A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling
2009
J Oper Res Soc
8
60
1056-1068
Journal_Article
Appointments_and_Schedules||Outpatient_Clinics
Simulation
Tactical
Yes
No
Article Link
Glowacka, K. J. H., R. M.; May, J. H. (2009). A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling. [Journal Article]. J Oper Res Soc, 60(8), 1056-1068.
This paper considers the outpatient no-show problem faced by a rural free clinic located in the south-eastern United States. Using data mining and simulation techniques, we develop sequencing schemes for patients, in order to optimize a combination of performance measures used at the clinic. We utilize association rule mining (ARM) to build a model for predicting patient no-shows; and then use a set covering optimization method to derive three manageable sets of rules for patient sequencing. Simulation is used to determine the optimal number of patients and to evaluate the models. The ARM technique presented here results in significant improvements over models that do not employ rules, supporting the conjecture that, when dealing with noisy data such as in an outpatient clinic, extracting partial patterns, as is done by ARM, can be of significant value for simulation modelling.