the top 5% of risk for an unplanned admission and have chronic kidney disease of stage 3 or higher?” or “Which patients are in the top 5% risk for an admission, older than 72, and have not undergone an annual wellness checkup?”


About the speaker

Shay Sayed

Principle Solutions Engineer at ClosedLoop

Shay Sayed is the Principal Solutions Engineer at ClosedLoop, where he collaborates with health systems and payers to prototype analytical solutions that improve both clinical and administrative processes.

Prior to this, he worked in the high-fashion space as well as the finance industry, incorporating machine learning to innovative products.

Outside the office, he enjoys reading about the history of US healthcare and listening to behavioral science podcasts.




Online Event: October 3-5, 2023




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