Skip to main content

Advertisement

SpringerLink
Log in

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients

  • Published:
Health Care Management Science Aims and scope Submit manuscript

This article has been updated

Abstract

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients’ arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches—beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Change history

  • 29 March 2024

    The original version of this article was updated to include the missing ESM file.

References

  1. Schonberger RJ (1982) Some observations on the advantages and implementation issues of just-in-time production systems. J Operat Manage 3(1):1–11

    Article  Google Scholar 

  2. Feitzinger E, Lee HL (1997) Mass customization at Hewlett-Packard: the power of postponement. Harvard Business Rev 75:116–123

    Google Scholar 

  3. Bastos LSL, Marchesi JF, Hamacher S, Fleck JL (2019) A mixed integer programming approach to the patient admission scheduling problem. European J Operat Res 273(3):831–840

    Article  Google Scholar 

  4. Ben Bachouch R, Guinet A, Hajri-Gabouj S (2012) An integer linear model for hospital bed planning. Int J Prod Econ 140(2):833–843

    Article  Google Scholar 

  5. Bilgin B, Demeester P, Misir M, Vancroonenburg W, Vanden Berghe G (2012) One hyper-heuristic approach to two timetabling problems in health care. J Heuristics 18(3):401–434

    Article  Google Scholar 

  6. Demeester P, Souffriau W, De Causmaecker P, Vanden Berghe G (2010) A hybrid tabu search algorithm for automatically assigning patients to beds. Artificial Intell Med 48(1):61–70

    Article  Google Scholar 

  7. Range TM, Lusby RM, Larsen J (2014) A column generation approach for solving the patient admission scheduling problem. European J Oper Res 235(1):252–264

    Article  Google Scholar 

  8. Ceschia S, Schaerf A (2012) Modeling and solving the dynamic patient admission scheduling problem under uncertainty. Artificial Intell Med 56(3):199–205

    Article  Google Scholar 

  9. Ceschia S, Schaerf A (2016) Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays. J Sched 19(4):377–389

    Article  Google Scholar 

  10. Zhu Y-H, Toffolo TAM, Vancroonenburg W, Vanden Berghe G (2019) Compatibility of short and long term objectives for dynamic patient admission scheduling. Comput Oper Res 104:98–112

    Article  Google Scholar 

  11. Heydar M, O’Reilly MM, Trainer E, Fackrell M, Taylor PG, Tirdad A (2021) A stochastic model for the patient-bed assignment problem with random arrivals and departures. Ann Oper Res 1–33

  12. Landa P, Sonnessa M, Tànfani E, Testi A (2018) Multiobjective bed management considering emergency and elective patient flows. Int Trans Oper Res 25(1):91–110

    Article  Google Scholar 

  13. Schäfer F, Walther M, Hübner A, Kuhn H (2019) Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management. Flexible Services and Manufacturing Journal 31(4):1012–1041

    Article  Google Scholar 

  14. Schmidt R, Geisler S, Spreckelsen C (2013) Decision support for hospital bed management using adaptable individual length of stay estimations and shared resources. BMC Medical Informatics and Decision Making 13:3

    Article  Google Scholar 

  15. Thomas BG, Bollapragada S, Akbay K, Toledano D, Katlic P, Dulgeroglu O, Yang D (2013) Automated bed assignments in a complex and dynamic hospital environment. Interfaces 43(5):435–448

    Article  Google Scholar 

  16. Thompson S, Nunez M, Garfinkel R, Dean MD (2009) OR Practice-Efficient short-term allocation and reallocation of patients to floors of a hospital during demand surges. Oper Res 57(2):261–273

    Article  Google Scholar 

  17. Shen Y, Lee LH (2019) Improving the wait time to admission by reducing bed rejections. BMJ Open Quality 8(3):000710

    Article  Google Scholar 

  18. Patel PB, Combs MA, Vinson DR (2014) Reduction of admit wait times: the effect of a leadership-based program. Academic Emergency Medicine 21(3):266–273

    Article  Google Scholar 

  19. Chan C, Scheulen J (2017) Administrators leverage predictive analytics to manage capacity, streamline decision-making. ED Management: the Monthly Update on Emergency Department Management 29(2):19–23

    Google Scholar 

  20. Chartier LB, Simoes L, Kuipers M, McGovern B (2016) Improving emergency department flow through optimized bed utilization. BMJ Open Quality 5(1):206156–2532

    Google Scholar 

  21. Khanna S, Sier D, Boyle J, Zeitz K (2016) Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emergency Medicine Australasia 28(2):164–170

  22. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV (2016) Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hospital Practice 44(5):252–259

    Article  Google Scholar 

  23. Wong HJ, Wu RC, Caesar M, Abrams H, Morra D (2010) Smoothing inpatient discharges decreases emergency department congestion: a system dynamics simulation model. Emer Med J 27(8):593–598

    Article  Google Scholar 

  24. Vermeulen MJ, Ray JG, Bell C, Cayen B, Stukel TA, Schull MJ (2009) Disequilibrium between admitted and discharged hospitalized patients affects emergency department length of stay. Ann Emer Med 54(6):794–804

    Article  Google Scholar 

  25. Fairley M, Scheinker D, Brandeau ML (2019) Improving the efficiency of the operating room environment with an optimization and machine learning model. Health Care Manage Sci 22(4):756–767

