Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data

Excerpt

Health systems are increasingly adopting intensive primary care and care coordination programs to improve outcomes for high-need, high-cost (HNHC) patients, the 5% of patients who account for over 50% of health care costs.¹ However, research on such programs has shown mixed results, improving patient satisfaction but having limited impact on quality of life, illness control, and need for acute care services.^{2, 3} As a group, HNHC patients are defined based on their utilization of care, rather than their clinical conditions. Yet, to better manage HNHC patients, clinicians need to match patients to care models tailored to their clinical conditions.⁴ Here, we utilized an open-source, machine learning method to describe different subgroups of HNHC patients based on their clinical characteristics for an urban Medicaid population in the Mount Sinai Health System (MSHS). …