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衡量2010 - 2015年巴勒斯坦公立医院的效率:两阶段数据包络分析方法的应用

Measuring the efficiency of Palestinian public hospitals during 2010-2015: an application of a two-stage DEA method.

作者信息

Sultan Wasim I M, Crispim José

机构信息

School of Economics and Management, University of Minho, 4710-057, Braga, Portugal.

, P.O. Box 198, Hebron, Palestine.

出版信息

BMC Health Serv Res. 2018 May 29;18(1):381. doi: 10.1186/s12913-018-3228-1.

DOI:10.1186/s12913-018-3228-1
PMID:29843732
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5975658/
Abstract

BACKGROUND

While health needs and expenditure in the Occupied Palestinian Territories (OPT) are growing, the international donations are declining and the economic situation is worsening. The purpose of this paper is twofold, to evaluate the productive efficiency of public hospitals in West Bank and to study contextual factors contributing to efficiency differences.

METHODS

This study examined technical efficiency among 11 public hospitals in West Bank from 2010 through 2015 targeting a total of 66 observations. Nationally representative data were extracted from the official annual health reports. We applied input-oriented Data Envelopment Analysis (DEA) models to estimate efficiency scores. To elaborate further on performance, we used Tobit regression to identify contextual factors whose impact on inefficient performance is statistically significant.

RESULTS

Despite the increase in efficiency mean scores by 4% from 2010 to 2015, findings show potential savings of 14.5% of resource consumption without reducing the volume of the provided services. The significant Tobit model showed four predictors explaining the inefficient performance of a hospital (p <  0.01) are: bed occupancy rate (BOR); the outpatient-inpatient ratio (OPIPR); hospital's size (SIZE); and the availability of primary healthcare centers within the hospital's catchment area (PRC). There is a strong effect of OPIPR on efficiency differences between hospitals: A one unit increase in OPIPR will lead a decrease of 19.7% in the predicted inefficiency level holding all other factors constant.

CONCLUSION

To date, no previous studies have examined the efficiency of public hospitals in the OPT. Our work identified their efficiency levels for potential improvements and the determinants of efficient performance. Based on the measurement of efficiency, the generated information may guide hospitals' managers, policymakers, and international donors improving the performance of the main national healthcare provider. The scope of this study is limited to public hospitals in West Bank. For a better understanding of the Palestinian market, further research on private hospitals and hospitals in Gaza Strip will be useful.

摘要

背景

尽管巴勒斯坦被占领土(OPT)的医疗需求和支出不断增长,但国际捐赠却在减少,经济形势也在恶化。本文的目的有两个,一是评估约旦河西岸公立医院的生产效率,二是研究导致效率差异的背景因素。

方法

本研究考察了2010年至2015年期间约旦河西岸11家公立医院的技术效率,共进行了66次观察。从官方年度健康报告中提取了具有全国代表性的数据。我们应用投入导向的数据包络分析(DEA)模型来估计效率得分。为了进一步阐述绩效,我们使用Tobit回归来识别对低效绩效有统计学显著影响的背景因素。

结果

尽管从2010年到2015年效率平均得分提高了4%,但研究结果表明,在不减少所提供服务数量的情况下,资源消耗可能节省14.5%。显著的Tobit模型显示,有四个预测因素可以解释医院的低效绩效(p < 0.01),分别是:床位占用率(BOR);门诊与住院比例(OPIPR);医院规模(SIZE);以及医院服务区域内初级医疗中心的可及性(PRC)。OPIPR对医院之间的效率差异有很强的影响:在其他因素不变的情况下,OPIPR每增加一个单位,预计的无效率水平将下降19.7%。

结论

迄今为止,之前没有研究考察过巴勒斯坦被占领土公立医院的效率。我们的研究确定了它们的效率水平,以便进行潜在的改进,并找出高效绩效的决定因素。基于效率的衡量,所生成的信息可以指导医院管理人员、政策制定者和国际捐助者提高主要国家医疗服务提供者的绩效。本研究的范围仅限于约旦河西岸的公立医院。为了更好地了解巴勒斯坦市场,对私立医院和加沙地带医院的进一步研究将很有帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/dc46eee76f98/12913_2018_3228_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/766c27aeed1d/12913_2018_3228_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/45079e7804fb/12913_2018_3228_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/3b57e7c80254/12913_2018_3228_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/2bb10040f1ec/12913_2018_3228_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/dc46eee76f98/12913_2018_3228_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/766c27aeed1d/12913_2018_3228_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/45079e7804fb/12913_2018_3228_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/3b57e7c80254/12913_2018_3228_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/2bb10040f1ec/12913_2018_3228_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e7b/5975658/dc46eee76f98/12913_2018_3228_Fig5_HTML.jpg

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