Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France.
Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France.
Int J Environ Res Public Health. 2022 Aug 5;19(15):9667. doi: 10.3390/ijerph19159667.
During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called "Prediction of the Patient Pathway in the Emergency Department" (3P-U) to predict patient outcomes.
Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH's ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as "likely to be discharged", 18,815 (17.8%) were flagged as "likely to be admitted", and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units.
The 3P-U model's AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.
在 2019 年冠状病毒病（COVID-19）大流行期间，计算有和没有疑似 COVID-19 的患者所需的急诊部（ED）床位数量是一个真正的公共卫生问题。在法国，亚眠皮卡第大学医院（APUH）开发了一个名为“预测急诊部患者路径”（3P-U）的人工智能（AI）项目，以预测患者结局。
使用 3P-U 模型，我们对 2020 年和 2021 年在 APUH 急诊部就诊的患者进行了前瞻性、单中心研究。目的是根据 3P-U 模型实时确定所需的最小和最大床位数量。结果：共纳入 105457 例患者。3P-U 的受试者工作特征曲线下面积（AUROC）对所有患者为 0.82，对明确病例为 0.90。具体而言，38353 例（36.4%）患者被标记为“可能出院”，18815 例（17.8%）患者被标记为“可能住院”，48297 例（45.8%）患者无法被标记。根据预测的最小床位数量（仅针对明确病例）和最大床位数量（所有患者），医院管理层协调将病房转换为专用 COVID-19 病房。
3P-U 模型的 AUROC 在文献中报告的类似分类器的范围中间。通过考虑所需床位数量的范围，可以减少资源（例如时间和床位）的浪费。该研究得出结论，人工智能的应用可以在全球大流行（如 COVID-19）期间帮助极大地改善医院资源的管理。
