No More Waiting Around


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Lean Six Sigma accelerates response at medical call centers

Reducing call center wait time at five clinics

Wexner Medical Center is a multidisciplinary academic medical center on the main campus of The Ohio State University. General Internal Medicine (GIM) is a division within the Department of Internal Medicine that provides outpatient treatment. Patients calling GIM experienced unacceptable wait times, sometimes as high as 21 minutes. This resulted in patient dissatisfaction and often delays in patient care.

To fix this problem, GIM put together a core project team that included a senior from Ohio State’s Integrated Systems Engineering Department, GIM’s director and clinic managers. The project aimed to improve wait time by 15 percent and increase service level by 10 percent.

Queuing theory was used to determine staffing requirements during the peak periods at three of the five centers. The team determined the number by which each call center was short-staffed during peak periods. Based on the work sampling study during peak periods, the team found that workers spent about 30 percent of their time doing activities outside of answering phone calls, thus presenting an opportunity for 15 percent to 20 percent improvement.

At the pilot site, wait time reduced by 50 percent and service level increased by 25 percent. Additionally, calls with wait times greater than five minutes declined from 16 percent to 2 percent, greatly enhancing patient satisfaction at the pilot clinic.


The wait time, defined as “response time delay” in the system, was a metric measured on a daily basis by clinic managers. Response time delays were unacceptable to management and had significant swings on a day-to-day basis. Defect for this project was defined as any customer who waited for more than one minute.


The measure phase aimed to understand “choke periods” in 15-minute intervals where the demand on the system exceeded capacity. It included a work sampling study to measure how much time call center employees spent on various tasks.

One major challenge was procuring data from the information warehouse. The reports generated from the information warehouse used operational definitions inconsistently. The project needed data on response time delays in 15-minute intervals.


This phase’s focus was to determine optimal staffing levels and standardize work flow during peak hours. The team used the staffing model to build a user-friendly calculator that would help managers determine optimal staffing solutions on a quarterly basis. The calculator will use an Excel database to refresh data and feed it in the calculator to perform all queuing theory calculations and give optimal staffing level outputs.

An engineered forecast predicted that following the staffing strategy swung the response time metric favorably, sometimes as much as 15 percent during peak periods.


The solution entities encompassed three strategies, first, to staff the call centers based on the queuing model output mentioned in the analyze phase. It was expected to have the biggest impact from the other two.

Second, to defer the non-urgent tasks to non-peak hours, which is clustered as high, medium, and low priority work. The lead physician decided what was medium and low priority, and they should be performed when call volume was low.

The last solution was increasing the awareness of a Web-based portal, MyChart, that lets patients schedule appointments with doctors.

Those solutions were experimented for two months which resulting in a 51% improvement and its success led upper management to implement the solutions at the other four GIM clinics.


Ensuring the project’s sustainability is critical to maintain high patient satisfaction, which leads to enhanced access to care for Wexner Medical Center patients and increased referrals.