Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.
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DOI：
10.1371/journal.pone.0260476
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Clifton DA
中科院分区：
文献类型：
--
作者：
Bishop JA;Javed HA;El-Bouri R;Zhu T;Taylor T;Peto T;Watkinson P;Eyre DW;Clifton DA
在季节性流感和COVID-19大流行等感染发病率增加的时期，医院的病人流量延迟和病床短缺是司空见惯的。这项研究的目的是开发和评估机器学习方法在识别和排名个别患者出院的实时准备情况方面的有效性，目的是在危机期间改善医院内的患者流量。来自牛津大学医院的电子健康记录数据用于训练独立模型，以根据患者入院的性质（计划或紧急）和自入院以来的天数对患者子集在24小时内的出院实时准备情况进行分类和排名。提出了一种使用模型推理的策略，通过该策略，模型对所有住院患者进行预测，并将其按在接下来的24小时内出院的可能性进行排序。排名最高的20%的患者被视为出院候选人，因此预计将由临床医生进行进一步筛选，以确认他们是否准备出院。根据阳性预测值（PPV）评价性能，即，这些患者在经过临床医生的第二次筛查后被正确地视为“准备出院”的比例。患者在入院第一天的性能较高（计划/急诊患者的PPV分别为0.96/0.94），但患者进一步进入更长时间的入院后性能下降（计划/急诊患者在7天后仍在住院，PPV = 0.66/0.71）。我们展示了机器学习方法在任何给定时刻为医院中的所有个体患者进行以操作为重点的第二天出院准备预测的有效性，并提出了在危机期间在决策支持工具中使用它们的策略。
Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients’ real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models’ inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as ‘ready for discharge’ after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.
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