Abstract
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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Acknowledgments
The author wish to thank Wayne Stockburger of the Scott and White Hospital for providing access to the clinic and historical data. The first author wishes to acknowledge research support from the Sloan Foundation and the National GEM Consortium.
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Appendix
Appendix
The intuition behind the NMSOS algorithm is explained in this section. The example presented in Section 3 is used as a reference. Figure 1 illustrates a typical nuclear medicine schedule for two different nuclear medicine procedures. The procedures are scheduled as they arrive using an ‘as soon as possible’ strategy. The first procedure (CPT 78815) is scheduled at the beginning of the day and it is represented by white bars. Since some of the resources required for the second procedure (CPT 78465) are already occupied at the beginning of the day, the second procedure, in gray bars, is scheduled later in the day. In this example no other procedure can be added to the schedule without overlapping.
The NMSOS algorithm takes into account possible future procedure requests which allows for making more informed decisions when scheduling patients and resources under uncertainty. For instance, a different appointment schedule will be provided to the first procedure request (CPT 78815) if the uncertainty about future procedure requests is taken into account. Figure 11 shows the schedule for the first procedure request when the scenarios generated for the SIP model show a high probability of having multiple procedure requests of type CPT 78465 later in the day.
The schedule has the first patient request arriving later in the day when compared to the appointment presented in Fig. 1. The change in the appointment time provides more flexibility to the clinic in terms of scheduling more patients. In fact, the new schedule will allow the system to schedule multiple procedures requests of type CPT 78465 as presented in Fig. 12. The NMSOS algorithm provides appointment times and resource assignments that allows the clinic to schedule more patients in the long run.
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Pérez, E., Ntaimo, L., Malavé, C.O. et al. Stochastic online appointment scheduling of multi-step sequential procedures in nuclear medicine. Health Care Manag Sci 16, 281–299 (2013). https://doi.org/10.1007/s10729-013-9224-4
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DOI: https://doi.org/10.1007/s10729-013-9224-4