Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

用于医院运营的机器学习研究不足。我们提出了一个预测管道，该管道使用英国教学医院急诊科 (ED) 患者的实时电子健康记录来生成急诊入院的短期概率预测。一组 XGBoost 分类器应用于 109,465 次 ED 就诊，根据预测点经过的就诊时间产生了从 0.82 到 0.90 的 AUROC。汇总患者级别的入院概率以预测当前 ED 患者的入院人数，并结合尚未到达的患者，在指定时间窗口内预测急诊入院总数。该管道的平均绝对误差 (MAE) 为 4.0（平均百分比误差为 17%），而基准指标为 6.5（32%）。用104开发的模型，在 Covid-19 大流行期间进行了 504 次访问，AUROC 为 0.68-0.90，MAE 为 4.2（30%），而基准为 4.9（33%）。我们讨论了我们如何克服设计和实施模型以供实时使用的挑战，包括时间框架、数据准备和不断变化的操作条件。