Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization

TL;DR

This paper introduces an automated, data-driven network optimization method to delineate hospital service areas and referral regions, improving upon existing units by better capturing hospitalization patterns and local healthcare structures.

Contribution

The study presents a novel, scalable, and automated modularity optimization approach for defining healthcare regions, enhancing accuracy over traditional methods.

Findings

Method outperforms Dartmouth units in size and market structure balance

Optimizes localization of hospital visits within regions

Effective in capturing natural healthcare system structures

Abstract

Objective. To develop an automated, data-driven, and scale-flexible method to delineate HSAs and HRRs that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. Data Sources. The 2011 State Inpatient Database (SID) in Florida from the Healthcare Cost and Utilization Project (HCUP). Study Design. A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. Principal Findings. The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most importantly, local hospitalization. Conclusions. The new method is automated, scale-flexible, and effective in capturing the natural structure of healthcare system. It has great potential for applications in delineating other healthcare service areas or in larger geographic regions.