Simulation and Optimization Models for Scheduling Multi-step Sequential Procedures in Nuclear Medicine



Journal Title

Journal ISSN

Volume Title



The rise in demand for specialized medical services in the U.S has been recognized as one of the contributors to increased health care costs. Nuclear medicine is a specialized service that uses relatively new technologies and radiopharmaceuticals with a short half-life for diagnosis and treatment of patients. Nuclear medicine procedures are multi-step and have to be performed under restrictive time constraints. Consequently, managing patients in nuclear medicine clinics is a challenging problem with little research attention. In this work we present simulation and optimization models for improving patient and resource scheduling in health care specialty clinics such as nuclear medicine departments. We rst derive a discrete event system speci cation (DEVS) simulation model for nuclear medicine patient service management that considers both patient and management perspectives. DEVS is a formal modeling and simulation framework based on dynamical systems theory and provides well de ned concepts for coupling components, hierarchical and modular model construction, and an object-oriented substrate supporting repository reuse. Secondly, we derive algorithms for scheduling nuclear medicine patients and resources and validate our algorithms using the simulation model. We obtain computational results that provide useful insights into patient service management in nuclear medicine. For example, the number of patients seen at the clinic during a year increases when a group of stations are reserved to serve procedures with higher demand. Finally, we derive a stochastic online scheduling (SOS) algorithm for patient and resource management in nuclear medicine clinics. The algorithm performs scheduling decisions by taking into account stochastic information about patient future arrivals. We compare the results obtained using the SOS algorithm with the algorithms that do not take into consideration stochastic information. The SOS algorithm provides a balanced utilization of resources and a 10% improvement in the number of patients served.