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How to Lower Costs, Waiting Times for Colonoscopies

Colorectal cancer is a leading cause of cancer-related deaths in the United States, leading to over 50,000 fatalities every year. But it can be prevented with early screening using a procedure called a colonoscopy. Now researchers from North Carolina State University, Mayo Clinic and the University of Massachusetts at Amherst (UMass) have created a tool to help colonoscopy facilities operate more efficiently, ultimately lowering costs and leading to shorter waiting times for patients.

The researchers have created a computer model that “helps people who manage colonoscopy facilities, such as hospitals and clinics, find the best combination of physicians, staff, rooms and equipment needed to cater to the number of patients they can expect,” says Bjorn Berg, lead author of the paper outlining the new tool and a Ph.D. student in the Edward P. Fitts Department of Industrial & Systems Engineering at NC State. The model can also be used to determine the optimum number of patients a facility can see in any given day.

“Colonoscopy facility managers can try out different ideas in the model to see how they work before trying them in the real world – which is an expensive place to experiment,” says Dr. Brian Denton, an assistant professor of industrial and systems engineering at NC State and co-author of the paper. “For example, a manager could see whether it is worthwhile to hire another endoscopist who can perform colonoscopies, hire another nurse, or add another recovery bed for the facility.”

Denton explains that finding the right combination of staff, equipment and rooms can be particularly challenging for colonoscopy facilities because of uncertainties related to how long it takes to perform the procedure and how long it takes a patient to recover from it.

The model could be a boon for patients, because “it could lead to efficiency gains for practices,” Denton says, “and ultimately lower the cost for patients.” It also predicts the amount of time patients will spend waiting for the procedure, and can be used to improve scheduling.

The researchers utilized operations research methods to develop their model, which uses mathematics as a way of studying systems in order to make them more efficient and effective.  They are now working with University of North Carolina Hospitals to implement the model, and ultimately hope to make it available for general use.

The research, which was funded in part by the National Science Foundation, was co-authored by Berg and Denton from NC State, Dr. Hari Balasubramanian of UMass, and Dr. Heidi Nelson, Dr. Keith Lindor, Ahmed Rahman and Angela Bailey of Mayo Clinic. The paper, “A Discrete Event Simulation Model to Evaluate Operational Performance of a  Colonoscopy Suite,” was published online by the journal Medical Decision Making.


Note to editors: The study abstract follows.

“A Discrete Event Simulation Model to Evaluate Operational Performance of a Colonoscopy Suite”

Authors: Bjorn Berg, Brian Denton, North Carolina State University; Hari Balasubramanian, University of Massachusetts; Heidi Nelson, Keith Lindor, Ahmed Rahman and Angela Bailey, Mayo Clinic.

Published: Online September 2009, Medical Decision Making

Abstract: Background and Aims. Colorectal cancer, a leading cause of cancer death, is preventable with colonoscopic screening. Colonoscopy cost is high, and optimizing resource utilization for colonoscopy is important. This study’s aim is to evaluate resource allocation for optimal use of facilities for colonoscopy screening. Method. The authors used data from a computerized colonoscopy database to develop a discrete event simulation model of a colonoscopy suite. Operational configurations were compared by varying the number of endoscopists, procedure rooms, the patient arrival times, and procedure room turnaround time. Performance measures included the number of patients served during the clinic day and utilization of key resources. Further analysis included considering patient waiting time tradeoffs as well as the sensitivity of the system to procedure room turnaround time. Results. The maximum number of patients served is linearly related to the number of procedure rooms in the colonoscopy suite, with a fixed room to endoscopist ratio. Utilization of intake and recovery resources becomes more efficient as the number of procedure rooms increases, indicating the potential benefits of large colonoscopy suites. Procedure room turnaround time has a significant influence on patient throughput, procedure room utilization, and endoscopist utilization for varying ratios between 1:1 and 2:1 rooms per endoscopist. Finally, changes in the patient arrival schedule can reduce patient waiting time while not requiring a longer clinic day. Conclusions. Suite managers should keep a procedure room to endoscopist ratio between 1:1 and 2:1 while considering the utilization of related key resources as a decision factor as well. The sensitivity of the system to processes such as turnaround time should be evaluated before improvement efforts are made.