De Micco Francesco, Di Palma Gianmarco, Ferorelli Davide, De Benedictis Anna, Tomassini Luca, Tambone Vittoradolfo, Cingolani Mariano, Scendoni Roberto
Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Department of Clinical Affair, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy.
Front Med (Lausanne). 2025 Jan 8;11:1522554. doi: 10.3389/fmed.2024.1522554. eCollection 2024.
Adverse events in hospitals significantly compromise patient safety and trust in healthcare systems, with medical errors being a leading cause of death globally. Despite efforts to reduce these errors, reporting remains low, and effective system changes are rare. This systematic review explores the potential of artificial intelligence (AI) in clinical risk management.
The systematic review was conducted using the PRISMA Statement 2020 guidelines to ensure a comprehensive and transparent approach. We utilized the online tool Rayyan for efficient screening and selection of relevant studies from three different online bibliographic.
AI systems, including machine learning and natural language processing, show promise in detecting adverse events, predicting medication errors, assessing fall risks, and preventing pressure injuries. Studies reveal that AI can improve incident reporting accuracy, identify high-risk incidents, and automate classification processes. However, challenges such as socio-technical issues, implementation barriers, and the need for standardization persist.
The review highlights the effectiveness of AI in various applications but underscores the necessity for further research to ensure safe and consistent integration into clinical practices. Future directions involve refining AI tools through continuous feedback and addressing regulatory standards to enhance patient safety and care quality.
医院中的不良事件严重损害了患者安全以及对医疗系统的信任，医疗差错是全球主要的死亡原因之一。尽管人们努力减少这些差错，但报告率仍然很低，有效的系统变革也很少见。本系统综述探讨了人工智能（AI）在临床风险管理中的潜力。
采用2020年PRISMA声明指南进行系统综述，以确保采用全面且透明的方法。我们使用在线工具Rayyan从三个不同的在线书目数据库中高效筛选和选择相关研究。
包括机器学习和自然语言处理在内的人工智能系统在检测不良事件、预测用药错误、评估跌倒风险和预防压疮方面显示出前景。研究表明，人工智能可以提高事件报告的准确性，识别高风险事件，并使分类过程自动化。然而，社会技术问题、实施障碍和标准化需求等挑战依然存在。
该综述强调了人工智能在各种应用中的有效性，但强调有必要进行进一步研究，以确保安全且一致地融入临床实践。未来的方向包括通过持续反馈优化人工智能工具，并解决监管标准问题，以提高患者安全和护理质量。
