{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:25:33Z","timestamp":1781763933043,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","award":["1R21HS029410-01A1"],"award-info":[{"award-number":["1R21HS029410-01A1"]}],"id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Emergency department (ED) overcrowding remains a major challenge for hospitals, resulting in worse outcomes, longer waits, elevated hospital operating costs, and greater strain on staff. Boarding count, the number of patients who have been admitted to an inpatient unit but are still in the ED waiting for transfer, is a key patient flow metric that affects overall ED operations. This study presents a deep learning-based approach to forecasting ED boarding counts using only operational and contextual features\u2014derived from hourly ED tracking, inpatient census, weather, holiday, and local event data\u2014without patient-level clinical information. Different deep learning algorithms were tested, including convolutional and transformer-based time-series models, and the best-performing model, Time Series Transformer Plus (TSTPlus), achieved strong performance at the 6-h prediction horizon, with a mean absolute error of 4.30 and an R2 score of 0.79. After identifying TSTPlus as the best-performing model, its performance was further evaluated at additional horizons of 8, 10, and 12 h. The model was also evaluated under extreme operational conditions, demonstrating robust and accurate forecasts. These findings highlight the potential of the proposed forecasting approach to support proactive operational planning and reduce ED overcrowding.<\/jats:p>","DOI":"10.3390\/informatics12030095","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T08:56:46Z","timestamp":1758013006000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Forecasting of Boarding Patient Counts to Address Emergency Department Overcrowding"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7918-5307","authenticated-orcid":false,"given":"Orhun","family":"Vural","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6775-0450","authenticated-orcid":false,"given":"Bunyamin","family":"Ozaydin","sequence":"additional","affiliation":[{"name":"Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA"},{"name":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4290-6014","authenticated-orcid":false,"given":"James","family":"Booth","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittany F.","family":"Lindsey","sequence":"additional","affiliation":[{"name":"Department of Patient Throughput, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6081-8507","authenticated-orcid":false,"given":"Abdulaziz","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL 35294, USA"},{"name":"Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, Y., Ortiz, S.S., Rowe, B.H., and Rosychuk, R.J. (2022). Emergency department crowding negatively influences outcomes for adults presenting with asthma: A population-based retrospective cohort study. BMC Emerg. Med., 22.","DOI":"10.1186\/s12873-022-00766-7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4500","DOI":"10.1111\/jan.16708","article-title":"Emergency Department Crowding as Contributing Factor Related to Patient-Initiated Violence Against Nurses\u2014A Literature Review","volume":"81","author":"Xie","year":"2025","journal-title":"J. Adv. Nurs."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alishahi Tabriz, A., Birken, S.A., Shea, C.M., Fried, B.J., and Viccellio, P. (2019). What is full capacity protocol, and how is it implemented successfully?. Implement. Sci., 14.","DOI":"10.1186\/s13012-019-0925-z"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mehrolhassani, M.H., Behzadi, A., and Asadipour, E. (2025). Key performance indicators in emergency department simulation: A scoping review. Scand. J. Trauma Resusc. Emerg. Med., 33.","DOI":"10.1186\/s13049-024-01318-7"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ajem.2021.10.049","article-title":"The impact of emergency department crowding on admission decisions and patient outcomes","volume":"51","author":"Ouyang","year":"2022","journal-title":"Am. J. Emerg. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Savioli, G., Ceresa, I.F., Gri, N., Bavestrello Piccini, G., Longhitano, Y., Zanza, C., Piccioni, A., Esposito, C., Ricevuti, G., and Bressan, M.A. (2022). Emergency department overcrowding: Understanding the factors to find corresponding solutions. J. Pers. Med., 12.","DOI":"10.3390\/jpm12020279"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1377\/hlthaff.2011.0786","article-title":"Solutions to emergency department \u2018boarding\u2019and crowding are underused and may need to be legislated","volume":"31","author":"Rabin","year":"2012","journal-title":"Health Aff."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1002\/emp2.12100","article-title":"The impact of hospital boarding on the emergency department waiting room","volume":"1","author":"Smalley","year":"2020","journal-title":"JACEP Open"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Su, H., Meng, L., Sangal, R., and Pinker, E.J. (2025, August 25). Emergency Department Boarding: Quantifying the Impact of ED Boarding on Patient Outcomes and Downstream Hospital Operations. Available SSRN 4693153 2024. Available online: https:\/\/ssrn.com\/abstract=4693153.","DOI":"10.2139\/ssrn.4693153"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1017\/cem.2018.18","article-title":"Emergency department boarding: A descriptive analysis and measurement of impact on outcomes","volume":"20","author":"Salehi","year":"2018","journal-title":"Can. J. Emerg. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Joseph, J.W., Elhadad, N., Mattison, M.L., Nentwich, L.M., Levine, S.A., Marcantonio, E.R., and Kennedy, M. (2024). Boarding Duration in the Emergency Department and Inpatient Delirium and Severe Agitation. JAMA Netw. Open, 7.","DOI":"10.1001\/jamanetworkopen.2024.16343"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1111\/acem.13978","article-title":"Managing and measuring emergency department care: Results of the fourth emergency department benchmarking definitions summit","volume":"27","author":"Yiadom","year":"2020","journal-title":"Acad. Emerg. