Automated delineation of hospital service areas as a new tool for health care planning

Abstract

Objective: To develop an automated, reproducible method for delineating hospital service areas (HSAs).

Data sources/setting: Discharge data from all Swiss acute care hospitals for the years 2013 to 2016.

Study design: We derived HSAs and hospital referral regions for Switzerland using a newly developed flow-based, automated, objective, and reproducible method using all discharge data. We compared our method to the classical, partially subjective approach used to delineate the Swiss Health Care Atlas by delineating four sets of intervention-specific HSAs.

Principal findings: Based on 4 105 885 discharges, the fully automated method delineated 63 HSAs. Comparison with existing HSAs reveals good overlap and comparable measures of health utilization between the methods and shows that in the Swiss setting, our method outperforms a cluster-based approach to defining HSAs. While the classical method potentially takes an entire day to delineate the regions, our method took approximately 10 minutes.

Conclusions: Hospital service areas are used to analyze differences in use of health care that may indicate underuse and overuse. Our new, fully automated, objective, and reproducible method provides a useful tool for hospital services researchers that will enable them to delineate and update patient-flow-based HSAs.

Keywords: Mapcurve; Switzerland; hospital referral regions.

Figure 1

The four sets of hospital service areas (HSAs) delineated by the classical method and the automated methods (HSAr and the two clustering‐based methods). The filled‐clustering method was only necessary for the spinal HSAs and is thus not shown for the first three intervention sets [Color figure can be viewed at http://www.wileyonlinelibrary.com]

Figure 2

Localization index, number of interventions, net patient flow, market share index, and population (in 10 000s; rows) for each hospital service area (HSA) in the four sets of HSAs delineated by the classical and automated methods (HSAr and the two clustering‐based methods). Horizontal lines in the box plots represent medians. The filled‐clustering‐based method (“Filled” for brevity) was only necessary for the spinal HSAs and is thus not shown for the first three intervention sets [Color figure can be viewed at http://www.wileyonlinelibrary.com]