From chaos to coordination: leveraging technology for efficient bed management in a large and complex healthcare system

Authors

  • Abhinav Wankar Department of Operations, AIG Hospitals, Hyderabad, India
  • Krishna Chaitanya Department of Operations, AIG Hospitals, Hyderabad, India
  • Mohammed A. Uddin Department of Operations, AIG Hospitals, Hyderabad, India
  • Capri Jalota Department of Operations, AIG Hospitals, Hyderabad, India
  • Kinjal Saxena Department of Healthcare Technology, AIG Hospitals, Hyderabad, India
  • Gaurav Mojasia Department of Healthcare Technology, AIG Hospitals, Hyderabad, India
  • Satish Pareek Department of Operations, AIG Hospitals, Hyderabad, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20252402

Keywords:

Bed management, Hospital operations, Health care delivery, Healthcare technology, Operational excellence

Abstract

Background: Effective bed management is fundamental to hospital operations, significantly impacting patient care, resource utilization, and overall efficiency. AIG Hospitals, Hyderabad (India) accommodates 620 in-patient beds catering to more than 2,500 outpatients daily. The emergency department caters to over 70 visits each day and a 65% in-patient admission conversion rate. On average, the hospital handles 100 admissions and 100 internal transfer requests daily, maintaining a bed occupancy rate exceeding 75%. The Hospital tried handling through shared Excel sheets however bed management efficiency was a concern.

Methods: To solve the problem, the hospital decided to leverage modern technology with good user interface to undertake real-time bed management.

Results: This led to improvement in bed occupancy from 75% to 80%, increase in bed turnover rate by 11%, reduction in admission to arrival turnaround time (TAT) by 65% and improvement in net promoter score (NPS) by 14%. Average monthly admission went up by 12% on an average – converting to 323 additional admissions per month. The correlation for bed occupancy rate admission to arrival TAT and NPS was statistically significant.

Conclusions: This article explores this innovative solution that revolutionized bed management by leveraging real-time tracking, intelligent analytics, and improved communication. As healthcare demand grows, hospitals must adopt innovative solutions. This study demonstrates how integrating a web-based bed management system can transform hospital operations, optimize resource use, and improve care delivery-setting a new benchmark in hospital efficiency.

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References

Matos J, Rodrigues PP. Modeling decisions for hospital bed management. HEALTHINF 2011 - International Conference on Health Informatics. 2011;504-7. DOI: https://doi.org/10.5220/0003135005040507

Blair R. Capacity management: the bedrock of efficiency. Health Manag Technol. 2005;26:30-1. DOI: https://doi.org/10.1097/00004010-200504000-00005

Szabo P. Bed-der than ever. Pittsburgh hospital uses Web-based bed tracking and control to speed efficiency in its ED. Health Manag Technol. 2003;24:58-9.

Reuille R. Bed control report: a computer-based system to track patient admissions delayed or rescheduled due to a bed shortage. J Nurs Adm. 2004;34:539-42. DOI: https://doi.org/10.1097/00005110-200412000-00001

Kannry J, Emro S, Blount M, Ebling M. Small-scale testing of RFID in a hospital setting: RFID as bed trigger. AMIA Annu Symp Proc. 2007;384:388.

Green LV, Nguyen V. Strategies for cutting hospital beds: The impact on patient service. Health Serv Res. 2001;36:421-42.

Nguyen JM, Six P, Antonioli D, Glemain P, Potel G, Lombrail P, et al. A simple method to optimize hospital beds capacity. Int J Med Inform. 2005;74(1):39-49. DOI: https://doi.org/10.1016/j.ijmedinf.2004.09.001

Kokangul A. A combination of deterministic and stochastic approaches to optimize bed capacity in a hospital unit. Comput Methods Programs Biomed. 2008;90(1):56-65. DOI: https://doi.org/10.1016/j.cmpb.2008.01.001

Mackay M, Lee M. Choice of Models for the Analysis and Forecasting of Hospital Beds. Health Care Management Sci. 2005;8:221-30. DOI: https://doi.org/10.1007/s10729-005-2013-y

Millard PH, Mackay M, Vasilakis C. Measuring and modelling surgical bed usage. Ann Royal Coll Surg Engl. 2000;82:75-82.

Ridge JC, Jones SK, Nielsen MS, Shahani AK. Capacity planning for intensive care units. Eur J Operational Res. 1998;105(2):346-55. DOI: https://doi.org/10.1016/S0377-2217(97)00240-3

van de Vrugt NM, Schneider AJ, Zonderland ME, Stanford DA, Boucherie RJ. Operations research for occupancy modeling at hospital wards and its integration into practice. In: Kahraman C, Topcu YI, editors. Operations Research Applications in Health Care Management. Springer US, Boston, MA. 2018;262:101-37. DOI: https://doi.org/10.1007/978-3-319-65455-3_5

Freitas EE, Schramm FR. The morality of allocating resources to the elderly care in intensive care unit. Rev Bras Ter Intensiva. 2009;21(4):432-6. DOI: https://doi.org/10.1590/S0103-507X2009000400014

Suppapitnarm N, Pongpirul K. Model for allocation of medical specialists in a hospital network. J Healthc Leadersh. 2018;10:45-53. DOI: https://doi.org/10.2147/JHL.S166944

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Published

2025-07-30

How to Cite

Wankar, A., Chaitanya, K., Uddin, M. A., Jalota, C., Saxena, K., Mojasia, G., & Pareek, S. (2025). From chaos to coordination: leveraging technology for efficient bed management in a large and complex healthcare system. International Journal of Research in Medical Sciences, 13(8), 3328–3335. https://doi.org/10.18203/2320-6012.ijrms20252402

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Original Research Articles