Journal of general internal medicine
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Observational Study
Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.
There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. ⋯ We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.
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Abstract
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Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. ⋯ Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.
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Michigan expanded Medicaid under the Affordable Care Act (Healthy Michigan Plan [HMP]) to improve the health of low-income residents and the state's economy. ⋯ Many low-income HMP enrollees reported improved health, ability to work, and job seeking after obtaining health insurance through Medicaid expansion.