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11739-019-02231-z","article-title":"Association between long boarding time in the emergency department and hospital mortality: A single-center propensity score-based analysis","volume":"15","author":"Boulain","year":"2020","journal-title":"Intern. Emerg. Med."},{"key":"ref_14","first-page":"663","article-title":"Clinicians\u2019 insights on emergency department boarding: An explanatory mixed methods study evaluating patient care and clinician well-being","volume":"49","author":"Loke","year":"2023","journal-title":"Jt. Comm. J. Qual. Patient Saf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, L., Tapia, M., Menzel, K., Page, M., and Ellis, W. (2022). Predicting need for hospital beds to reduce emergency department boarding. Perm. J., 26.","DOI":"10.7812\/TPP\/21.211"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hoot, N.R., Banuelos, R.C., Chathampally, Y., Robinson, D.J., Voronin, B.W., and Chambers, K.A. (2021). Does crowding influence emergency department treatment time and disposition?. JACEP Open, 2.","DOI":"10.1002\/emp2.12324"},{"key":"ref_17","unstructured":"Suley, E.O. (2022). A Hybrid Systems Model for Emergency Department Boarding Management. [Ph.D. Thesis, University of Texas at Arlington]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32479","DOI":"10.1109\/ACCESS.2022.3160742","article-title":"Analysis on benefits and costs of machine learning-based early hospitalization prediction","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10729-019-09496-y","article-title":"Prediction of emergency department patient disposition decision for proactive resource allocation for admission","volume":"23","author":"Lee","year":"2020","journal-title":"Health Care Manag. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.ecolmodel.2004.03.013","article-title":"An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data","volume":"178","author":"Olden","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_21","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Curran Associates Inc.. (NIPS\u201917)."},{"key":"ref_22","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_23","unstructured":"Oguiza, I. (2025, August 29). TSTPlus. Available online: https:\/\/timeseriesai.github.io\/tsai\/models.tstplus.html."},{"key":"ref_24","unstructured":"Oguiza, I. (2025, August 29). TSiTPlus. Available online: https:\/\/timeseriesai.github.io\/tsai\/models.tsitplus.html."},{"key":"ref_25","unstructured":"Oguiza, I. (2025, August 29). ResNetPlus. Available online: https:\/\/timeseriesai.github.io\/tsai\/models.resnetplus.html."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vural, O., Ozaydin, B., Aram, K.Y., Booth, J., Lindsey, B.F., and Ahmed, A. (2025). An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study. arXiv.","DOI":"10.2196\/73960"},{"key":"ref_27","unstructured":"OpenWeather (2025, August 29). History Bulk. Available online: https:\/\/openweathermap.org\/history-bulk."},{"key":"ref_28","unstructured":"United States Office of Personnel Management (2025, August 29). Federal Holidays, Available online: https:\/\/www.opm.gov\/policy-data-oversight\/pay-leave\/federal-holidays\/."},{"key":"ref_29","unstructured":"Alabama Athletics\u2014Official Athletics Website (2025, August 29). Football Schedule. Available online: https:\/\/rolltide.com\/sports\/football\/schedule."},{"key":"ref_30","unstructured":"Auburn Tigers\u2014Official Athletics Website (2025, August 29). Football Schedule. Available online: https:\/\/auburntigers.com\/sports\/football\/schedule."},{"key":"ref_31","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","unstructured":"Oguiza, I. (2025, August 29). Tsai\u2014A State-of-the-Art Deep Learning Library for Time Series and Sequential Data. Available online: https:\/\/github.com\/timeseriesai\/tsai."},{"key":"ref_33","unstructured":"Paszke, A. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Howard, J., and Gugger, S. (2020). Fastai: A layered API for deep learning. Information, 11.","DOI":"10.3390\/info11020108"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., and Eickhoff, C. (2021, January 14\u201318). A transformer-based framework for multivariate time series representation learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.","DOI":"10.1145\/3447548.3467401"},{"key":"ref_36","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_38","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., and K\u00e9gl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1162\/106365601750190398","article-title":"Completely derandomized self-adaptation in evolution strategies","volume":"9","author":"Hansen","year":"2001","journal-title":"Evol. Comput."},{"key":"ref_40","unstructured":"Kingma, D.P. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer. [2nd ed.].","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_42","unstructured":"You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., and Hsieh, C.-J. (2019). Large batch optimization for deep learning: Training bert in 76 min. arXiv."},{"key":"ref_43","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J. (2019). On the variance of the adaptive learning rate and beyond. arXiv."},{"key":"ref_44","first-page":"1","article-title":"An introduction to the bootstrap","volume":"57","author":"Tibshirani","year":"1993","journal-title":"Monogr. Stat. Appl. Probab."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/3\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:45:58Z","timestamp":1760035558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/3\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["informatics12030095"],"URL":"https:\/\/doi.org\/10.3390\/informatics12030095","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]}}}