Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.
Department of Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.
Br J Clin Pharmacol. 2022 May;88(5):2035-2051. doi: 10.1111/bcp.15160. Epub 2021 Dec 15.
The aim of this scoping review is to summarize approaches and outcomes of clinical validation studies of clinical decision support systems (CDSSs) to support (part of) a medication review. A literature search was conducted in Embase and Medline. In total, 30 articles validating a CDSS were ultimately included. Most of the studies focused on detection of adverse drug events, potentially inappropriate medications and drug-related problems. We categorized the included articles in three groups: studies subjectively reviewing the clinical relevance of CDSS's output (21/30 studies) resulting in a positive predictive value (PPV) for clinical relevance of 4-80%; studies determining the relationship between alerts and actual events (10/30 studies) resulting in a PPV for actual events of 5-80%; and studies comparing output of CDSSs to chart/medication reviews in the whole study population (10/30 studies) resulting in a sensitivity of 28-85% and specificity of 42-75%. We found heterogeneity in the methods used and in the outcome measures. The validation studies did not report the use of a published CDSS validation strategy. To improve the effectiveness and uptake of CDSSs supporting a medication review, future research would benefit from a more systematic and comprehensive validation strategy.
本综述旨在总结支持（部分）药物审查的临床决策支持系统 (CDSS) 的临床验证研究的方法和结果。我们在 Embase 和 Medline 中进行了文献检索。最终纳入了 30 篇验证 CDSS 的文章。大多数研究集中于检测药物不良事件、潜在不适当药物和与药物相关的问题。我们将纳入的文章分为三组：主观审查 CDSS 输出的临床相关性的研究（21/30 篇研究），其临床相关性的阳性预测值（PPV）为 4-80%；确定警报与实际事件之间关系的研究（10/30 篇研究），其实际事件的 PPV 为 5-80%；以及比较整个研究人群中 CDSS 输出与图表/药物审查的研究（10/30 篇研究），其敏感性为 28-85%，特异性为 42-75%。我们发现使用的方法和结果衡量标准存在异质性。验证研究未报告使用已发布的 CDSS 验证策略。为了提高支持药物审查的 CDSS 的有效性和采用率，未来的研究将受益于更系统和全面的验证策略。
