Business Faculty Publications
Title
A Hybrid Data Mining/Simulation Approach For Modelling Outpatient No-Shows In Clinic Scheduling
Document Type
Article
Publication Date
8-2009
Publication Title
Journal of the Operational Research Society
Keywords
Information Systems
Disciplines
Business | Management Information Systems
Abstract
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.
Recommended Citation
Glowacka, K J.; Henry, Raymond M.; and May, Jerrold H., "A Hybrid Data Mining/Simulation Approach For Modelling Outpatient No-Shows In Clinic Scheduling" (2009). Business Faculty Publications. 218.
https://engagedscholarship.csuohio.edu/bus_facpub/218
DOI
10.1057/jors.2008.177
Volume
60
Issue
8