Automated delineation of hospital service areas as a new tool for health care planning.
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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.
SUBMITTER: Haynes AG
PROVIDER: S-EPMC7240760 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.
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