An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department. (July 2020)

• Record Type: Journal Article
• Authors: Kuo, Yong-Hong; Chan, Nicholas B.; Leung, Janny M.Y.; Meng, Helen; So, Anthony Man-Cho; Tsoi, Kelvin K.F.; Graham, Colin A.
• Is Part Of: International journal of medical informatics. Volume 139(2020)
• Publication Date: 2020-07
• Subjects: emergency departments -- waiting time -- machine learning -- artificial intelligence -- systems thinking; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104143
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Machine learning models are presented which tackle the real-time and personalized waiting time prediction in an emergency department (ED) Machine learning models are more effective than linear regression models for predicting patient waiting time The knowledge of the ED system is effective in enhancing the performance of the prediction models Accurate patient waiting time prediction through machine learning is useful for hospitals to manage resources to anticipate potential overcrowding situations in a timely manner. Also, patients can make informed decision in waiting in the ED or seeking medical services at other healthcare facilities. Abstract: Objective: The objective of this study is to apply machine learning algorithms for real-time and personalized waiting time prediction in emergency departments. We also aim to introduce the concept of systems thinking to enhance the performance of the prediction models. Methods: Four popular algorithms were applied: (i) stepwise multiple linear regression; (ii) artificial neural networks; (iii) support vector machines; and (iv) gradient boosting machines. A linear regression model served as a baseline model for comparison. We conducted computational experiments based on a dataset collected from an emergency department in Hong Kong. Model diagnostics were performed, and the results were cross-validated. Results: All the four machine learning algorithms with the use of systems knowledge outperformed the baseline model. The stepwise multiple linear regression reduced the mean-square error by almost 15%. The other three algorithms had similar performances, reducing the mean-square error by approximately 20%. Reductions of 17 – 22% in mean-square error due to the utilization of systems knowledge were observed. Discussion: The multi-dimensional stochasticity arising from the ED environment imposes a great challenge on waiting time prediction. The introduction of the concept of systems thinking led to significant enhancements of the models, suggesting that interdisciplinary efforts could potentially improve prediction performance. Conclusion: Machine learning algorithms with the utilization of the systems knowledge could significantly improve the performance of waiting time prediction. Waiting time prediction for less urgent patients is more challenging.