Background Patient flow directly affects quality of care, access and financial performance for hospitals. Multidisciplinary discharge-focused rounds have proven to minimise avoidable delays experienced by patients near discharge. The study objective was to support discharge-focused rounds by implementing a machine-learning-based discharge prediction model using real-time electronic health record (EHR) data. We aimed to evaluate model predictive performance and impact on hospital length-of-stay. Methods Discharge prediction models were developed from hospitalised patients on four inpatient units between April 2016 and September 2018. Unit-specific models were implemented to make individual patient predictions viewable with the EHR patient track board. Predictive performance was measured prospectively for 12 470 patients (120 780 patient-predictions) across all units. A pre/poststudy design applying interrupted time series methods was used to assess the impact of the discharge prediction model on hospital length-of-stay. Results Prospective discharge prediction performance ranged in area under the receiver operating characteristic curve from 0.70 to 0.80 for same-day and next-day predictions; sensitivity was between 0.63 and 0.83 and specificity between 0.48 and 0.80. Elapsed length-of-stay, counts of labs and medications, mobility assessments and measures of acute kidney injury were model features providing the most predictive value. Implementing the discharge predictions resulted in a reduction in hospital length-of-stay of over 12 hours on a medicine unit (p<0.001) and telemetry unit (p=0.002), while no changes were observed for the surgery unit (p=0.190) and second medicine unit (p<0.555). Conclusions Incorporating automated patient discharge predictions into multidisciplinary rounds can support decreases in hospital length-of-stay. Variation in execution and impact across inpatient units existed.

中文翻译：

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基于机器学习的出院预测可以支持多学科检查并减少住院时间

背景 患者流量直接影响医院的护理质量、可及性和财务绩效。以多学科出院为重点的轮次已被证明可以最大限度地减少即将出院的患者经历的可避免的延误。研究目标是通过使用实时电子健康记录 (EHR) 数据实施基于机器学习的出院预测模型来支持以出院为重点的轮次。我们旨在评估模型预测性能和对住院时间的影响。方法 出院预测模型是从 2016 年 4 月至 2018 年 9 月期间四个住院病房的住院患者开发的。实施了特定于病房的模型，以便通过 EHR 患者跟踪板查看个别患者的预测。对所有单位的 12 470 名患者（120 780 名患者预测）的预测性能进行了前瞻性测量。应用间断时间序列方法的研究前/研究后设计用于评估出院预测模型对住院时间的影响。结果 预期出院预测性能在接受者操作特征曲线下面积范围为 0.70 至 0.80，用于当天和次日预测；敏感性介于 0.63 和 0.83 之间，特异性介于 0.48 和 0.80 之间。经过的住院时间、实验室和药物的计数、活动性评估和急性肾损伤的测量是提供最具预测价值的模型特征。实施出院预测后，医疗单位 (p<0.001) 和遥测单位 (p=0.0.001) 的住院时间减少了 12 小时以上。002），而手术单元（p=0.190）和第二药物单元（p<0.555）没有变化。结论将自动患者出院预测纳入多学科轮次可以支持缩短住院时间。住院部之间的执行和影响存在差异。