Suppr超能文献

电子审核与反馈的系统评价:干预效果及行为改变理论的应用

A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory.

作者信息

Tuti Timothy, Nzinga Jacinta, Njoroge Martin, Brown Benjamin, Peek Niels, English Mike, Paton Chris, van der Veer Sabine N

机构信息

KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.

Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.

出版信息

Implement Sci. 2017 May 12;12(1):61. doi: 10.1186/s13012-017-0590-z.

Abstract

BACKGROUND

Audit and feedback is a common intervention for supporting clinical behaviour change. Increasingly, health data are available in electronic format. Yet, little is known regarding if and how electronic audit and feedback (e-A&F) improves quality of care in practice.

OBJECTIVE

The study aimed to assess the effectiveness of e-A&F interventions in a primary care and hospital context and to identify theoretical mechanisms of behaviour change underlying these interventions.

METHODS

In August 2016, we searched five electronic databases, including MEDLINE and EMBASE via Ovid, and the Cochrane Central Register of Controlled Trials for published randomised controlled trials. We included studies that evaluated e-A&F interventions, defined as a summary of clinical performance delivered through an interactive computer interface to healthcare providers. Data on feedback characteristics, underlying theoretical domains, effect size and risk of bias were extracted by two independent review authors, who determined the domains within the Theoretical Domains Framework (TDF). We performed a meta-analysis of e-A&F effectiveness, and a narrative analysis of the nature and patterns of TDF domains and potential links with the intervention effect.

RESULTS

We included seven studies comprising of 81,700 patients being cared for by 329 healthcare professionals/primary care facilities. Given the extremely high heterogeneity of the e-A&F interventions and five studies having a medium or high risk of bias, the average effect was deemed unreliable. Only two studies explicitly used theory to guide intervention design. The most frequent theoretical domains targeted by the e-A&F interventions included 'knowledge', 'social influences', 'goals' and 'behaviour regulation', with each intervention targeting a combination of at least three. None of the interventions addressed the domains 'social/professional role and identity' or 'emotion'. Analyses identified the number of different domains coded in control arm to have the biggest role in heterogeneity in e-A&F effect size.

CONCLUSIONS

Given the high heterogeneity of identified studies, the effects of e-A&F were found to be highly variable. Additionally, e-A&F interventions tend to implicitly target only a fraction of known theoretical domains, even after omitting domains presumed not to be linked to e-A&F. Also, little evaluation of comparative effectiveness across trial arms was conducted. Future research should seek to further unpack the theoretical domains essential for effective e-A&F in order to better support strategic individual and team goals.

摘要

背景

审核与反馈是支持临床行为改变的常见干预措施。越来越多的健康数据以电子形式存在。然而,关于电子审核与反馈(e-A&F)是否以及如何在实际中提高医疗质量,人们知之甚少。

目的

本研究旨在评估e-A&F干预措施在初级保健和医院环境中的有效性,并确定这些干预措施背后行为改变的理论机制。

方法

2016年8月,我们检索了五个电子数据库,包括通过Ovid检索的MEDLINE和EMBASE,以及Cochrane对照试验中心注册库,以查找已发表的随机对照试验。我们纳入了评估e-A&F干预措施的研究,e-A&F干预措施定义为通过交互式计算机界面向医疗服务提供者提供的临床绩效总结。两名独立的综述作者提取了关于反馈特征、潜在理论领域、效应大小和偏倚风险的数据,他们确定了理论领域框架(TDF)中的领域。我们对e-A&F的有效性进行了荟萃分析,并对TDF领域的性质和模式以及与干预效果的潜在联系进行了叙述性分析。

结果

我们纳入了七项研究,涉及由329名医疗专业人员/初级保健机构护理的81,700名患者。鉴于e-A&F干预措施的异质性极高,且五项研究存在中度或高度偏倚风险,平均效应被认为不可靠。只有两项研究明确使用理论来指导干预设计。e-A&F干预措施最常针对的理论领域包括“知识”、“社会影响”、“目标”和“行为调节”,每项干预措施至少针对三个领域的组合。没有一项干预措施涉及“社会/职业角色和身份”或“情感”领域。分析发现,对照组中编码的不同领域数量在e-A&F效应大小的异质性中起最大作用。

结论

鉴于已确定研究的高度异质性,发现e-A&F的效果差异很大。此外,即使在排除假定与e-A&F无关的领域后,e-A&F干预措施往往只隐含地针对已知理论领域的一部分。而且,对各试验组之间的比较有效性评估很少。未来的研究应寻求进一步剖析有效e-A&F所需的理论领域,以便更好地支持个人和团队的战略目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2fd/5427645/50de39c2d972/13012_2017_590_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验