Improvement of surgery duration estimation using ...

Improvement of surgery duration estimation using statistical methods and analysis of scheduling policies using discrete event simulation by Alexandra Blake Olsen

A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE Major: Industrial Engineering Program of Study Committee: Guiping Hu, Major Professor

Lizhi Wang Frank Montabon

Iowa State University Ames, Iowa 2015

Copyright ? Alexandra Blake Olsen. 2015. All rights reserved.

ii DEDICATION

This paper is dedicated to all of the people who have and will benefit from the United States health care system and their family and friends.

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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION.......................................................................................... 1 CHAPTER 2: STATISTICAL METHODS FOR SURGERY DURATION ESTIMATION....................................................................................................................... 11

Abstract ............................................................................................................................... 11 2.1 Introduction................................................................................................................... 11 2.2 Methods......................................................................................................................... 16 2.3 Case Study .................................................................................................................... 17

2.3.1 Comparison of Estimated and Actual Surgery Durations...................................... 17 2.3.2 Impact of Variables Using Multiple Linear Regression ........................................ 19 2.3.3 Results.................................................................................................................... 20 2.4 Conclusion .................................................................................................................... 25 CHAPTER 3: A DISCRETE EVENT SIMULATION OF A DAY'S SURGERY SCHEDULE........................................................................................................................... 27 Abstract ............................................................................................................................... 27 3.1 Introduction................................................................................................................... 27 3.2 Methods......................................................................................................................... 32 3.2.1 Process Mapping .................................................................................................... 32 3.2.2 Problem Statement ................................................................................................. 34 3.2.3 Arena Simulation ................................................................................................... 36 3.2.4 Model Validation ................................................................................................... 38 3.2.5 Scheduling Policy Comparison.............................................................................. 38 3.3 Results and Analysis ..................................................................................................... 39 3.4 Conclusion .................................................................................................................... 43 CHAPTER 4: CONCLUSION............................................................................................. 45 REFERENCES...................................................................................................................... 47

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LIST OF FIGURES

Figure 1: Surgery Scheduling Process ...................................................................................... 7 Figure 2: Add-on Scheduling Process....................................................................................... 8 Figure 3: Add-on Scheduling Process..................................................................................... 33 Figure 4: Timetable displaying the eight simulated cases ...................................................... 36 Figure 5: Snapshot of Arena model with descriptions of process .......................................... 37 Figure 6: Scenario comparison results .................................................................................... 40 Figure 7: Idle time scenario comparison................................................................................. 41

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LIST OF TABLES Table 1: t test results ............................................................................................................... 18 Table 2: R2 values ................................................................................................................... 21 Table 3: OR 04 Regression Results ........................................................................................ 22 Table 4: Esophag model with all surgeons ............................................................................. 24

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ACKNOWLEDGEMENTS I would like to thank my major professor, Dr. Guiping Hu, for all of her guidance throughout this process and for her willingness to learn about the health care industry alongside me. I appreciate her encouragement to pursue my interests and calming demeanor. Thank you, as well, to Lincoln Banwart for all of his work as an undergraduate research assistant. All the work he did was extremely helpful and saved me a significant amount of time. I would also like to thank the rest of my committee, Dr. Frank Montabon and Dr. Lizhi Wang, for their help and guidance. I appreciate all of the help Dr. Max Morris gave me in the statistical methods and Dr. Douglas Gemmill in Arena modeling. Without their help, the models would not have been as effective. Thank you, as well, to the rest of the faculty and staff in the IMSE department for creating an environment where it is fun to learn and students are encouraged to follow their passions. I would like to thank those at UnityPoint Health - Des Moines, including Val Boelman, Vanessa Calderon, Brandi Vennink, Tony Nurre, and Lacey Andrews, for their willingness to work with me. I appreciate all the time they took to teach me about their processes, provide data, answer questions, and provide feedback. Without their knowledge and willingness to share that knowledge, this project would not have been possible. I would also like to thank my peers and friends in the IMSE program for their support and making my experience at Iowa State University an enjoyable one. Lastly, I would like to thank my parents and fianc?, Kevin, for their unconditional love and support.

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ABSTRACT The United States health care system currently faces many challenges, with the most notable one being rising costs. In an effort to decrease those costs, health providers are aiming to improve efficiency in their operations. A primary source of revenue for hospitals and some clinics is the surgery department, making it a key department for improvement in efficiency. Surgery schedules drive the department and affect the operations of many other departments. The most significant challenge to creating an efficient surgery schedule is estimating surgery durations and scheduling cases in a manner that will minimize the time a surgery is off schedule and maximize utilization of resources. To identify ways to better estimate surgery durations, an analysis of the surgery scheduling process at UnityPoint Health - Des Moines, in Des Moines, Iowa was completed. Estimated surgery durations were compared to actual durations using a t test. Multiple linear regression models were created for the most common surgeries including the input variables of age of the patient, anesthesiologist, operating room (OR), number of residents, and day of the week. To find optimal scheduling policies, simulation models were created, each representing a series of surgery cases in one operating room during one day. Four scheduling policies were investigated: shortest estimated time first, longest estimated time first, most common surgery first, and adding an extra twenty minutes to each case in the existing order. The performance of the policies was compared to those of the existing schedule. Using the historical data from a one-year period at UnityPoint Health - Des Moines, the estimated surgery durations for the top four surgeries by count and top surgeons were found to be statistically different in 75% of the data sets. After creating multiple linear regression models for each of the top four surgeries and surgeons performing those surgeries, the values for each variable were compared across models. Age was found to have a minimal impact on surgery

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duration in all models. The binary variable indicating residents present, was found to have minimal impact as well. For the rest of the variables, consistencies were difficult to assess, making multiple linear regression an unideal method for identifying the impact of the variables investigated.

On the other hand, the simulation model proved to be useful in identifying useful scheduling policies. Eight series based on real series were modeled individually. Each model was validated against reality, with 75% of durations simulated in the models not being statistically different than reality. Each of the four scheduling policies was modeled for each series and the average minutes off schedule and idle time between cases were compared across models. Adding an extra twenty minutes to each case in the existing order resulted in the lowest minutes off schedule, but significantly increased the idle time between cases. Most common surgery first did not have a consistent impact on the performance indicators. Longest estimated time first did not improve the performance indicators in the majority of the cases. Shortest estimated time first resulted in the best performance for minutes off schedule and idle time between cases in combination; therefore, we recommend this policy is employed when the scheduling process allows.

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