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Comparing methods of grouping hospitals.

Jordan Everson1, John M Hollingsworth2,3, Julia Adler-Milstein4

  • 1Department of Health Policy, Vanderbilt University School of Medicine, Nashville, Tennessee.

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Community detection algorithms (CDAs) provide a superior method for grouping hospitals, demonstrating higher distinctiveness and generalizability than traditional approaches like HRRs, MSAs, and CBSAs.

Keywords:
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Area of Science:

  • Health Services Research
  • Network Science
  • Data Analytics

Background:

  • Defining hospital groups is crucial for analyzing healthcare delivery and patient flow.
  • Existing methods like Hospital Referral Regions (HRRs), Metropolitan Statistical Areas (MSAs), and Core-Based Statistical Areas (CBSAs) have limitations in accurately reflecting patient-sharing dynamics.
  • Network analysis offers a novel approach to understanding inter-hospital relationships based on shared patient volume.

Purpose of the Study:

  • To compare the performance of widely used hospital grouping methods (HRRs, MSAs, CBSAs) against a new network analysis approach using Community Detection Algorithms (CDAs).
  • To evaluate the distinctiveness, reliability, and generalizability of different hospital grouping strategies.

Main Methods:

  • Utilized data from the American Hospital Association Annual Survey, Census Bureau, Dartmouth Atlas, and Medicare on interhospital patient travel (2014).
  • Assessed measurement properties including distinctiveness (modularity), reliability (over time and to alternative specifications), and generalizability.
  • Compared four grouping methods: HRRs, MSAs, CBSAs, and CDAs.

Main Results:

  • CDA-defined hospital groups exhibited the highest distinctiveness (modularity=0.86) compared to HRRs (0.75), MSAs (0.83), and CBSAs (0.72).
  • CDAs demonstrated superior generalizability and reliability to alternative specifications.
  • CDAs showed slightly lower reliability over time (NMI=0.93) compared to MSAs and CBSAs.

Conclusions:

  • Community Detection Algorithms offer a highly valid, reliable, and generalizable method for defining hospital groups.
  • CDA-based groupings may provide a more accurate representation of hospital behaviors and dynamics than current widely used approaches.
  • Metrics such as modularity, shared information, and inclusivity are valuable for evaluating provider grouping methods.