  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.
