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

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.

Keywords: case management; case manager; case mix; chronic disease management; clinical decision support; data warehouse; decision support; diabetes; disparities; health care system; health disparities; predictive analytics; social determinants of health; social work; tertiary care.

Figure 1

Study framework.

Figure 2

Data items used to build the proactive risk assessment decision support model. ADI: Area Deprivation Index; ED: emergency department; eGFR: estimated glomerular filtration rate; HbA1c: hemoglobin A1c; ICD: International Classification of Diseases; PRAPARE: Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences; SVI: Social Vulnerability Index; uACR: urine albumin-creatinine ratio.

Figure 3

Diabetes-related posthospitalization emergency department (ED) visit frequency.

Figure 4

A tree form example. HbA1c: hemoglobin A1c.

Figure 5

Sequential mixed methods evaluation of the PRADS for Stage 2. 3P-MDM: three-phase modified Delphi method; AUC: are under the curve; PRADS: proactive risk assessment decision support.