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Journal of General Internal Medicine

, Volume 34, Issue 2, pp 211–217 | Cite as

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations

  • Jiali Yan
  • Kristin A. Linn
  • Brian W. Powers
  • Jingsan Zhu
  • Sachin H. Jain
  • Jennifer L. Kowalski
  • Amol S. NavatheEmail author
Original Research

Abstract

Background

Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.

Objective

To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients.

Design

We applied three different clustering algorithms—connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm—to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics.

Participants

Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154).

Main Measures

Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures.

Key Results

Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering.

Conclusions

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.

KEY WORDS

high-cost patients machine learning patient segmentation 

Notes

Funding Information

This study was supported by a grant from the Anthem Public Policy Institute and, in part, under a grant with the Pennsylvania Department of Health.

Compliance with Ethical Standards

This study was approved by the Institutional Review Board of the University of Pennsylvania.

Conflict of Interest

Dr. Navathe reports that he has received grant support from Hawaii Medical Service Association, Anthem Public Policy Institute, and Oscar Health; personal fees from Navvis and Co, Navigant Inc., Lynx Medical, Indegene Inc., Agathos, Inc, and Sutherland Global Services; personal fees and equity from NavaHealth; serves on the board without compensation for Integrated Services, Inc., speaking fees from the Cleveland Clinic, and honoraria from Elsevier Press. Dr. Linn reports that she has received grant support from Hawaii Medical Service Association. Dr. Jain reports employment by Anthem, Inc.; stock ownership in Anthem, Inc., and honoraria from Elsevier Press. Ms. Kowalski reports employment by Anthem, Inc. and stock ownership in Anthem, Inc. and Amazon. Dr. Powers reports employment by Anthem, Inc. All other authors declare no conflicts of interest.

Disclaimer

The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.

Supplementary material

11606_2018_4760_MOESM1_ESM.docx (640 kb)
ESM 1 (DOCX 639 kb)

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Copyright information

© Society of General Internal Medicine 2018

Authors and Affiliations

  • Jiali Yan
    • 1
  • Kristin A. Linn
    • 2
  • Brian W. Powers
    • 3
    • 4
    • 5
    • 6
  • Jingsan Zhu
    • 7
  • Sachin H. Jain
    • 5
  • Jennifer L. Kowalski
    • 8
  • Amol S. Navathe
    • 7
    • 9
    Email author
  1. 1.Department of MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  2. 2.Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  3. 3.Department of MedicineBrigham and Women’s HospitalBostonUSA
  4. 4.Department of Population MedicineHarvard Medical School/Harvard Pilgrim Health Care InstituteBostonUSA
  5. 5.CareMore Health SystemCerritosUSA
  6. 6.Atrius HealthBostonUSA
  7. 7.Department of Medical Ethics and Health PolicyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaUSA
  8. 8.Anthem Public Policy InstituteWashingtonUSA
  9. 9.Corporal Michael J. Cresencz VA Medical CenterPhiladelphiaUSA

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