The increased demand for medical diagnosis procedures has been recognized as one of the contributors to the rise of health care costs in the U.S. in the last few years. Nuclear medicine is a subspecialty of radiology that uses advanced technology and radiopharmaceuticals for the diagnosis and treatment of medical conditions. Procedures in nuclear medicine require the use of radiopharmaceuticals, are multi-step, and have to be performed under strict time window constraints. These characteristics make the scheduling of patients and resources in nuclear medicine challenging. In this work, we derive a stochastic online scheduling algorithm for patient and resource scheduling in nuclear medicine departments which take into account the time constraints imposed by the decay of the radiopharmaceuticals and the stochastic nature of the system when scheduling patients. We report on a computational study of the new methodology applied to a real clinic. We use both patient and clinic performance measures in our study. The results show that the new method schedules about 600 more patients per year on average than a scheduling policy that was used in practice by improving the way limited resources are managed at the clinic. The new methodology finds the best start time and resources to be used for each appointment. Furthermore, the new method decreases patient waiting time for an appointment by about two days on average.

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Increased demand for specialized healthcare services has been identified as one of the causes of increased healthcare costs in the US. Nuclear medicine, a sub-specialty of radiology, uses relatively new technology to diagnose and treat patients. Procedures (tests) in nuclear medicine require the use of radiopharmaceuticals with a limited half-life and involve several steps that are constrained by strict time windows and require multiple resources for completion. Consequently, managing patient service in nuclear medicine is a very challenging problem that has received very little research attention. In this paper, we present a discrete event system specification (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-defined concepts for coupling components, hierarchical and modular model construction, and an object-oriented substrate supporting repository reuse. We report on simulation results based on historical data using both patient and management performance measures. The results provide useful insights regarding the management of patient service in nuclear medicine. While this work focuses on nuclear medicine, results will find generality in other healthcare settings.