使用DEA方法论对初级医疗保健效率进行建模:在医疗保健区的经验分析
Silvia González-de-Julián1, David Vivas-Consuelo2, Isabel Barrachina-Martínez2
1Research Unit for Health Economics and Management, Universitat Politècnica de València, Valencia, 46022, Spain. silgonde@upv.edu.es.
BMC health services research
|August 24, 2024
概括
使用数据环境分析 (DEA) 评估初级医疗保健中心 (PHC) 显示效率因模型而异. 一些医疗保健中心的表现一直很好,而另一些医疗保健中心的表现仍然不高效,这突显了所选择的指标对医疗保健结果的影响.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 卫生经济学 卫生经济学
- 公共卫生 公共卫生
背景情况:
- 初级医疗保健效率对于专业护理,卫生系统成果和可持续性至关重要.
- 本研究评估了瓦伦西亚Clínico - La Malvarrosa健康区的初级医疗保健中心 (PHC) 的效率.
研究的目的:
- 开发和应用评估PHC效率的模型,并纳入健康结果.
- 分析不同效率评估方法对PHC绩效的影响.
主要方法:
- 数据包围分析 (DEA) 具有输出导向和可变的规模回报 (2015-2019).
- 输入:医疗/护理人员,药房费用. 输出:咨询,紧急情况,转诊,可避免的住院/死亡率,处方效率.
- 使用不同的输出组合 (活动,结果,两者) 开发了三种模型,并与/没有外源变量 (年龄,案例组合) 进行分析.
主要成果:
- 根据使用的DEA模型,效率得分有很大差异.
- 一些PHC始终表现出高效率,而另一些则持续低效.
- 包括医疗保健活动成果在内,提高了效率得分,提高了高效的医疗保健中心的数量,但总体得分随着时间的推移而下降.
结论:
- DEA有效地区分高效和低效的PHC,尽管分数根据评估模型有所不同.
- 评估视角的选择 (活动,结果或两者兼而有之) 显著影响效率结果.
- 为了进行有意义的效率分析,需要明确确定PHC目标.
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