{"title":"管理实践对患者相对死亡率的影响:来自公立医院的证据。","authors":"Reza Salehnejad, Manhal Ali, Nathan C Proudlove","doi":"10.1177/09514848211068627","DOIUrl":null,"url":null,"abstract":"<p><p>A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.</p>","PeriodicalId":45801,"journal":{"name":"Health Services Management Research","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574893/pdf/","citationCount":"0","resultStr":"{\"title\":\"The impact of management practices on relative patient mortality: Evidence from public hospitals.\",\"authors\":\"Reza Salehnejad, Manhal Ali, Nathan C Proudlove\",\"doi\":\"10.1177/09514848211068627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.</p>\",\"PeriodicalId\":45801,\"journal\":{\"name\":\"Health Services Management Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574893/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Services Management Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09514848211068627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/2/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Services Management Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09514848211068627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/2/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
The impact of management practices on relative patient mortality: Evidence from public hospitals.
A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.
期刊介绍:
Health Services Management Research (HSMR) is an authoritative international peer-reviewed journal which publishes theoretically and empirically rigorous research on questions of enduring interest to health-care organizations and systems throughout the world. Examining the real issues confronting health services management, it provides an independent view and cutting edge evidence-based research to guide policy-making and management decision-making. HSMR aims to be a forum serving an international community of academics and researchers on the one hand and healthcare managers, executives, policymakers and clinicians and all health professionals on the other. HSMR wants to make a substantial contribution to both research and managerial practice, with particular emphasis placed on publishing studies which offer actionable findings and on promoting knowledge mobilisation toward theoretical advances.