Access Type

Open Access Dissertation

Date of Award

January 2012

Degree Type


Degree Name



Industrial and Manufacturing Engineering

First Advisor

Kai Yang


Operating Rooms (OR) are the most expensive care units in health care systems. In order for OR theatre to operate in cost efficient way, it is desirable that the ORs exhibit high utilization, while at the same time, maintain a low-level over-utilized OR time. At the operational level, there are many factors that could influence the OR utilization performances. The objective of this study is to develop effective approaches focusing on the most important factors that influence OR utilization to assist OR management in decision making to achieve better utilization and cost efficiency. In the study, model selection and cross-validation methods were used to find the best linear model of OR utilization given different subsets of the factors. As the scheduled utilization and case duration prediction accuracy were identified as the two most statistically significant factors, we then proposed a new distribution to approximate the total duration of surgery lists of multiple cases and compared its accuracy in the estimation of the probability of under- and over-run of surgery lists with the widely applied t-distribution. Monte Carlo simulation was used to validate the appropriateness of the proposed new distribution by comparing the percentiles of the empirical distribution of the duration of surgery lists with those calculated from the proposed distribution. The tardiness of case starts prohibit OR from achieving optimum efficiency as they causes over-utilized OR time and cancellations. Given limited resources, it is critical for the OR management to prioritize the tackling of tardiness. An iterative simulation method considering multiple delay reasons at a time was proposed that continuously identifies the top delay risks to facilitate proactive decision making to prevent tardiness from taking place. The effectiveness of this approach was examined through a case study by having different scheduling policies and case duration distributions. In the end, the behavioral pattern of OR staff was explored by constructing a structural equation model. Relationships among different variables and mean turnover time duration were estimated. It was found out that the work pace of OR staff during turnover times was not affected by the OR workload, and proved there was a psychological bias of OR staff to make decisions based on increasing clinical work per unit time during the hours they are assigned. This research complements current OR management study by introducing better and new methods for OR operational decision making.