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Problem

Located on Munson Medical Center’s main campus in Traverse City, MI, the Cowell Family Cancer Center is transforming cancer care in northern Michigan. In 2017, the center was recognized by the advocacy group Less Cancer for its leadership in cancer prevention efforts.

Prior to implementing iQueue for Infusion Centers, Munson’s scheduling process depended heavily on the charge nurse and took 2 to 4 hours each day to complete. Adding to the complexity was the fact that the schedule was actually maintained in two systems, which necessitated an extra step in the process to assure both schedules matched. Center staff lacked visibility beyond the day’s schedule in terms of foreseeing the work week ahead, which left the staff feeling helpless to plan ahead. As a result, the center often experienced days with extremely high patient volumes and a pace so frenetic that nurses would often miss lunches, stay late beyond the anticipated closing time, and in work in constant “survival mode”. Patient safety – especially during the peak hours between 10am and 2pm– remained an ever-present concern.

To improve conditions, center administrators tried changing the scheduling method from scheduling-by-nurse to scheduling-by-pod, restricting certain treatments (e.g. BCG) to certain days, reserving appointments for injections/port flushes, and shifting non-oncology patients to certain weekdays. Despite these efforts, the center did not see meaningful, lasting improvement.

Solution

Staff were eager to try the different approach iQueue for Infusion Centers offered because the scheduling methods from before were not working. After fine-tuning the initial templates during a “break-in” period, the staff was able to immediately feel the benefits. “Sometimes, I think the staff forget how far we’ve come with LeanTaaS,“ said Kate Swisher, Manger Nursing Services. “The days before were frenetic, missed lunches were common, and extended days happened frequently. Further, the nurses felt like they had no say in how the patients were scheduled or assigned to them. This has changed completely.”

Utilization Curve Before

Utilization Curve After

Results

14%
DECREASE IN
overall average
wait times
8%
INCREASE IN
volume without
additional chairs, staff
or operating hours
15%
Additional capacity
unlocked
27%
INCREASE IN
the frequency
of nurses leaving
on-time
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