Date of Graduation

5-2015

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor

Zhang, Shengfan

Abstract

The Walker Family Clinic in the Psychiatric Research Institute at the University of Arkansas for Medical Sciences in Little Rock, Arkansas provides general and specialty mental health and substance abuse services for adolescents and adults. As there is an increasing need for health services at the clinic, the current capacity may not be able to meet all demands. Patients may wait a long time before receiving care due to inefficiencies in the current system. Also, based on data collected from August 1, 2013 to November 26, 2014, the average daily no-show rate was 13.9% and the maximum daily no-show rate was 50%. No-shows have numerous adverse effects on healthcare clinics, such as financial costs. Both open-access and overbooking have been proven to help mitigate the adverse effects of no-shows at various clinics. In order to help combat the system inefficiencies at the WFC by reducing the wait time to first appointment and improving no-show rates, a decision support tool is proposed to help the WFC implement open-access scheduling in coordination with the existing method of fixed scheduling, as well as strategic overbooking practices. To achieve this goal, conclusions drawn about risk factors for no-shows from statistical analysis on patient appointment data were used to create a scenario tree and rank the scenarios by highest number of no-shows and highest probability of no-shows. Using Pareto analyses on these two lists, the lists were compared, and the scenarios that fit both lists were deemed “high risk of no-show,” with the remaining scenarios categorized as “low risk of no-show.” Using these separate groups of scenarios, additional statistical tests were conducted on the remaining factors initially found to be insignificant to no-show rates to determine if certain levels of these factors are more prominent in one of the groups of scenarios than another. In addition, a decision support tool was developed in Microsoft Excel that inputs the risk factors, finds the matching scenario in one of the two lists, and makes a recommendation of whether to schedule the patient using open-access scheduling/overbooking or the existing method of fixed scheduling.

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