Increasing Operating Room Profits and Decreasing Wait Lists by Use of a Data-Driven Overbooking Model

Document Type : Research Paper


Health systems engineering, Faculty of Industrial and Systems Engineering, Tarbiat modares university, Tehran, Iran.


Background and Objectives: Operating rooms (ORs) are precious resources in hospitals, as they constitute more than 40% of the hospital revenues.As such, surgical cancellations are very costly to hospitals. Same-day surgery cancellations or no-shows were found to be the main contributing factor to underutilization of operating rooms (ORs) in a public-sector hospital despite the existence of long surgical wait lists.
Method: To demonstrate the feasibility of overbooking surgical procedures, a Monte-Carlo simulation model to predict unused OR time was built and validated using six months of historical data. We first fitted statistical distributions to the random parameters, using Easyfit©software. Then, a surgical case-mix optimization problem was formulated to prescribe the surgeries for overbooking that maximize the profits over the predicted unused OR time.   The optimization model was solved both deterministically based on average historical surgery durations using Lingo Software Package and heuristically by taking into account the random nature of surgical durations using Risk Solver Platform© add-on to Microsoft Excel.
Findings: We conducted simulation-optimization of the stochastic model for the three selected date. Results show significant improvements over the base case of no overbooking. The increases in the average surgical profits due to overbooking on three randomly selected dates were 89%, 36%, 93%.
Conclusions: To the best of our knowledge, this study is the first applied demonstration of descriptive, predictive, and prescriptive analytics to improve surgical operations through overbooking. The analysis shows significant opportunities in generating surgical profits (36%-93% on randomly selected validation dates) and reducing average wait times (by 4.16 weeks) without risking OR overtime.


