The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Appointment planning and scheduling in primary care
Author:	Hoseini, Babak
View Online:	njit-etd2017-016 (xii, 122 pages ~ 7.0 MB pdf)
Department:	Department of Mechanical and Industrial Engineering
Degree:	Doctor of Philosophy
Program:	Industrial Engineering
Document Type:	Dissertation
Advisory Committee:	Cai, Wenbo (Committee chair) Abdel-Malek, Layek (Committee member) Das, Sanchoy K. (Committee member) Bladikas, Athanassios K. (Committee member) Shi, Junmin (Committee member)
Date:	2017-01
Keywords:	Healthcare systems Overbooking Stochastic scheduling Newsvendor Appointment planning Joint pannel optimization
Availability:	Unrestricted
Abstract:	The Affordable Care Act (ACA) puts greater emphasis on disease prevention and better quality of care; as a result, primary care is becoming a vital component in the health care system. However, long waits for the next available appointments and delays in doctors offices combined with no-shows and late cancellations have resulted in low efficiency and high costs. This dissertation develops an innovative stochastic model for patient planning and scheduling in order to reduce patients’ waiting time and optimize primary care providers’ utility. In order to facilitate access to patients who request a same-day appointment, a new appointment system is presented in which a proportion of capacity is reserved for urgent patients while the rest of the capacity is allocated to routine patients in advance. After the examination of the impact of no-shows on scheduling, a practical double-booking strategy is proposed to mitigate negative impacts of the no-show. Furthermore, proposed model demonstrates the specific circumstances under which each type of scheduling should be adopted by providers to reach higher utilization. Moreover, this dissertation extends the single physician’s model to a joint panel scheduling and investigates the efficiency of such systems on the urgent patients’ accessibility, the physicians’ utilization, and the patients’ waiting time. Incorporating the newsvendor approach and stochastic optimization, these models are robust and practical for planning and scheduling in primary care settings. All the analytical results are supported with numerical examples in order to provide better managerial insights for primary care providers.