Deep Learning Techniques For Forecasting Emergency Department Patient Wait Times In Healthcare Queue Systems
Keywords:
Customer, Queue, EHR, Optimization Algorithm, Phase type Queuing Model
Abstract
A lot of hospitals make use of the duration of patients' stays in queue as a gauge for overcrowding in the emergency room (ER). Many emergency rooms have lengthy wait times, which make it more challenging to provide patients with appropriate care and increases overall expenses. In queuing system applications, Innovative techniques like machine learning and deep learning (DL) have become crucial. In order to forecast Waiting periods for patients in a system, this research will use deep learning techniques for historical queuing variables, either in addition to or instead of queuing theory .SGD, Adam, RMSprop, and AdaGrad were the four optimization algorithms that were applied. To determine which model has the minimum absolute mean error (MAE), there was an algorithmic comparison. To facilitate more comparisons, a traditional mathematical simulation was utilized. The findings demonstrated that the DL model may be used to estimate patients' waiting times utilizing the SGD algorithm, with the lowest MAE of 09.60 minutes (23% reduction of errors) activated. In order to better priorities patients in a queue, this study contributes theoretically to the field of patient waiting time prediction using alternative methodologies by establishing the highest performing model. This study also makes a useful addition by utilizing actual data from emergency rooms. In addition, we suggested models that, compared to a conventional mathematical approach, produced more accurate predictions of patients' waiting times. Our method can be readily applied to the healthcare sector's queue system by utilizing data from electronic health records (EHRs). Since over 40% of people who are admitted to hospitals do so through the emergency rooms (ER), most hospitals suffer from extreme patient overcrowding. Since most ER departments in hospitals have lengthy patient wait times, they are an important component of healthcare facilities.
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2. emergency department crowding on disposition patterns and outcomes of discharged patients, International Journal of Emergency Medicine, vol. 12, no. 1, pp. 1-11, 2019.
3. Bittencourt, O., Vedat, V., and Morty, Y., Hospital capacity management based on the queueing theory, International Journal of Productivity and Performance Management, vol. 67, no. 2, pp. 224-38, 2018.
4. Brownlee, J., Gentle introduction to the adam optimization algorithm for deep learning. machine learning mastery. Available: https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/, 2020.
5. Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40(1), 16–28.
6. Cai, X., Oscar, P., Enrico, C., Fernando M., Richard D., David R., and Blanca G., Real-time prediction of mortality, readmission, and length of stay using electronic health record data, Journal of the American Medical Informatics Association, vol. 23, no. 3, pp. 553-61, 2016.
7. Chandrashekar, G., and Ferat, S., A survey on feature selection methods, Computers and Electrical Engineering, vol. 40, no. 1, pp.16-28, 2014.
8. Curtis, C., Chang, L., Thomas, J. B., and Oleg, S. P., Machine learning for predicting patient wait times and appointment delays, Journal of the American College of Radiology, vol. 15, no. 9, pp. 1310-1316, 2018.
9. Dong, J., Elad, Y., and Galit, B. Y., The impact of delay announcements on hospital network coordination and waiting times, Management Science, vol. 65, no. 5, pp. 1969-1994, 2019.
10. Di S. S., Paladino, L, V., Lalle, I., Magrini, L., and Magnanti, M., Overcrowding in emergency department: an international issue, Internal and emergency medicine, vol. 10, no. 2, pp. 171-175. 2015.
11. Eiset, A. H., Hans, K., and Mogens, E., Crowding in the emergency department in the absence of boarding - a transition regression model to predict departures and waiting time, BMC Medical Research Methodology, vol. 19, no. 1, pp. 68, 2019.
12. Gupta, D., Queueing Models for Healthcare Operations, handbook of healthcare operations management, Springer New York LLC, vol. 184, pp. 19–44, 2013.
13. Gupta, D., and Brian, D., Appointment scheduling in health care: challenges and opportunities, IIE Transactions, vol. 40, no. 9, pp. 800–819, 2008.
14. Hara, K., Daisuke, S., and Hayaru, S., Analysis of function of rectified linear unit used in deep learning, Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 12-17 July 2015.
15. Kaushal, A., Yuancheng, Z., Qingjin P., Trevor, S., Erin, W., Michael, Z., and Alecs, C., Evaluation of fast-track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department, Socio-Economic Planning Sciences, vol. 50, pp. 18-31, 2015.
16. Kea, B., Rochelle, F., Robert, A. L., and Benjamin, C. S., Interpreting the national hospital ambulatory medical care survey: United States Emergency Department Opioid Prescribing, Academic Emergency Medicine, vol. 23, no. 2, pp. 159-165, 2006-2010
17. Kuo, Y. H., Nicholas, B. C., Janny, M. Y. L., Helen, M., Anthony, M. C. S., Kelvin, K. F. T., and Colin, A. G., An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department, International Journal of Medical Informatics, vol. 139, pp. 104-143, 2020.
