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Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

Jiali Yan¹, Kristin A Linn², Brian W Powers^3,4,5,6

¹Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Journal of General Internal Medicine

|December 14, 2018

Summary

This summary is machine-generated.

Machine learning effectively segments high-cost patients into clinically distinct subgroups. Density-based clustering with OPTICS best identified these groups, revealing significant differences in healthcare utilization and spending.

Keywords:

high-cost patients machine learning patient segmentation

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Area of Science:

Health Informatics
Machine Learning in Healthcare
Health Services Research

Background:

Improving care value for high-cost patients requires targeted strategies.
Identifying clinically distinct subgroups is crucial for effective care management.

Purpose of the Study:

Evaluate machine learning algorithms for subgroup identification in high-cost patients.
Compare the clinical distinctiveness and utilization metric differences of identified subgroups.

Main Methods:

Applied connectivity-based (agglomerative hierarchical), centroid-based (k-medoids), and density-based (OPTICS) clustering.
Utilized a clinical and administrative dataset from Medicare Advantage patients in the top spending decile.
Assessed subgroup clinical distinctiveness and differences in utilization and spending using post hoc discriminative models.

Main Results:

Clustering yielded 8 (connectivity), 5 (centroid), and 10 (density-based) subgroups.
Density-based clustering (OPTICS) identified the most clinically distinct subgroups.
Subgroups from density-based clustering exhibited the greatest variance in utilization and spending.

Conclusions:

Machine learning can segment high-cost populations into clinically meaningful subgroups.
Density-based clustering with OPTICS demonstrates superior performance for this task.
Identified subgroups show significant differences in healthcare utilization and spending patterns.