Education, Science, Technology, Innovation and Life
Open Access
Sign In

Data-Driven Approaches to Hospital Capacity Planning and Management

Download as PDF

DOI: 10.23977/infkm.2023.040202 | Downloads: 9 | Views: 207


Safiye Turgay 1, Ömer Faruk Özçelik 1


1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Safiye Turgay


Data-driven approaches to hospital capacity planning and management involve using historical and real-time data to identify patterns and trends in patient demand, resource utilization, and other key metrics. This information provides to develop predictive models, forecast patient demand, optimize staffing levels, and improve patient outcomes. Electronic health record systems and Internet of Things devices can also be used to monitor hospital operations in real-time and identify areas of inefficiency. Hospital capacity planning and management are critical to ensuring that healthcare facilities have enough resources to meet the needs of their patients. Data-driven approaches can be helpful in addressing these challenges by providing insights into patient demand, resource utilization, and other key metrics. This article discusses the various data-driven approaches to hospital capacity planning and management and their potential benefits. It also highlights the importance of having the right infrastructure and expertise effectively collect, analyse, and act on this data.


Healthcare System; Data Driven; Capacity Planning; Data Management; Simulation


Safiye Turgay, Ömer Faruk Özçelik, Data-Driven Approaches to Hospital Capacity Planning and Management . Information and Knowledge Management (2023) Vol. 4: 6-14. DOI:


