Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information

Brittany M Krzyzanowski, Steven Manson

Research output: Contribution to journalArticlepeer-review

Abstract

This article addresses the challenge of sharing finer scale protected health information (PHI) while maintaining patient privacy by using regionalization to create higher resolution Health Insurance Portability and Accountability Act (HIPAA)-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), and self-organizing map (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intraunit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures, whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP, which outperformed on most measures. The SOM variant of max-p-regions (MSOM) could also be recommended, because it provided the highest resolution while maintaining suitable performance on all other measures.

Original languageEnglish (US)
JournalAnnals of the American Association of Geographers
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Keywords

  • HIPAA
  • patient data
  • patient privacy
  • regionalization
  • self-organizing maps

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