{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:44:31Z","timestamp":1790718271859,"version":"4.1.0"},"reference-count":38,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The shortage of pediatric medical resources and overcrowding in children\u2019s hospitals are severe issues in China. Accurately predicting waiting times can help optimize hospital operational efficiency.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to develop machine learning models to predict waiting times for various laboratory and radiology examinations at a pediatric hospital.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Time stamp data from laboratory and radiology examinations were retrospectively collected from the pediatric hospital information system between November 1, 2024, and March 13, 2025. Two queue-related and 4 time-based features were extracted using queue theory. Linear regression and 8 machine learning models were trained to predict waiting times for each medical task. Hyperparameters were fine-tuned using randomized search and 10-fold cross-validation, and the bootstrap method was used for model evaluation. Mean absolute error, mean square error, root mean square error, and the coefficient of determination (\n R<\/jats:italic>\n \u00b2) were used as evaluation metrics. Shapley additive explanations values were used to assess feature importance.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A total of 230,864 time-stamped records were included after data preprocessing. The median waiting time was 4.817 (IQR 1.867-12.050) minutes for all medical tasks. Waiting times for radiology examinations were generally longer than those for laboratory tests. Tree-based algorithms, such as random forest and classification and regression trees, performed best in predicting laboratory test waiting times, with\n R<\/jats:italic>\n \u00b2 values ranging from mean 0.880(SD 0.003) to mean 0.934 (SD 0.003). However, the machine learning models did not perform well in predicting radiology examination waiting times, with\n R<\/jats:italic>\n 2<\/jats:sup>\n ranging from 0.114 (SD 0.005) to 0.719 (SD 0.004). Feature importance analysis revealed that queue-related predictors, especially the number of queuing patients, were the most important in predicting waiting times.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Task-specific prediction models are more appropriate for accurately predicting waiting times across various medical tasks. Guided by queue theory principles, we developed machine learning models for the waiting time prediction of each medical task and highlighted the importance of queue-related predictors.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/77297","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T17:10:06Z","timestamp":1754327406000},"page":"e77297-e77297","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Waiting Times for Medical Tasks in a Pediatric Hospital Using Machine Learning: Comprehensive, Retrospective, Real-World Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0862-2758","authenticated-orcid":false,"given":"Lin Lin","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-8558","authenticated-orcid":false,"given":"Rui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1734-2835","authenticated-orcid":false,"given":"Jia Yang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-9774","authenticated-orcid":false,"given":"Si","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7668-8720","authenticated-orcid":false,"given":"Yin","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9144-4816","authenticated-orcid":false,"given":"Jun","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7129-9236","authenticated-orcid":false,"given":"Mo Chen","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6391-8343","authenticated-orcid":false,"given":"Jiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4032-0308","authenticated-orcid":false,"given":"Ying","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"6","key":"R1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2020-013292","article-title":"The pediatric workforce: recent data trends, questions, and challenges for the future","volume":"147","author":"Vinci","journal-title":"Pediatrics"},{"issue":"6","key":"R2","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2022-059143","article-title":"The pediatrician workforce in the United States and China","volume":"151","author":"Russ","journal-title":"Pediatrics"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1136\/leader-2023-000894","article-title":"Innovative management strategies for addressing paediatric medical staff shortages in underdeveloped cities in developing