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医疗保健中绩效薪酬的有效性:一项荟萃分析及结果差异探究。

The effectiveness of payment for performance in health care: A meta-analysis and exploration of variation in outcomes.

作者信息

Ogundeji Yewande Kofoworola, Bland John Martin, Sheldon Trevor Andrew

机构信息

Department of Health Sciences, University of York, York, YO10 5DD, UK; Health Strategy and Delivery Foundation (HSDF), 1980 Wikki Spring Street, Maitama, Abuja, Nigeria.

Department of Health Sciences, University of York, York, YO10 5DD, UK.

出版信息

Health Policy. 2016 Oct;120(10):1141-1150. doi: 10.1016/j.healthpol.2016.09.002. Epub 2016 Sep 5.

Abstract

BACKGROUND

Pay for performance (P4P) incentive schemes are increasingly used world-wide to improve health system performance but results of evaluations vary considerably. A systematic analysis of this variation in the effects of P4P schemes is needed.

METHODS

Evaluations of P4P schemes from any country were identified by searching for and updating systematic reviews of P4P schemes in health care in four bibliographic databases. Outcomes using different measures of effect were converted into standardized effect sizes (standardized mean difference, SMD) and each study was categorized as to whether or not it found a positive effect. Subgroup analysis, meta-regression and multilevel logistic regression were used to investigate factors explaining heterogeneity. Random-effects models were used because they take into account heterogeneity likely to be due to differences between studies rather than just chance. Sensitivity analysis was used to test the effect of different assumptions.

FINDINGS

96 primary studies were identified; 37 were included in the meta-analysis and meta-regression and all 96 in the logistic regression. The proportion of observed variation in study results that can be explained by true heterogeneity (I) was 99.9%. Estimates of effect of P4P schemes were lower in evaluations using randomized controlled trials (SMD=0.08; 95% CI: 0.01-0.15) compared to no controls (0.15; 95% CI: 0.09-0.21), and lower for those measuring outcomes (e.g., smoking cessation) (SMD=0.0; 95% CI: -0.01 to 0.01) compared to process measures (e.g., giving cessation advice) (0.18; 95% CI: 0.06-0.31). Adjusting for other design features and the evaluation method, the odds of showing a positive effect was three times higher for schemes with larger incentives (>5% of salary/usual budget) (OR=3.38; 95% CI: 1.07-10.64). There were non-statistically significant increases in the odds of success if the incentive is paid to individuals (as opposed to groups) (OR=2.0; 95% CI: 0.62-6.56) and if there is a lower perceived risk of not earning the incentive (OR=2.9; 95% CI: 0.78-10.83). Schemes evaluated using less rigorous designs were 24 times more likely to have positive estimates of effect than those using randomized controlled trials (OR=24; 95% CI: 6.3-92.8).

INTERPRETATION

Estimates of the effectiveness of incentive schemes on health outcomes are probably inflated due to poorly designed evaluations and a focus on process measures rather than health outcomes. Larger incentives and reducing the perceived risk of non-payment may increase the effect of these schemes on provider behavior.

摘要

背景

按绩效付费(P4P)激励计划在全球范围内越来越多地用于改善卫生系统绩效,但评估结果差异很大。需要对P4P计划效果的这种差异进行系统分析。

方法

通过在四个书目数据库中搜索和更新医疗保健领域P4P计划的系统评价,确定来自任何国家的P4P计划评估。使用不同效果测量指标的结果被转换为标准化效应大小(标准化均数差,SMD),每项研究根据是否发现积极效果进行分类。采用亚组分析、元回归和多水平逻辑回归来研究解释异质性的因素。使用随机效应模型,因为它们考虑了可能由于研究间差异而非仅仅是偶然导致的异质性。敏感性分析用于检验不同假设的效果。

结果

确定了96项主要研究;37项纳入了荟萃分析和元回归,96项全部纳入逻辑回归。研究结果中可由真正异质性(I)解释的观察变异比例为99.9%。与无对照试验(SMD = 0.15;95% CI:0.09 - 0.21)相比,在使用随机对照试验的评估中,P4P计划的效果估计值较低(SMD = 0.08;95% CI:0.01 - 0.15),与过程指标(如提供戒烟建议)(SMD = 0.18;95% CI:0.06 - 0.31)相比,测量结果(如戒烟)的评估效果估计值较低(SMD = 0.0;95% CI: - 0.01至0.01)。在调整其他设计特征和评估方法后,激励较大(>工资/通常预算的5%)的计划显示出积极效果的几率高出三倍(OR = 3.38;95% CI:1.07 - 10.64)。如果激励支付给个人(而非团体)(OR = 2.0;95% CI:0.62 - 6.56)以及如果未获得激励的感知风险较低(OR = 2.9;95% CI:0.78 - 10.83),成功几率有非统计学意义的增加。使用设计不太严谨的评估的计划比使用随机对照试验的计划有积极效果估计值的可能性高24倍(OR = 24;95% CI:6.3 - 92.8)。

解读

由于评估设计不佳以及关注过程指标而非健康结果,激励计划对健康结果有效性的估计可能被夸大。更大的激励以及降低未支付的感知风险可能会增加这些计划对提供者行为的影响。

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