Graduation Year

2006

Document Type

Thesis

Degree

M.S.E.M.

Degree Granting Department

Engineering Management

Major Professor

José L. Zayas-Castro, Ph.D.

Keywords

Healthcare, Clinic, Waiting time, Eigenvector method, Simulation

Abstract

Hospital ambulatory patients are seen in outpatient departments (OPDs) located in the hospital. 83.3 million visits were made to these departments in 2002. Many sources of patient waiting time exist including: poor coordination of information, inefficient scheduling, inaccurate time estimation and others. Well-designed and executed patient scheduling has the potential to remedy some of these problems. To properly schedule patients, variability in demand must be addressed. Patients may cancel appointments, arrive late and arrive without appointments. We address this problem based on a Multi-attribute Decision Making (MADM) approach. Decision models are developed using the Simple Additive Weighting (SAW) method to address scheduling decisions for late-arrival and walk-in patients and the operational decision of calling back patients from the waiting room.The models are developed as part of a case study at H.

Lee Moffitt Cancer Center and tested in a single-clinic computer simulation against the current clinic system decision process with respect to various performance measures.The proposed decision models successfully made walk-in and late patient scheduling decisions. The contributions of this research include identifying, defining and weighting of relevant decision making criteria at H. Lee Moffitt. Our decision models guaranteed all of the defined criteria are included every time a walk-in or late patient decision must be made. Based on the findings, implementation of the models with no reduction in number of patients would improve scheduling and operational decisions while not affecting clinic output measures.Using criteria to restrict the number of late and walk-in patients, on average, the clinic closed between 36.20 minutes and 47.95 minutes earlier. However, practitioner and room utilization suffered.

The tradeoff among number of patients seen, resource utilization, waiting time and clinic close time should be considered but cannot be fully assessed solely on the information gathered in this research. As a case study of H. Lee Moffitt Cancer Center, the decision models successfully incorporated all relevant patient criteria without adversely affecting the clinic system. Future research is needed to better understand what factors will impact system measures and expand the decision models to other outpatient clinic settings.

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