[1] Lee J., Lee D. H., Kim H. J., & Kim H. (2019). A data-driven approach for predicting hospital bed occupancy rates using machine learning models. Healthcare informatics research, 25(3), 177-183.
[2] Luo M., Tang C., & Li Y. (2020). Design of the system for hospital bed capacity prediction based on data mining. Journal of medical systems, 44(5), 104.
[3] Zeng X, Liu J, Wang X, et al. Data-driven hospital capacity planning for COVID-19 emergency management: a review. Brief Bioinform. 2021; 22(5):2024-2035. doi: 10.1093/bib/bbaa202.
[4] Fung C. H., Martin J. L., & Hays R. D. (2008). Using patient-reported outcomes to improve health care quality: from data to actionable intelligence. Joint Commission Journal on Quality and Patient Safety, 34(12), 621-627.
[5] Almeida J., Paisana H., & Oliveira M. (2019). Data-driven approaches for hospital bed capacity management. Health informatics journal, 25(3), 1246-1257.
[6] Wu M. W., Cheng Y. H., & Chen Y. C. (2017). The impact of big data on the healthcare system and clinical practice. Journal of medical systems, 41(10), 183.
[7] Arora S., Kaza S., & Krishnan R. (2019). Predictive modeling of hospital admissions using machine learning and ensemble methods. Health care management science, 22(4), 659-675.
[8] Mitchell J. A., & McClelland III L. E. (2019). Big data and predictive analytics in health care. American journal of health-system pharmacy, 76(4), 201-208.
[9] Fossett J. W. (2019). Capacity planning and management in healthcare: a review. Journal of healthcare management, 64(5), 325-336.
[10] Fleischman W., Penny L., & Kellermann A. (2015). Hospital capacity planning: from measuring stocks to modeling flows. RAND Corporation.
[11] Elnahal S. M., Rajkumar R., & Rogers S. O. (2019). The power of data-driven health systems. Health Affairs, 38(9), 1478-1484.
[12] Hu P. J. H., & Chau P. Y. K. (2003). Hospital information systems in Taiwan: a case study of a major medical center. International Journal of Medical Informatics, 70(2-3), 235-249.
[13] Díaz J., Girón F. J., & Fernández E. (2017). A data-driven approach to hospital capacity planning. Procedia computer science, 121, 422-427.
[14] Mohanty SD, Lekan D, McCoy TP, Jenkins M, Manda P. Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare. Patterns (N Y). 2021 Dec 3; 3(1):100395. doi: 10.1016/j.patter.2021.100395. PMID: 35079714; PMCID: PMC8767300.
[15] Davis S., Zhang J., Lee I. et al. Effective hospital readmission prediction models using machine-learned features. BMC Health Serv Res 22, 1415 (2022).
[16] Brailsford S. C., Harper P. R., Patel B., Pitt M., & Schmidt P. E. (2017). Combining simulation modelling and machine learning: a new toolkit for predicting and understanding hospital admission patterns. Journal of the operational research society, 68(9), 1098-1110.
[17] Seetharam K., Balla S., Bianco C. et al. Applications of Machine Learning in Cardiology. Cardiol Ther 11, 355–368 (2022).
[18] Turgay S., Reference Model of Cloud Computing Based Decision Support Hospital Management System, Cloud and Service-Oriented Computing (2022), Clausius Scientific Press, Canada) Vol. 2, DOI: 10.23977/csoc.2022.020101, ISSN 2516-399X Vol. 2 Num. 1
[19] Habehh H, Gohel S. Machine Learning in Healthcare. Curr Genomics. 2021 Dec 16; 22(4):291-300. doi: 10.2174/1389202922666210705124359. PMID: 35273459; PMCID: PMC8822225.
[20] Filippiadis D. K., Charalampopoulos G., Mazioti A., Alexiou G., Brountzos E., & Kelekis A. (2020). A data-driven approach to optimizing the use of resources in a radiology department. Journal of digital imaging, 33(1), 110-116.
[21] Mao L, Wu X, Yang X, et al. Hospital bed allocation with emergency medical service transport under the integrated triage policy: a discrete event simulation approach. BMC Health Serv Res. 2021; 21(1):61. doi: 10.1186/s12913-021-06004-1.
[22] Sah S., & Gupta S. (2019). Hospital capacity planning under uncertainty: a review. Health Care Management Science, 22(1), 1-16.
[23] Lee DJ, Ding J, Guzzo TJ. Improving Operating Room Efficiency. Curr Urol Rep. 2019 Apr 15; 20(6):28. doi: 10.1007/s11934-019-0895-3. PMID: 30989344.
[24] Seung H. Y., Kim J. W., & Kim J. H. (2018). Data-driven approach for predicting the probability of discharge in emergency department. Healthcare informatics research, 24(1), 35-42.
[25] Kariuki E. M., Yegon E. K., & Muumbo A. (2017). Big data analytics in healthcare: a review. International Journal of Computer Applications, 179(43), 1-8.
[26] Batko K, Ślęzak A. The use of Big Data Analytics in healthcare. J Big Data. 2022; 9(1):3. doi: 10.1186/s40537-021-00553-4. Epub 2022 Jan 6. PMID: 35013701; PMCID: PMC8733917.
[27] Ramesh T., Santhi V., Exploring big data analytics in health care, International Journal of Intelligent Networks, Volume 1, 2020, Pages 135-140.
[28] Rismanchian F, Kassani SH, Shavarani SM, Lee YH. A Data-Driven Approach to Support the Understanding and Improvement of Patients' Journeys: A Case Study Using Electronic Health Records of an Emergency Department. Value Health. 2023 Jan; 26(1):18-27. doi: 10.1016/j.jval. 2022.04.002. Epub 2022 May 25. PMID: 35623973.
[29] Michailidis P, Dimitriadou A, Papadimitriou T, Gogas P. Forecasting Hospital Readmissions with Machine Learning. Healthcare (Basel). 2022 May 25; 10(6):981. doi: 10.3390/healthcare10060981. PMID: 35742033; PMCID: PMC9222500.
[30] Civak H., Küren C., Turgay S., Examining the effects of COVID-19 Data with Panel Data Analysis, Social Medicine and Health Management (2021) Vol. 2: 1-16 Clausius Scientific Press, Canada DOI: 10.23977/socmhm. 2021.020101 ISSN 2616-2210
[31] Russo S. G., Neumann P., & Reinhardt S. (2016). Overview of commonly used hospital performance measures. Der Anaesthesist, 65(10), 788-797.
[32] Patel M. S., Volpp K. G., & Asch D. A. (2018). Nudge units to improve the delivery of health care. New England Journal of Medicine, 378(3), 214-216.
[33] Mckay B., & Degenholtz H. B. (2016). A simulation approach to hospital capacity planning. Journal of healthcare management, 61(1), 18-3. 

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.