Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.

Abstract

Objectives: Adherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.

Methods: This study analyzed the 2021 Medical Expenditure Panel Survey and used a k-mode cluster analysis. Patient features analyzed were adherence to a select set of preventive measures from the ADA Standards of Medical Care (HbA1c test, foot examination, blood cholesterol test, dilated eye examination, and influenza vaccination) and some patient characteristics (age, gender, health insurance, insulin use, and diabetes-related complications).

Results: The study included 1,219 patients with self-reported diabetes, and the adherence rate to the ADA standards was 33.72%. Five distinct clusters emerged: (A) moderate-complexity, privately insured male; (B) moderate-complexity, publicly insured female; (C) low-complexity, privately insured female; (D) high-complexity, publicly insured female; (E) moderate-complexity, publicly insured male. Groups B, C, and E exhibited nonadherence.

Conclusions: Pharmacists can target publicly insured elderly (Groups B and E) and privately insured middle-aged females (Group C) for interventions. For instance, pharmacists may help patients in Groups B and E locate existing resources in their insurance program and remind those in Group C of the importance of adequate diabetes care.