Department of Healthcare Management, Berlin University of Technology, Straße des 17. Juni 135, 10623 Berlin, Germany.
Institute for Entrepreneurship and Business Development, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
PLoS One. 2018 Sep 6;13(9):e0203017. doi: 10.1371/journal.pone.0203017. eCollection 2018.
We specify a Bayesian, geoadditive Stochastic Frontier Analysis (SFA) model to assess hospital performance along the dimensions of resources and quality of stroke care in German hospitals. With 1,100 annual observations and data from 2006 to 2013 and risk-adjusted patient volume as output, we introduce a production function that captures quality, resource inputs, hospital inefficiency determinants and spatial patterns of inefficiencies. With high relevance for hospital management and health system regulators, we identify performance improvement mechanisms by considering marginal effects for the average hospital. Specialization and certification can substantially reduce mortality. Regional and hospital-level concentration can improve quality and resource efficiency. Finally, our results demonstrate a trade-off between quality improvement and resource reduction and substantial regional variation in efficiency.
我们指定了一个贝叶斯、地理附加随机前沿分析（SFA）模型，以评估德国医院在资源和中风护理质量方面的绩效。我们使用了 2006 年至 2013 年的 1100 个年度观测值和数据，以及风险调整后的患者量作为产出，引入了一个生产函数，该函数捕获了质量、资源投入、医院效率决定因素和效率的空间模式。对于医院管理和卫生系统监管者来说，我们通过考虑平均医院的边际效应来确定绩效改进机制。专业化和认证可以显著降低死亡率。区域和医院层面的集中可以提高质量和资源效率。最后，我们的结果表明，在质量改进和资源减少之间存在权衡，并且效率在区域上存在很大差异。