countries","author":"Yan","journal-title":"BMJ Lead"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6963-7-31","article-title":"Willing to wait?: the influence of patient wait time on satisfaction with primary care","volume":"7","author":"Anderson","journal-title":"BMC Health Serv Res"},{"issue":"23","key":"R5","doi-asserted-by":"publisher","first-page":"E627","DOI":"10.1503\/cmaj.181426","article-title":"The impact of pediatric emergency department crowding on patient and health care system outcomes: a multicentre cohort study","volume":"191","author":"Doan","journal-title":"CMAJ"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.34172\/ijhpm.2024.8207","article-title":"Analysis of the prognosis outcomes and treatment delay among ST-segment elevation myocardial infarction patients in emergency department based on the presence of symptoms suggestive of COVID-19","volume":"13","author":"Kwak","journal-title":"Int J Health Policy Manag"},{"key":"R7","unstructured":"Clinic queue management system: enhancing patient experience and streamlining operations. Qtech, Queuing System Pte Ltd. URL: https:\/\/qtechqueueingsystem.com\/clinic-queue-management-system\/#real-time-queue-updates [Accessed 28-04-2025]"},{"issue":"9","key":"R8","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-014-0107-0","article-title":"Time series modelling and forecasting of emergency department overcrowding","volume":"38","author":"Kadri","journal-title":"J Med Syst"},{"issue":"5","key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/26402","article-title":"Optimization of patient flow in urgent care centers using a digital tool for recording patient symptoms and history: simulation study","volume":"5","author":"Montazeri","journal-title":"JMIR Form Res"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1287\/msom.2015.0560","article-title":"Accurate emergency department wait time prediction","volume":"18","author":"Ang","journal-title":"M&SOM"},{"key":"R11","unstructured":"Gajanan A . Reducing wait time prediction in hospital emergency room: lean analysis using a random forest model. University of Tennessee. URL: https:\/\/trace.tennessee.edu\/utk_gradthes\/4722 [Accessed 08-09-2025]"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2558199","article-title":"A parallel patient treatment time prediction algorithm and its applications in hospital queuing-recommendation in a big data environment","volume":"4","author":"Chen","journal-title":"IEEE Access"},{"key":"R13","volume":"2019","author":"Lin","journal-title":"AMIA Annu Symp Proc"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1109\/TEM.2022.3222094","article-title":"Delay prediction for managing multiclass service systems: an investigation of queueing theory and machine learning approaches","volume":"71","author":"Chocron","journal-title":"IEEE Trans Eng Manage"},{"key":"R15","doi-asserted-by":"publisher","DOI":"10.2196\/49605","article-title":"Characteristics and admission preferences of pediatric emergency patients and their waiting time prediction using electronic medical record data: retrospective comparative analysis","volume":"25","author":"Guo","journal-title":"J Med Internet Res"},{"issue":"4","key":"R16","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1136\/emj.2010.106534","article-title":"How accurate are predicted waiting times, determined upon a patient\u2019s arrival in the emergency department?","volume":"29","author":"Hemaya","journal-title":"Emerg Med J"},{"issue":"1","key":"R17","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.annemergmed.2021.02.010","article-title":"Predicting ambulance patient wait times: a multicenter derivation and validation study","volume":"78","author":"Walker","journal-title":"Ann Emerg Med"},{"issue":"9","key":"R18","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1016\/j.jacr.2017.08.021","article-title":"Machine learning for predicting patient wait times and appointment delays","volume":"15","author":"Curtis","journal-title":"J Am Coll Radiol"},{"issue":"6","key":"R19","first-page":"409","volume":"67","author":"Camacho","journal-title":"N C Med J"},{"key":"R20","doi-asserted-by":"crossref","unstructured":"Thiongane M Chan W L\u2019Ecuyer P . New history-based delay predictors for service systems. Presented at: 2016 Winter Simulation Conference (WSC); Dec 11-14, 2016:425-436; Washington, DC, USA. [doi: 10.1109\/WSC.2016.7822109]","DOI":"10.1109\/WSC.2016.7822109"},{"key":"R21","unstructured":"Lundberg SM Lee S . A unified approach to interpreting model predictions. Presented at: In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917). Dec 4-9, 2017:Curran Associates Inc. 4768-4777; Red Hook, NY."