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Cited by 5Machine Learning in HealthcareArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study

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Lee, Seung-Yup, Hayes, Leslie W, Ozaydin, Bunyamin, Howard, Steven, Garretson, Alison M, Bradley, Heather M, Land, Andrew M, DeLaney, Erin W +6 more·2024·JMIR Research Protocols
DOI 10.2196/56049PMC PMC11464948

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Abstract

BACKGROUND: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes. OBJECTIVE: This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system. METHODS: Our study setting is a large, tertiary care academic medical center in the Deep South of the United States, where SDoH contribute to disparities in diabetes-specific hospitalizations and emergency department (ED) visits. This project will develop an interpretable artificial intelligence model for a population with diabetes using SDoH and clinical data to identify which posthospitalization cases have a higher likelihood of subsequent ED use. The electronic health record data collected for the study include demographics, SDoH, comorbidities, hospitalization-related factors, laboratory test results, and medication use to predict posthospitalization ED visits. Subsequently, a mixed methods approach will be used to validate prediction outcomes and develop an implementation strategy from insights into patient outcomes from case managers, clinicians, and quality and patient safety experts. RESULTS: As of December 2023, we had abstracted data on 174,871 inpatient encounters between January 2018 and September 2023, involving 89,355 unique inpatients meeting inclusion criteria. Both clinical and SDoH data items were included for these patient encounters. In total, 85% of the inpatient visits (N=148,640) will be used for training (learning from the data) and the remaining 26,231 inpatient visits will be used for mixed-methods validation (testing). CONCLUSIONS: By integrating a critical suite of SDoH with clinical data related to diabetes, the proposed data-driven risk stratification model can enable individualized risk estimation and inform health professionals (eg, case managers) about the risk of patients' upcoming ED use. The prediction outcome could potentially automate case management referrals, helping to better prioritize services. By taking a mixed methods approach, we aim to align the model with the hospital's specific quality and patient safety considerations for the quality of patient care and the optimization of case management resource allocation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56049.

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Cited by

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Authors
Lee, S.-Y., Hayes, L. W., Ozaydin, B., Howard, S., Garretson, A. M., Bradley, H. M., Land, A. M., DeLaney, E. W., Pritchett, A. O., Furr, A. L., Allgood, A., Wyatt, M. C., Hall, A. G., & Banaszak-Holl, J. C.
Affiliations
  • University of Alabama at Birmingham (US)
  • Cooper Green Mercy Hospital (US)
Corresponding author
Lee, Seung-Yup
ORCID
Venue
JMIR Research Protocols
Publisher
JMIR Publications Inc.
Published
2024-09-25
Volume
13
Pages
e56049
ISSN
1929-0748 (ISSN-L)
DOI
10.2196/56049
PMC
PMC11464948
OpenAlex
W4400080633
Cited by
5
References
3
Citations by year
2026: 1 · 2025: 4
FWCI
0.74
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Research Protocols (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
social determinants of health, diabetes mellitus, case management, clinical decision support, risk stratification, emergency department utilization, post-hospital care, model interpretability, referral prioritization, health disparities, electronic health records, implementation strategies, forecast verification, resource allocation, hospital readmission, quality of care, patient safety, racial and ethnic disparities
MeSH terms
Machine Learning, Decision Support Techniques, Diabetes Mellitus, Female, Humans, Male, Middle Aged, United States, Case Management, Decision Support Systems, Clinical, Social Determinants of Health

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