Know Thy Patient: AI/ML-Driven Clustering of Diabetes/Hypertension Populations

In Dallas County’s safety-net population, an AI/machine learning-driven unsupervised clustering algorithm identifies clusters of diabetic and hypertensive patients with a combination of social and clinical risk factors associated with suboptimal quality of care (e.g., inadequate of Hemoglobin A1C monitoring) and poor disease control. Clusters analyses uncover underlying, actionable risk drivers such as criminal justice involvement and immigration concerns that require innovative, culturally-responsive approaches for a sustainable engagement of these vulnerable populations into effective preventive care. Additional in-depth analyses identify missed and potential opportunities for care engagement that inform innovative workflow modifications leveraging traditional (e.g., EHR-based standing orders) and nontraditional (e.g., telehealth modalities and mobile units) approaches to effectively engage and support these vulnerable populations and improve health quality, outcomes and equity countywide. The data sets and analytical approaches are scalable and replicable to other vulnerable populations nationwide.