The Timing of Staffing Decisions in Hospital Operating Rooms: Incorporating Workload Heterogeneity into the Newsvendor Problem

Published Online:https://doi.org/10.1287/msom.1110.0350

References

  • American Hospital Association American Hospital Association hospital statistics. Health Forum (2008) 6–7Google Scholar
  • Besbes O., Phillips R., Zeevi A. Testing the validity of a demand model: An operations perspective. Manufacturing Service Oper. Management (2010) 12(1):162–183LinkGoogle Scholar
  • Bradford J. W., Sugrue P. K. A Bayesian approach to the two-period style-goods inventory problem with single replenishment and heterogeneous Poisson demands. J. Oper. Res. Soc. (1990) 41(3):211–218Google Scholar
  • Centers for Medicare and Medicaid Services National health expenditure projections 2008–2018. (2009) . Centers for Medicare and Medicaid Services, U.S. Department of Health & Human Services, BaltimoreGoogle Scholar
  • Deshpande V., Arikan M. Are airlines newsvendors? Or, an empirical estimation of the impact of airline flight schedules on flight delays. (2007) . Working paper, Purdue University, West Lafayette, INGoogle Scholar
  • Dexter F., Ledolter J. Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data. Anesthesiology (2005) 103(6):1259–1267CrossrefGoogle Scholar
  • Dexter F., Traub R. D., Qian F. Comparison of statistical methods to predict the time to complete a series of surgical cases. J. Clinical Monitoring Comput. (1999) 15(1):45–51CrossrefGoogle Scholar
  • Dexter F., Traub R. D., Macario A. How to release allocated operating room time to increase efficiency: Predicting which surgical service will have the most underutilized operating room time. Anesth. Analg. (2003a) 96(2):507–512CrossrefGoogle Scholar
  • Dexter F., Abouleish A. E., Epstein R. H., Whitten C. W., Lubarsky D. A. Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesth. Analg. (2003b) 97(4):1119–1126CrossrefGoogle Scholar
  • Epstein R. H., Dexter F. Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity. Anesth Analg. (2002) 94(3):640–643CrossrefGoogle Scholar
  • Gallego G., Moon I. The distribution-free newsvendor problem: Review and extensions. J. Oper. Res. Soc. (1993) 44(8):825–834CrossrefGoogle Scholar
  • Gaur V., Kesavan S., Raman A., Fisher M. L. Estimating demand uncertainty using judgmental forecasts. Manufacturing Service Oper. Management (2007) 9(4):480–491LinkGoogle Scholar
  • Green L. V., Brandeau M. L., Sainfort F., Pierskalla W. P. Capacity planning and management in hospitals. Operations Research and Health Care: A Handbook of Methods and Applications (2004) (Kluwer Academic, Norwell, MA) 15–42Google Scholar
  • Greene W. H.Econometric Analysis (2007) 6th ed.(Prentice Hall, Upper Saddle River, NJ) 156–175Google Scholar
  • Hasija S., Pinker E., Shumsky R. A. OM practice—Work expands to fill the time available: Capacity estimation and staffing under Parkinson's law. Manufacturing Service Oper. Management (2010) 12(1):1–18LinkGoogle Scholar
  • Hastie T., Tibshirani R., Friedman J. H.The Elements of Statistical Learning (2001) (Springer, New York) 193–196CrossrefGoogle Scholar
  • Hill R. M. Applying Bayesian methodology with a uniform prior to the single period inventory mode. Eur. J. Oper. Res. (1997) 98(3):555–562CrossrefGoogle Scholar
  • Kc D. S., Terwiesch C. Impact of workload on service time and patient safety: An econometric analysis of hospital operations. Management Sci. (2009) 55(9):1486–1498LinkGoogle Scholar
  • Koenker R., Bassett G. Regression quantiles. Econometrica (1978) 46(1):33–50CrossrefGoogle Scholar
  • Liyanage L. H., Shanthikumar J. G. A practical inventory control policy using operational statistics. Oper. Res. Lett. (2005) 33(4):341–348CrossrefGoogle Scholar
  • Macario A., Dexter F. Estimating the duration of a case when the surgeon has not recently scheduled the procedure at the surgical suite. Anesth. Analg. (1999) 89(5):1241–1245CrossrefGoogle Scholar
  • Macario A., Vitez T. S., Dunn B., Tom M. Where are the costs in perioperative care? Analysis of hospital costs and charges for inpatient surgical care. Anesthesiology (1995) 83(6):1138–1144CrossrefGoogle Scholar
  • May J., Strum D., Vargas L. Fitting the lognormal distribution to surgical procedure times. Decision Sci. (2000) 31(1):129–148CrossrefGoogle Scholar
  • McIntosh C., Dexter F., Epstein R. H. The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: A tutorial using data from an Australian hospital. Anesth. Analg. (2006) 103(6):1499–1516CrossrefGoogle Scholar
  • Naddor E. Sensitivity to distributions in inventory systems. Management Sci. (1978) 24(16):1769–1772LinkGoogle Scholar
  • Olivares M., Terwiesch C., Cassorla L. Structural estimation of the newsvendor model: An application to reserving operating room time. Management Sci. (2008) 54(1):41–55LinkGoogle Scholar
  • O'Neill L., Dexter F., Brandeau M. L., Sainfort F., Pierskalla W. P. Evaluating the efficiency of hospitals' perioperative services using DEA. Operations Research and Health Care: A Handbook of Methods and Applications (2004) (Kluwer Academic, Norwell, MA) 147–168Google Scholar
  • Perakis G., Roels G. Regret in the newsvendor model with partial information. Oper. Res. (2008) 56(1):188–203LinkGoogle Scholar
  • Scarf H. E., Arrow K. J., Karlin S., Scarf H. E. A min-max solution to an inventory problem. Studies in Mathematical Theory of Inventory and Production (1958) (Stanford University Press, Stanford, CA) 201–209Google Scholar
  • Shih W. A note on Bayesian approach to newsboy inventory problem. Decision Sci. (1973) 4(2):184–189CrossrefGoogle Scholar
  • Strum D., May J., Vargas L. Modeling the uncertainty of surgical procedure times. Anesthesiology (2000a) 92(4):1160–1167CrossrefGoogle Scholar
  • Strum D., Sampson A., May J., Vargas L. Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology (2000b) 92(5):1454–1466CrossrefGoogle Scholar
  • Strum D. P., Vargas L. G., May J. H., Bashein G. Surgical suite utilization and capacity planning: A minimal cost analysis model. J. Medical Systems (1997) 21(5):309–322CrossrefGoogle Scholar
  • Wachtel R. E., Dexter F. Tactical increases in operating room block time for capacity planning should not be based on utilization. Anesth. Analg. (2008) 106(1):215–222CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.