Journal ArticleDOI
Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice
Diwakar Gupta,Lei Wang +1 more
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TLDR
A Markov decision process model is developed for the appointment-booking problem in which the patients' choice behavior is modeled explicitly and it is proved that the optimal policy is a threshold-type policy as long as the choice probabilities satisfy a weak condition.Abstract:
In addition to having uncertain patient arrivals, primary-care clinics also face uncertainty arising from patient choices. Patients have different perceptions of the acuity of their need, different time-of-day preferences, as well as different degrees of loyalty toward their designated primary-care provider (PCP). Advanced access systems are designed to reduce wait and increase satisfaction by allowing patients to choose either a same-day or a scheduled future appointment. However, the clinic must carefully manage patients' access to physicians' slots to balance the needs of those who book in advance and those who require a same-day appointment. On the one hand, scheduling too many appointments in advance can lead to capacity shortages when same-day requests arrive. On the other hand, scheduling too few appointments increases patients' wait time, patient-PCP mismatch, and the possibility of clinic slots going unused.
The capacity management problem facing the clinic is to decide which appointment requests to accept to maximize revenue. We develop a Markov decision process model for the appointment-booking problem in which the patients' choice behavior is modeled explicitly. When the clinic is served by a single physician, we prove that the optimal policy is a threshold-type policy as long as the choice probabilities satisfy a weak condition. For a multiple-doctor clinic, we partially characterize the structure of the optimal policy. We propose several heuristics and an upper bound. Numerical tests show that the two heuristics based on the partial characterization of the optimal policy are quite accurate. We also study the effect on the clinic's optimal profit of patients' loyalty to their PCPs, total clinic load, and load imbalance among physicians.read more
Citations
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Journal ArticleDOI
Appointment scheduling in health care: Challenges and opportunities
Diwakar Gupta,Brian T. Denton +1 more
TL;DR: A road map of the state of the art in the design of appointment management systems is provided and future opportunities for novel applications of IE/OR models are identified.
Journal ArticleDOI
Comparison Methods for Stochastic Models and Risks
TL;DR: The author does an admirable job of explaining the differences between Bayesian probability and the frequentist notion of probability, showing that, philosophically, only the Bayesian makes sense.
Journal ArticleDOI
Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities
Zuo-Jun Max Shen,Xuanming Su +1 more
TL;DR: In this article, the authors review current models of customer behavior in the revenue management and auction literatures and suggest several future research directions for customer behavior modeling in the operations management community.
Journal ArticleDOI
Dynamic Multipriority Patient Scheduling for a Diagnostic Resource
TL;DR: A method to dynamically schedule patients with different priorities to a diagnostic facility in a public health-care setting and the form of the optimal linear value function approximation and the resulting policy is presented.
Journal ArticleDOI
Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS
Peter J. H. Hulshof,Nikky Kortbeek,Nikky Kortbeek,Richard J. Boucherie,Erwin W. Hans,Piet J. M. Bakker +5 more
TL;DR: A comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision.
References
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Book
Discrete Choice Methods with Simulation
TL;DR: In this paper, the authors describe the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation, and compare simulation-assisted estimation procedures, including maximum simulated likelihood, method of simulated moments, and methods of simulated scores.
Book
Discrete Choice Analysis: Theory and Application to Travel Demand
Moshe Ben-Akiva,Steven R. Lerman +1 more
TL;DR: In this article, the authors present the methods of discrete choice analysis and their applications in the modeling of transportation systems and present a complete travel demand model system presented in chapter 11, which is intended as a graduate level text and a general professional reference.
Journal ArticleDOI
When choice is demotivating: Can one desire too much of a good thing?
Sheena S. Iyengar,Mark R. Lepper +1 more
TL;DR: The authors found that people are more likely to purchase gourmet jams or chocolates or to undertake optional class essay assignments when offered a limited array of 6 choices rather than a more extensive array of 24 or 30 choices.
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Appointment scheduling in health care: Challenges and opportunities
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