Generation of automated clinical notes have been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center. The approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically. To our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write.

翻译：自动化临床笔记的生成已被提出作为减轻医生职业倦怠的策略之一。特别是，患者住院期间事件的自动化叙述性摘要可补充住院医师在电子健康记录系统中记录的出院小结的住院病程部分。在本研究中，我们开发并评估了一种基于编码器-解码器序列到序列Transformer模型的住院病程自动摘要方法。我们微调了BERT和BART模型，并通过约束束搜索优化事实准确性，使用某学术医疗中心神经内科收治患者的电子健康记录数据进行训练和测试。该方法取得了良好的ROUGE评分，其中R-2得分为13.76。在盲法评估中，两名委员会认证的医师将62%的自动生成摘要评为符合护理标准，表明该方法可能具有临床实用性。据我们所知，本研究是首批证明自动生成质量接近医生书写水平的出院小结住院病程的方法之一。