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

• Published in: JMIR research protocols Vol. 13; p. e56049
• Main Authors: Lee, Seung-Yup, Hayes, Leslie W, Ozaydin, Bunyamin, Howard, Steven, Garretson, Alison M, Bradley, Heather M, Land, Andrew M, DeLaney, Erin W, Pritchett, Amy O, Furr, Amanda L, Allgood, Ashleigh, Wyatt, Matthew C, Hall, Allyson G, Banaszak-Holl, Jane C
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 25.09.2024
• Subjects: Case Management; Decision Support Systems, Clinical; Decision Support Techniques; Diabetes Mellitus - epidemiology; Diabetes Mellitus - therapy; Female; Humans; Machine Learning; Male; Middle Aged; Protocol; Social Determinants of Health; United States - epidemiology; United States; disparities; case mix; data warehouse; social determinants of health; predictive analytics; decision support; social work; case management; health disparities; clinical decision support; tertiary care; case manager; chronic disease management; diabetes; health care system
• ISSN: 1929-0748; 1929-0748
• DOI: 10.2196/56049

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. 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. 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. 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). 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. DERR1-10.2196/56049.