    Article  Google Scholar 

  26. Zenteno AC, Carnes T, Levi R, Daily BJ, Dunn PF (2016) Systematic OR block allocation at a large academic medical center. Ann Surg 264(6):973–981

    Article  Google Scholar 

  27. Mathews KS, Long EF (2015) A conceptual framework for improving critical care patient flow and bed use. Ann Am Thoracic Soc 12(6):886–894

    Article  Google Scholar 

  28. Kelen GD, Wolfe R, D’Onofrio G, Mills AM, Diercks D, Stern SA, Wadman MC, Sokolove PE (2021) Emergency department crowding: the canary in the health care system. NEJM Catalyst Innovations in Care Delivery 2(5)

  29. Baugh CW, Venkatesh AK, Bohan JS (2011) Emergency department observation units: a clinical and financial benefit for hospitals. Health Care Manage Rev 36(1):28–37

    Article  Google Scholar 

  30. Perry M, Franks N, Pitts SR, Moran TP, Osborne A, Peterson D, Ross MA (2021) The impact of emergency department observation units on a health system. Am J Emer Med 48:231–237

    Article  Google Scholar 

  31. Vrabec S, Oltmann SC, Clark N, Chen H, Sippel RS (2013) A short-stay unit for thyroidectomy patients increases discharge efficiency. J Surg Res 184(1):204–208

    Article  Google Scholar 

  32. Shaw SE, Preece R, Stenson KM, De Bruin JL, Loftus IM, Holt PJ, Patterson BO (2019) Short stay evar is safe and cost effective. European Journal of Vascular and Endovascular Surgery 57(3):368-373

    Article  Google Scholar 

  33. Hiltrop J (2014) Modeling neuroscience patient flow and inpatient bed management. Master’s thesis, Massachusetts Institute of Technology

  34. Team SimPy: SimPy documentation, version 3.0.10. https://simpy.readthedocs.io/en/3.0/

  35. Ragavan MV, Blinman TA, Fieldston ES (2013) Scheduled surgery admissions and occupancy at a children’s hospital: variation we can control to improve efficiency and value in health care delivery. Ann Surg 257(3):564–570

    Article  Google Scholar 

  36. McManus ML, Long MC, Cooper A, Mandell J, Berwick DM, Pagano M, Litvak E (2003) Variability in surgical caseload and access to intensive care services. The Journal of the American Society of Anesthesiologists 98(6):1491–1496

    Google Scholar 

  37. Kontopantelis E, Doran T, Springate D, Buchan I, Reeves D (2015) Interrupted time-series analysis: a regression based quasi-experimental approach for when randomisation is not an option. British Med J 350:2750

    Article  Google Scholar 

  38. Lopez Bernal J, Cummins S, Gasparrini A (2016) Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology 46(1):348-355

    Google Scholar 

  39. Hyndman RJ, Athanasopoulos G (2018) Forecasting: Principles and Practice. OTexts, available at: http://OTexts.com/fpp2/

  40. Lopez Bernal J, Cummins S, Gasparrini A (2018) The use of controls in interrupted time series studies of public health interventions. International Journal of Epidemiology 47(6):2082–2093

    Article  Google Scholar 

  41. Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 27:1–22

    Article  Google Scholar 

  42. Green LV (2002) How many hospital beds? INQUIRY: The Journal of Health Care Organization. Provision, and Financing 39(4):400–412

    Google Scholar 

  43. Akcali E, Côté M, Lin C (2006) A network flow approach to optimizing hospital bed capacity decisions. Health Care Management Science 9(4)

  44. Kane EM, Scheulen JJ, Püttgen A, Martinez D, Levin S, Bush BA, Huffman L, Jacobs MM, Rupani H, Efron DT (2019) Use of systems engineering to design a hospital command center. The Joint Commission Journal on Quality and Patient Safety 45(5):370–379

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Ann L. Prestipino and Peter L. Slavin, MD (Senior Vice President of MGH and President of MGH, respectively, at the time of this work) for their continuous support throughout this process. We are grateful to the nursing directors from the units who participated and contributed to the implementation of the strategy, and to Gianna Wilkins from Process Improvement for helping to map and document the new processes during implementation. Finally, we thank the editor and two anonymous reviewers for detailed feedback which has improved the completeness of the work herein.

Author information

Authors and Affiliations

  1. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aleida Braaksma, Elizabeth Ugarph & Retsef Levi

  2. Massachusetts General Hospital, Boston, MA, USA

    Martin S. Copenhaver, Ana C. Zenteno, Bethany J. Daily, Benjamin Orcutt, Kathryn M. Turcotte & Peter F. Dunn

  3. Harvard Medical School, Boston, MA, USA

    Martin S. Copenhaver & Peter F. Dunn

Authors
  1. Aleida Braaksma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin S. Copenhaver
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana C. Zenteno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth Ugarph
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Retsef Levi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bethany J. Daily
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Benjamin Orcutt
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kathryn M. Turcotte
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peter F. Dunn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Aleida Braaksma or Martin S. Copenhaver.

Ethics declarations

Competing interests

The authors do not declare any competing interests related to this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 364 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Braaksma, A., Copenhaver, M.S., Zenteno, A.C. et al. Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients. Health Care Manag Sci 26, 501–515 (2023). https://doi.org/10.1007/s10729-023-09638-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-023-09638-3

Keywords

Search

Navigation