Machine Learning for Healthcare Workforce Analytics: A Review of Models for Staffing Prediction, Burnout Detection, Skill Matching, Productivity Monitoring, and Retention Risk Forecasting

cirpublications.com-rbRg-1791387360-y753802699 1791387360 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2026 6 1 Machine Learning for Healthcare Workforce Analytics: A Review of Models for Staffing Prediction, Burnout Detection, Skill Matching, Productivity Monitoring, and Retention Risk Forecasting Diego Morales Andres Gutierrez Lucia Navarro Pablo Rios 2026 10.68159/y753802699 https://cirpublications.com/pub/journal/2/article/y753802699 Tawfik DS, Profit J, Lake ET, Liu JB, Sanders LM, Phibbs CS. Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit. Health Serv Res. 2020;55(2):190-200. Erhard M, Schoenfelder J, Fügener A, Brunner JO. State of the art in physician scheduling. Eur J Oper Res. 2018;265(1):1-18. Tello M, Reich ES, Puckey J, Maff R, Garcia-Arce A, Feijoo F, et al. Machine learning based forecast for the prediction of inpatient bed demand. BMC Med Inform Decis Mak. 2022;22(1):55. Srinivas S, Ravindran AR. Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: a prescriptive analytics framework. Expert Syst Appl. 2018;102:245-61. Lou SS, Liu H, Warner BC, Harford D, Lu C, Kannampallil T. Predicting physician burnout using clinical activity logs: model performance and lessons learned. J Biomed Inform. 2022;127:104015. Lopez K, Li H, Paek H, Williams B, Nath B, Loza AJ, et al. Predicting physician departure with machine learning on EHR use patterns: a longitudinal cohort from a large multispecialty ambulatory practice. PLoS One. 2023;18(2):e0280251. Jiang H, Gomes P, Vander Meer D. Promoting continuity of care in nurse-patient assignment: a multiple objective heuristic algorithm. Decis Support Syst. 2023;167:113926. Lee JT, Crettenden I, Tran M, Miller D, Cormack M, Li J, et al. Methods for health workforce projection model: systematic review and recommended good practice reporting guideline. Hum Resour Health. 2024;22(1):25. Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med. 2017;15(5):419-26. Brandt T, Klein TL, Reuter-Oppermann M, Schäfer F, Thielen C, de Vrugt MV, et al. Integrated patient-to-room and nurse-to-patient assignment in hospital wards. OR Spectr. 2025;47:1-44. Camiat F, Restrepo MI, Chauny JM, Lahrichi N, Rousseau LM. Productivity-driven physician scheduling in emergency departments. Health Syst. 2021;10(2):104-17. Lieslehto J, Rantanen N, Oksanen LM, Oksanen SA, Kivimäki A, Paju S, et al. A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic. Sci Rep. 2022;12(1):8055. Saville CE, Griffiths P, Ball JE, Monks T. How many nurses do we need? A review and discussion of operational research techniques applied to nurse staffing. Int J Nurs Stud. 2019;97:7-13. Mahmoudian Y, Nemati A, Safaei AS. A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals. Healthc Anal. 2023;4:100245. Liu H, Lou SS, Warner BC, Harford DR, Kannampallil T, Lu C. HiPAL: a deep framework for physician burnout prediction using activity logs in electronic health records. In: Proc 28th ACM SIGKDD Conf Knowl Discov Data Min; 2022. p. 3377-87. Tawfik D, Bayati M, Liu J, Nguyen L, Sinha A, Shanafelt T, et al. Predicting primary care physician burnout from electronic health record use measures. Mayo Clin Proc. 2024;99(9):1411-21. Shi H, Liu J, Yang C, Shang J, Zeng Y. Machine learning for predicting burnout among healthcare workers: a systematic review and meta-analysis. Contemp Nurse. 2025;61:1-21. Othman MI, Nashwan AJ, Abujaber AA. Optimising nurse-patient assignments: the impact of machine learning model on care dynamics. Nurs Open. 2025;12(4):e70195. Zaerpour F, Bijvank M, Ouyang H, Sun Z. Scheduling of physicians with time-varying productivity levels in emergency departments. Prod Oper Manag. 2022;31(2):645-67. Kim SK, Kim EJ, Kim HK, Song SS, Park BN, Jo KW. Development of a nurse turnover prediction model in Korea using machine learning. Healthcare (Basel). 2023;11(11):1583. Xu Y, Park Y, Park JD, Sun B. Predicting nurse turnover for highly imbalanced data using the synthetic minority over-sampling technique and machine learning algorithms. Healthcare (Basel). 2023;11(24):3173. Moyo S, Doan TN, Yun JA, Tshuma N. Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa. Hum Resour Health. 2018;16(1):68. Bounsanga J, Lipsky MS, Hon ES, Licari FW, Ruttinger C, Salt A, et al. Using machine learning to identify factors associated with practice location of the healthcare workforce. Rural Remote Health. 2022;22(1):1-13. Baris VK, Fu Y, Gilbreath B, Rainbow J, Fiorini LA, Love P. Harnessing machine learning to predict nurse turnover intention and uncover key predictors: a multinational investigation. J Adv Nurs. 2025. 10.1111/jan.16678 Hudays A. Machine learning models for predicting nurse turnover and turnover intention: a systematic review. Saudi J Nurs Health Care. 2025;8(6). [In press]. Burford JS, Booth RG, McIntyre A. Exploring the intersection of nursing leadership and artificial intelligence: scoping review. JMIR Nurs. 2025;8:e80085. Chen B, Alrifai W, Gao C, Jones B, Novak L, Lorenzi N, et al. Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning. J Am Med Inform Assoc. 2021;28(6):1168-77. Ventura-Silva J, Martins MM, Trindade LD, Faria AD, Pereira S, Zuge SS, et al. Artificial intelligence in the organization of nursing care: a scoping review. Nurs Rep. 2024;14(4):2733-45. Kiwanuka F, Stevanin S, Ahtisham Y, Owusu B, Nurmeksela A, Kvist T. Nurse leadership and artificial intelligence integration in nursing workforce management: a scoping review. J Adv Nurs. 2025. 10.1111/jan.16789 Yakusheva O, Bouvier MJ, Hagopian CO. How artificial intelligence is altering the nursing workforce. Nurs Outlook. 2025;73(1):102300. Liu C, Chuang YC, Qin L, Ren L, Chien CW, Tung TH. Machine learning-based model for analysing and accurately predicting factors related to burnout in healthcare workers. BMJ Public Health. 2025;3(2).