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313 Data-Driven Evaluation of Community Health Worker Program

Published online by Cambridge University Press:  03 April 2024

Roselyne Tchoua
Affiliation:
DePaul University
Kate Karam
Affiliation:
DePaul University
Kelly McCabe
Affiliation:
DePaul University
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Abstract

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OBJECTIVES/GOALS: Thiswork is an evidential study that demonstrates the positive impactof integrating Community Health Workers (CHWs) and SocialDeterminants of Health on an important health outcome, notably in decreasing the 30-day unplanned hospital ED readmissions at Sinai Health Systems. METHODS/STUDY POPULATION: Using datafrom the Sinai Urban Health Institute (SUHI), we compare predictingthe readmissions of patients with and without data pertainingto Social Determinants of Health (SDoH). We thoroughly describe the data cleaning and data pre-processing, done in collaboration with experts in community health. We use a fundamental and ubiquitous classifier in Random Forest for its feature characterization capability in order to translate models results into insights and recommendations for the CHW program. RESULTS/ANTICIPATED RESULTS: We show that when patients are simply engaged byCHWs, regardless of the content of those conversations, we canincrease the predictive accuracy of our classifier by 5%. We usethis result to make recommendations for improving patient careand discuss limitations and future work. Importantly our workpoints directly to the human connection between patients andCHWs as an important feature in the readmission rate. DISCUSSION/SIGNIFICANCE: Our work shows that the predictive capabilities of the classifier increases with CHW logs and SDoH survey data, highlighting the benefit of collecting this information. This is the first step in early identification of such patients so that CHWs are focusing on and providing resources to patients who will most benefit from the program.

Type
Informatics and Data Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science

Footnotes

The online version of this abstract has been updated since original publication. A notice detailing the change has been published at https://doi.org/10.1017/cts.2024.522.