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Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management:

Seung-Yup Lee1, Leslie W Hayes2, Bunyamin Ozaydin1

  • 1School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States.

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Summary

This study developed a data-driven system using clinical data and social determinants of health (SDoH) to prioritize diabetes patients for case management, aiming to reduce disparities and optimize resource use.

Keywords:
case managementcase managercase mixchronic disease managementclinical decision supportdata warehousedecision supportdiabetesdisparitieshealth care systemhealth disparitiespredictive analyticssocial determinants of healthsocial worktertiary care

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Public Health

Background:

  • Clinical factors and Social Determinants of Health (SDoH) integration in case management referrals can optimize resource allocation and reduce health outcome disparities in diabetes patients.
  • The Deep South region faces significant diabetes-related health disparities, particularly in hospitalizations and emergency department (ED) visits, exacerbated by SDoH.

Purpose of the Study:

  • To develop a data-driven decision-support system that integrates clinical factors and SDoH to prioritize patients for case management services.
  • To design a prediction validation and preimplementation assessment strategy using a mixed-methods approach to guide system implementation.

Main Methods:

  • Development of an interpretable artificial intelligence model using electronic health record data (demographics, SDoH, comorbidities, hospitalization factors, lab results, medications) to predict posthospitalization ED use in a diabetes population.
  • Utilizing a mixed-methods approach for prediction outcome validation and developing an implementation strategy informed by case managers, clinicians, and quality/patient safety experts.
  • Study setting: a large, tertiary care academic medical center in the Deep South, USA.

Main Results:

  • Data abstracted from 174,871 inpatient encounters (January 2018 - September 2023) involving 89,355 unique patients.
  • 85% of inpatient visits (N=148,640) allocated for model training, with the remaining 26,231 visits for mixed-methods validation.
  • Included both clinical and SDoH data items for patient encounters.

Conclusions:

  • The proposed data-driven risk stratification model integrates SDoH and clinical data to estimate individualized risk for upcoming ED use among diabetes patients.
  • The model can potentially automate case management referrals, improving prioritization of services and optimizing resource allocation.
  • A mixed-methods approach ensures alignment with hospital quality and patient safety considerations for enhanced patient care.