  1. Denton, B., Viapiano, J. and Vogl, A. Optimization of surgery sequencing and scheduling decisions under uncertainty. Health care management science 2007; 10:13-24.
  2. Perroca, M.G., Jericó, M.D.C. and Facundin, S.D. Surgery cancelling at a teaching hospital: implications for cost management. Revista latino-americana de enfermagem 2007; 15: 1018-1024.
  3. Argo, J.L., Vick, C.C., Graham, L.A., Itani, K.M., Bishop, M.J. and Hawn, M.T. Elective surgical case cancellation in the Veterans Health Administration system: identifying areas for improvement. The American Journal of Surgery 2009; 198: 600-606.
  4.  Boudreau, S.A. and Gibson, M.J. Surgical cancellations: a review of elective surgery cancellations in a tertiary care pediatric institution. Journal of Perianesthesia Nursing 2011; 26: 315-322.
  5. Dimitriadis, P.A., Iyer, S. and Evgeniou, E. The challenge of cancellations on the day of surgery. International Journal of Surgery 2013; 11: 1126-1130.
  6. Smith, M.M., Mauermann, W.J., Cook, D.J., Hyder, J.A., Dearani, J.A. and Barbara, D.W. Same-day cancellation of cardiac surgery: a retrospective review at a large academic tertiary referral center. The Journal of thoracic and cardiovascular surgery 2014; 148: 721-725.
  7. Karnalkar, A. Cancellation of Elective Operations on the Day of Surgery: Three Years Study. Journal of Evidence based Medicine and Healthcare 2015; 44: 7847-7852.
  8. Kumar, R. and Gandhi, R. Reasons for cancellation of operation on the day of intended surgery in a multidisciplinary 500 bedded hospital. Journal of Anaesthesiology Clinical Pharmacology 2012; 28: 66.
  9. Kaddoum, R., Fadlallah, R., Hitti, E., Fadi, E.J. and El Eid, G. Causes of cancellations on the day of surgery at a Tertiary Teaching Hospital. BMC Health Services Research 2016; 216: 259.
  10. Amaruchkul, K. and Sae-Lim, P. Airline overbooking models with misspecification. Journal of Air Transport Management 2011; 17: 143-147.
  11. Chen, L. and Homem-de-Mello, T. Re-solving stochastic programming models for airline revenue management. Annals of Operations Research 2010; 177: 91-114.
  12. Lindenmeier, J. and Tscheulin, D.K. The effects of inventory control and denied boarding on customer satisfaction: The case of capacity-based airline revenue management. Tourism Management 2008; 29: 32-43.
  13. Klophaus, R. and Pölt, S. Airline overbooking with dynamic spoilage costs. Journal of Revenue and Pricing Management 2007; 6: 9-18.
  14. Suzuki, Y. The net benefit of airline overbooking. Transportation Research Part E: Logistics and Transportation Review 2006; 42: 1-19.
  15. Chatwin, R.E. Continuous-time airline overbooking with time-dependent fares and refunds. Transportation Science 1999; 33: 182-191.
  16. Belobaba, P.P. Survey paper—airline yield management an overview of seat inventory control. Transportation science 1987; 21:63-73.
  17. Rothstein, M. OR Forum—OR and the airline overbooking problem. Operations Research 1985; 33: 237-248.
  18. Toh, R.S. and Dekay, F. Hotel room-inventory management: an overbooking model. The Cornell Hotel and Restaurant Administration Quarterly 2002; 43: 79-90.
  19. Hwang, J. and Wen, L. The effect of perceived fairness toward hotel overbooking and compensation practices on customer loyalty. International Journal of Contemporary Hospitality Management 2009; 21: 659-675.
  20. Noone, B.M. and Lee, C.H. Hotel overbooking: the effect of overcompensation on customers’ reactions to denied service. Journal of Hospitality & Tourism Research 2011; 35: 334-357.
  21. Jenčková, J. and Abrhám, J. Smart Overbooking in the Accommodation Facilities in the Czech Republic. Journal of International Studies 2016; 9: 265-275.
  22. Weatherford, L.R. and Bodily, S.E. A taxonomy and research overview of perishable-asset revenue management: yield management, overbooking, and pricing. Operations Research 1992; 40: 831-844.
  23. Kim, S. and Giachetti, R.E. A stochastic mathematical appointment overbooking model for healthcare providers to improve profits. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2006; 36: 1211-1219.
  24. Parizi, M.S. and Ghate, A. Multi-class, multi-resource advance scheduling with no-shows, cancellations and overbooking. Computers & Operations Research 2016; 67: 90-101.
  25. LaGanga, L.R. and Lawrence, S.R. Clinic overbooking to improve patient access and increase provider productivity. Decision Sciences 2007; 38: 251-276.
  26. LaGanga, L.R. and Lawrence, S.R. Appointment overbooking in health care clinics to improve patient service and clinic performance. Production and Operations Management 2012; 21: 874-888.
  27. Huang, Y.L. Redefining Policies to Reduce the Negative Effects of Patient No-Show. IIE Annual Conference 2011.
  28. Kros, J., Dellana, S. and West, D. Overbooking increases patient access at East Carolina University's student health services clinic. Interfaces 2009; 39: 271-287.
  29. Muthuraman, K. and Lawley, M. A stochastic overbooking model for outpatient clinical scheduling with no-shows, IIE Transactions 2008; 40: 820-837.
  30. Daggy J, Lawly M, Willis D, Thayer D. Using no-show modeling to improve clinic performance. Health Informatics Journal 2010; 16: 246-259.
  31. Zhenghao F, Xiaolei X, Reynerio A. Sanchez and Xiang Z. Overbooking for Specialty Clinics with Patient No-Shows: A Queueing Approach, IEEE 14th International conference on Automation Science and Engineering (CASE) 2018; 20: 20-24.
  32. May FP, Reid MW, Cohen S, Dailey F, Spiegel BMR. Predictive overbooking and active recruitment increases uptake of endoscopy appointments among African American patients. Gastrointestinal Endoscopy 2016.
  33. Chiara A P, Domenic S. Using overbooking to manage no-shows in an Italian healthcare center. BMC Health Services Research 2018; 180-185.