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Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A
Emilien Arnaud1,2, Mahmoud Elbattah2,3, Christine Ammirati1,4
1Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France.
Insights
Artificial intelligence (AI) accurately predicted emergency department patient flow during COVID-19. This AI model helped hospitals manage resources effectively by estimating bed needs, reducing waste during the pandemic.
Area of Science:
- Health Informatics
- Artificial Intelligence in Healthcare
- Public Health Management
Background:
- The COVID-19 pandemic created a critical need to estimate emergency department (ED) bed requirements for patients with and without COVID-19.
- Amiens Picardy University Hospital (APUH) in France developed an AI project, "Prediction of the Patient Pathway in the Emergency Department" (3P-U), to address this challenge.
Purpose of the Study:
- To evaluate the 3P-U AI model's effectiveness in predicting real-time minimum and maximum emergency department bed requirements.
- To assess the impact of AI-driven bed predictions on hospital resource management during a pandemic.
Main Methods:
- A prospective, single-center study was conducted at APUH involving 105,457 patients from 2020-2021.
- The 3P-U model was used to predict patient disposition (discharge, admission, or unclassified).
- Performance was measured using the area under the receiver operating characteristic curve (AUROC).
Main Results:
- The 3P-U model achieved an AUROC of 0.82 for all patients and 0.90 for unambiguous cases.
- Patient predictions included 36.4% likely to be discharged, 17.8% likely to be admitted, and 45.8% unclassified.
- Predicted bed numbers informed hospital management in converting wards to COVID-19 units.
Conclusions:
- The 3P-U model demonstrates a practical application of AI for improving hospital resource management during global health crises.
- Utilizing AI to predict required bed numbers can significantly reduce resource waste, including time and beds.
- AI-driven predictive models are valuable tools for enhancing healthcare system resilience during pandemics.
Background:
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.
Materials:
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.
Discussion And Conclusions:
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.
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