18. Kyritsis, A. I. and Michel, D., A machine learning approach to waiting time prediction in queueing scenarios, Proceedings of 2nd International Conference on Artificial Intelligence for Industries, pp. 17-21, 2019.
19. Liang, T. K., Queueing for healthcare, Article in Journal of Medical Systems, vol. 36, no. 2, pp. 541-547, 2010.
20. Mor, A., Shlomo, I., Avishai, M., Yariv N. M., Yulia, T., Galit B. Y., On patient flow in hospitals: A data-based queueing-science perspective, Stochastic Systems, vol. 5.1, pp. 146-194, 2015.
21. Moreno, Atilio, Lina A., Julián, F., Camilo, C., Sandra, T., and Oscar, M. M., Application of queuing theory to optimize the triage process in a tertiary emergency care (ER) department, Journal of Emergencies, Trauma and Shock, vol. 12, no. 4, pp. 268–273, 2019.
22. McMahan, B., and Streeter, M., Delay-tolerant algorithms for asynchronous distributed online learning. In Advances in Neural Information Processing Systems, pp. 2915-2923, 2014.
23. Mahadevan, B, Operations Management Theory and Practice, 3rd Edition, Pearson Education, India, 2015.
24. Pak, A., Brenda, G., and Andrew, S., Predicting waiting time to treatment for emergency department patients, International Journal of Medical Informatics, vol. 145, pp. 104303, 2020.
25. Palmer, G. I., Vincent, A. K., Paul R. H., and Asyl, L. H., Ciw: an open-source discrete event simulation library, Journal of Simulation, vol. 13, no. 1, pp. 68–82, 2019.
26. Pargent, F., Bischl, B., and Thomas, J., A benchmark experiment on how to encode categorical features in predictive modeling, Master Thesis, 2019.
27. Peterson, M. D., Dimitris, J. B., and Amedeo, R. O., Models and algorithms for transient queueing congestion at airports, Management Science, vol. 41, no. 8, pp. 1279-1295, 1995.
28. Pianykh, O. S. and Daniel, I. R., Can we predict patient wait time? Journal of the American College of Radiology, vol. 12, no. 10, pp. 1058–1066, 2015.
29. Rasouli, H. R., Esfahani, A. A., and Mohsen, A. F., Challenges, consequences, and lessons for way-outs to emergencies at hospitals: a systematic review study, BMC Emergency Medicine, vol. 19, no. 1, pp. 1-10, 2019.
30. Ruder, S., An overview of gradient descent optimization algorithms, Available: https://arxiv.org/abs/1609.04747, 2016
31. Ruben, A., Billy, J. M., Ying, P. T., Mark, H. D., Christopher, A. C., Song, Z., Gary, R., Timothy, S. S., Ying, M., and Ethan, A. H., An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data, Medical Care, vol. 48, No. 11, pp. 981-988, 2010.
32. Sasanfar, S., Morteza, B., and Afrooz, M., Improving emergency departments: simulation-based optimization of patients waiting time and staff allocation in an Iranian hospital, International Journal of Healthcare Management. vol. 16, pp. 1-8, 2020.
33. Shafaf, N., and Hamed, M., Applications of machine learning approaches in emergency medicine; a review article, Archives of Academic Emergency Medicine, vol. 7, no. 1, pp. 34, 2019.
34. Srivastava, T., How to predict waiting time using queuing theory? Available: https://www.analyticsvidhya.com/blog/2016/04/predict-waiting-time-queuing-theory/, December 17, 2019.
35. Sun, B. C., Adams, J., Orav, E. J., Rucker, D. W., Brennan, T. A., and Burstin, H. R., Determinants of patient satisfaction and willingness to return with emergency care, Annals of Emergency Medicine, vol. 35, no. 5, pp. 426-434, 2000.
36. Ülkü, Sezer, Chris, H., and Shiliang, C., Making the wait worthwhile: experiments on the effect of queueing on consumption, Management Science, vol. 66, no. 3, pp.1149-171, 2020.
37. Ward, P. R., Philippa, R., Clinton, C., Mariastella, P., Nicola, D., Simon, A.C., and Samantha, M., Waiting for’ and ‘waiting in’ public and private hospitals: a qualitative study of patient trust in south australia, BMC Health Services Research, vol. 17, no. 1, pp. 1-11, 2017.
Published
2024-09-09
How to Cite
R. K. Mishra, & Geetanjali Sharma. (2024). Deep Learning Techniques For Forecasting Emergency Department Patient Wait Times In Healthcare Queue Systems. Revista Electronica De Veterinaria, 25(1S), 775-784. https://doi.org/10.69980/redvet.v25i1S.857
Section
Articles
