Medication errors continue to be the leading cause of avoidable patient harm in hospitals. This paper sets out a framework to assure medication safety that combines machine learning and safety engineering methods. It uses safety analysis to proactively identify potential causes of medication error, based on expert opinion. As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis. Combining these two views has the potential to enable the risk of medication errors to be managed proactively and dynamically. We apply the framework to a case study involving thoracic surgery, e.g. oesophagectomy, where errors in giving beta-blockers can be critical to control atrial fibrillation. This case study combines a HAZOP-based safety analysis method known as SHARD with Bayesian network structure learning and process mining to produce the analysis results, showing the potential of the framework for ensuring patient safety, and for transforming the way that safety is managed in complex healthcare environments.

翻译：医疗错误仍然是医院中可以避免的病人伤害的主要原因。本文提出了一个确保药物安全的框架,将机器学习和安全工程方法结合起来。它利用安全分析,根据专家意见,主动地查明药物错误的潜在原因。由于保健数据丰富,因此有可能通过机器学习来增加安全分析,从数据中找出药物错误的实际原因,并查明它们与安全分析中预测的药物错误的不同之处。将这两种观点结合起来,就有可能积极主动地和动态地管理药物错误的风险。我们将这一框架应用到涉及胸腺外科手术的案例研究中,例如食道切除术中,给乙型阻塞器的错误对于控制临床纤维化至关重要。本案例研究将HAZOP基于安全分析的方法(HAZORD)与Bayesian网络结构的学习和过程采矿结合起来,以产生分析结果,展示确保病人安全的框架的潜力,并改变在复杂的保健环境中管理安全的方式。