Centre for Rheumatic Diseases, King's College London, London, UK.
Department of Psychology, Institute of Psychiatry, King's College London, London, UK.
BMJ Open. 2024 Nov 2;14(11):e083620. doi: 10.1136/bmjopen-2023-083620.
Performance visualisation tools are increasingly being applied in healthcare to enhance decision-making and improve quality of care. However, there is a lack of comprehensive synthesis of their overall effectiveness and the contextual factors that influence their success in different clinical settings. This study aims to provide a broad synthesis of visualisation interventions not limited to a specific department.
Systematic review.
MEDLINE and Embase were searched until December 2022.
Randomised controlled trials (RCTs) and observational studies in English involving a visualisation intervention, either alone or as a core intervention, that reported quantitative outcomes including process and outcome indicators.
Data on study characteristics, intervention characteristics, outcome measures and results were extracted. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach, and risk of bias was evaluated with Risk of Bias 2 for RCTs and Risk of Bias in Non-randomised Studies - of Interventions for non-randomised studies. RESULTS : Of the 12 studies included, 2 were RCTs and 10 were observational studies, including 1 before-after study and 1 interrupted time series study. Five studies (42%) were conducted in teaching hospital settings. Compared with the control group or baseline, 10 studies reported a statistically significant change in at least one of their outcome measures. A majority of the studies reported a positive impact, including prescription adherence (6/10), screening tests (3/10) and monitoring (3/10). Visualisation tool factors like type, clinical setting, workflow integration and clinician engagement, may have some influence on the effectiveness of the intervention, but no reliable evidence was identified.
Performance visualisation tools have the potential to improve clinical performance indicators. More studies with standardised outcome measures and integrating qualitative methods are needed to understand the contextual factors that influence the effectiveness of these interventions.
性能可视化工具在医疗保健领域中的应用越来越广泛,以增强决策能力并提高护理质量。然而,目前缺乏对其整体效果的综合分析,也缺乏对影响其在不同临床环境中成功的背景因素的综合分析。本研究旨在广泛综合各种可视化干预措施,不限于特定科室。
系统综述。
截至 2022 年 12 月,检索了 MEDLINE 和 Embase 数据库。
英语发表的随机对照试验(RCT)和观察性研究,涉及可视化干预措施,无论是单独干预还是核心干预,报告了包括过程和结果指标在内的定量结果。
提取研究特征、干预特征、结果测量和结果数据。使用推荐评估、制定与评估(GRADE)方法评估证据质量,并使用 RCT 的偏倚风险 2 工具和非随机干预研究的偏倚风险(ROBINS-I)工具评估非随机研究的偏倚风险。结果:纳入的 12 项研究中,有 2 项 RCT 和 10 项观察性研究,包括 1 项前后对照研究和 1 项中断时间序列研究。有 5 项研究(42%)在教学医院进行。与对照组或基线相比,有 10 项研究报告了至少一项结果测量指标的统计学显著变化。大多数研究报告了积极的影响,包括处方依从性(6/10)、筛查测试(3/10)和监测(3/10)。可视化工具的因素,如类型、临床环境、工作流程集成和临床医生的参与度,可能对干预的有效性有一定影响,但没有可靠的证据。
性能可视化工具有可能改善临床绩效指标。需要更多具有标准化结果测量和整合定性方法的研究,以了解影响这些干预措施有效性的背景因素。