A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits

This paper develops a stochastic mathematical overbooking model (SMOM) for determining the optimal number of patient appointments to accept to maximize expected total profits for diverse healthcare environments. Overbooking is necessary to alleviate the detrimental effects of no-shows that are experienced by healthcare providers. Compared with traditional simple deterministic overbooking approaches, SMOM is unique since it considers the probability distributions of no-shows and walk-ins to obtain the optimal solution. Usually, healthcare providers would only have no-show data based on their current practices, so the authors provide a method to extrapolate the conditional probability to estimate what happens when overbooking occurs. SMOM is then compared with two alternative strategies: the base case of no overbooking and the naive statistical overbooking approach (NSOA) that simply adds the mean number of no-shows minus the mean number of walk-ins to the number of appointments to accept. It is shown using data collected for 59 physicians in a medical clinic that SMOM compared with the base case can increase profits by 43.72% on average whereas NSOA improves profits by 29.66% on average. Sensitivity analyses demonstrate SMOM is robust under a diversity of healthcare environments and cost structures

[1]  M. Murray,et al.  Redefining open access to primary care. , 1999, Managed care quarterly.

[2]  K. Littlewood..  Special Issue Papers: Forecasting and control of passenger bookings , 2005 .

[3]  V. Pesata,et al.  A descriptive study of missed appointments: families' perceptions of barriers to care. , 1999, Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & Practitioners.

[4]  Rex S. Toh,et al.  Hotel room-inventory management: an overbooking model. , 2002 .

[5]  Samuel E. Bodily,et al.  A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing , 1992, Oper. Res..

[6]  P. Pfeifer,et al.  The Airline Discount Fare Allocation Problem , 1989 .

[7]  Richard E. Chatwin,et al.  Continuous-Time Airline Overbooking with Time-Dependent Fares and Refunds , 1999, Transp. Sci..

[8]  Samuel E. Bodily,et al.  Overbooking decision rules , 1992 .

[9]  Philip J. Tuso,et al.  The Easy Access Program: A Way to Reduce Patient No-Show Rate, Decrease Add-Ons to Primary Care Schedules, and Improve Patient Satisfaction , 1999 .

[10]  J. Talaga,et al.  Appointment breaking: causes and solutions. , 1992, Journal of health care marketing.

[11]  C. G. Moore,et al.  Time and money: effects of no-shows at a family practice residency clinic. , 2001, Family medicine.

[12]  Edward J. Rising,et al.  A Systems Analysis of a University-Health-Service Outpatient Clinic , 1973, Oper. Res..

[13]  M. Nettleman,et al.  Patient education and emergency room visits. , 2000, Clinical performance and quality health care.

[14]  Barry C. Smith,et al.  Yield Management at American Airlines , 1992 .

[15]  E. Shlifer,et al.  An Airline Overbooking Policy , 1975 .

[16]  R. Javalgi,et al.  Tackling no-show behavior: a market-driven approach. , 1998, Health marketing quarterly.

[17]  K Terry.  Managed care: could you live without it? , 2001, Medical economics.

[18]  Jeffrey I. McGill,et al.  Revenue Management: Research Overview and Prospects , 1999, Transp. Sci..

[19]  A Murdock,et al.  Why Do Patients not Keep their Appointments? Prospective Study in a Gastroenterology Outpatient Clinic , 2002 .

[20]  Marvin Rothstein,et al.  OR Forum - OR and the Airline Overbooking Problem , 1985, Oper. Res..

[21]  Bryan Smith,et al.  Making the tough choices for cost control. , 2004, Healthcare financial management : journal of the Healthcare Financial Management Association.