},{"issue":"3","key":"R22","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.emc.2020.04.006","article-title":"Queuing theory and modeling emergency department resource utilization","volume":"38","author":"Joseph","journal-title":"Emerg Med Clin North Am"},{"issue":"11","key":"R23","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-018628","article-title":"Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan","volume":"7","author":"Juang","journal-title":"BMJ Open"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6139847","article-title":"Research on M\/M\/1 queue of exponential departure intensity","volume":"2022","author":"Yang","journal-title":"Math Probl Eng"},{"key":"R25","unstructured":"Winston WL Goldberg JB . Operations Research: Applications and Algorithms. Vol 3. Duxbury Press; 2004."},{"issue":"4","key":"R26","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1023\/a:1020390425029","article-title":"Forecasting demand of emergency care","volume":"5","author":"Jones","journal-title":"Health Care Manag Sci"},{"issue":"3","key":"R27","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/s0169-2607(98)00032-7","article-title":"Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis","volume":"56","author":"Abdel-Aal","journal-title":"Comput Methods Programs Biomed"},{"issue":"18","key":"R28","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1002\/(sici)1097-0258(19970930)16:18<2117::aid-sim649>3.0.co;2-e","article-title":"Ten-year follow-up of ARIMA forecasts of attendances at accident and emergency departments in the Trent region","volume":"16","author":"Milner","journal-title":"Stat Med"},{"issue":"8","key":"R29","doi-asserted-by":"publisher","DOI":"10.2196\/38428","article-title":"Predicting patient wait times by using highly deidentified data in mental health care: enhanced machine learning approach","volume":"9","author":"Rastpour","journal-title":"JMIR Ment Health"},{"key":"R30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104143","article-title":"An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department","volume":"139","author":"Kuo","journal-title":"Int J Med Inform"},{"key":"R31","unstructured":"Grinsztajn L Oyallon E Varoquaux G . Why do tree-based models still outperform deep learning on tabular data? arXiv. Preprint posted online on Jul 18, 2022. URL: https:\/\/arxiv.org\/abs\/2207.08815"},{"key":"R32","unstructured":"China National Influenza Center homepage. China National Influenza Center. URL: https:\/\/ivdc.chinacdc.cn\/cnic\/ [Accessed 08-09-2025]"},{"key":"R33","unstructured":"National sentinel surveillance of acute respiratory infectious diseases. Chinese Center for Disease Control and Prevention. URL: https:\/\/www.chinacdc.cn\/jksj\/jksj04_14275\/ [Accessed 08-09-2025]"},{"issue":"6","key":"R34","doi-asserted-by":"publisher","first-page":"475","DOI":"10.12372\/jcp.2024.24e0432","article-title":"The impact of infectious disease outbreaks on the pediatric healthcare system and countermeasures","volume":"42","author":"Kang","journal-title":"J Clin Pediatr"},{"issue":"4","key":"R35","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s00431-015-2653-y","article-title":"Seasonal variation of diseases in children: a 6-year prospective cohort study in a general hospital","volume":"175","author":"Schrijver","journal-title":"Eur J Pediatr"},{"issue":"2","key":"R36","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1097\/PEC.0000000000001496","article-title":"Seasonality of common pediatric infectious diseases","volume":"37","author":"Lipsett","journal-title":"Pediatr Emerg Care"},{"issue":"12","key":"R37","doi-asserted-by":"publisher","first-page":"881","DOI":"10.3760\/cma.j.cn101070-20241105-00719","article-title":"Guideline for diagnosis, treatment and prevention of influenza in children (medical version, 2024)","volume":"39","author":"China Medicine Education Association Committee on Pediatrics","journal-title":"Chin J Appl Clin Pediatr"},{"issue":"330","key":"R38","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3969\/j.issn.2096-3327.2023.03.003","article-title":"Guidelines for diagnosis and treatment of Mycoplasma pneumonae pneumonia in children (2023 Edition)","volume":"3","author":"National Health Commission of the People\u2019s Republic of China","journal-title":"Chin J Ration Drug Use"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e77297","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:09:25Z","timestamp":1782842965000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2025\/1\/e77297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":38,"URL":"https:\/\/doi.org\/10.2196\/77297","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"article-number":"v13i6e77297"}}