School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Level 3, Building 503, 85 Park Road, Grafton, Auckland 1023, New Zealand.
Int J Qual Health Care. 2024 Jan 13;36(1). doi: 10.1093/intqhc/mzad114.
Identifying the risk and predicting complaints and misconduct against health practitioners are essential for healthcare regulators to implement early interventions and develop long-term prevention strategies to improve professional practice and enhance patient safety. This scoping review aims to map out existing literature on the risk identification and prediction of complaints and misconduct against health practitioners. This scoping review followed Arksey and O'Malley's five-stage methodological framework. A comprehensive literature search was conducted on MEDLINE, EMBASE, and CINAHL databases and finished on the same day (6 September 2021). Articles meeting the eligibility criteria were charted and descriptively analysed through a narrative analysis method. The initial search generated 5473 articles. After the identification, screening, and inclusion process, 81 eligible studies were included for data charting. Three key themes were reported: methods used for identifying risk factors and predictors of the complaints and misconduct, synthesis of identified risk factors and predictors in eligible studies, and predictive tools developed for complaints and misconduct against health practitioners. The findings reveal that risk identification and prediction of complaints and misconduct are complex issues influenced by multiple factors, exhibiting non-linear patterns and being context specific. Further efforts are needed to understand the characteristics and interactions of risk factors, develop systematic risk prediction tools, and facilitate the application in the regulatory environment.
识别医疗从业者投诉和不当行为的风险并对其进行预测，对于医疗保健监管机构实施早期干预和制定长期预防策略以改善专业实践和提高患者安全至关重要。本范围综述旨在绘制现有关于医疗从业者投诉和不当行为风险识别和预测的文献。本范围综述遵循 Arksey 和 O'Malley 的五阶段方法框架。在 MEDLINE、EMBASE 和 CINAHL 数据库上进行了全面的文献检索，并于同一天（2021 年 9 月 6 日）完成。符合入选标准的文章通过叙述性分析方法进行图表制作和描述性分析。最初的搜索产生了 5473 篇文章。经过识别、筛选和纳入过程，有 81 篇符合条件的研究被纳入数据图表制作。报告了三个关键主题：用于识别投诉和不当行为的风险因素和预测因素的方法、在合格研究中综合确定的风险因素和预测因素，以及为医疗从业者投诉和不当行为开发的预测工具。研究结果表明，投诉和不当行为的风险识别和预测是复杂的问题，受到多种因素的影响，表现出非线性模式并且具有特定的背景。需要进一步努力了解风险因素的特征和相互作用，开发系统的风险预测工具，并促进在监管环境中的应用。
