Optimizing scheduling for outpatient clinics : a combination of developing a generic tool and immediate application

Hölscher, A.M. (2016) Optimizing scheduling for outpatient clinics : a combination of developing a generic tool and immediate application.

Abstract:Outpatient clinics* from many different departments cope with the problem that they have to slot in both new patients and follow up patients. In this project a method was developed to find the strategy that best optimizes the scheduling for these outpatient clinics. The method was applied to a selection of clinics from the Ophthalmology Department of the University Hospital of Wales, but kept as generic as possible. It needs to be started with determining the demand on the system. If the available historic data provides enough information, forecasting methods can be used. The best forecasting method for the specific time series needs to be determined. For the time series used in this project the best forecasting method turned out to be Holt’s Linear Exponential Smoothing. Unfortunately in our situation there was not enough data available to be sure of previous demand. Because of governmental targets however, new patients have recently been prioritized. Thus the historic appointment data for new patients only could be accurate as historic demand and forecasting could be used on only new patients. In case historic data does not provide enough information, a simulation, based on follow up structure, can be used to determine demand. This simulation was developed and carried out for our specific situation. An average monthly demand of 213 patients requesting an appointment was found, 45 new patients and 168 follow up patients. If forecasting new patient’s demand was possible, this can be used as a part of the input for this simulation. To be able to find the best allocation of capacity slots between new patients and follow up patients an optimization, minimizing total waiting time, can be carried out. Applying this optimization method for our situation provided us with an optimal allocation of 44 slots for new patients and 161 slots for follow up patients a month. Finally, the capacity can be implemented in the earlier mentioned simulation. This way, it can be analysed what happens to waiting times if the capacity is divided between new patients and follow up patients. In our situation it was concluded that the waiting times were distributed very unfairly. To make it fairer the Cardiff and Vale University Health Board can try a couple of solutions. It can be chosen to alter the optimal distribution of slots. This means total waiting time will increase but the waiting times can be distributed in a fairer way. If increasing total waiting time is not a possibility it can be chosen to increase total capacity. A new allocation of capacity can be determined by applying the optimization. The final capacity simulation can also be used to analyse the influence of other factors. In this project specific attention has been paid to the influence of dividing capacity over time. The waiting times with the current weekly master schedule for our situation were generated. It was concluded that in particular new patient waiting times can be decreased significantly by changing the weekly master schedule.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:http://purl.utwente.nl/essays/72099
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