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iQueue for Infusion Centers Case Study – Penn Medicine

Ranked as Pennsylvania’s #1 health system, Penn Medicine is a world- renowned academic medical center in Philadelphia that combines education, research, and clinical care to provide the best possible patient care. Penn Medicine’s Abramson Cancer Center Infusion Suite at the Perelman Center for Advanced Medicine sees over 50,000 infusion visits each year and is one of 72 elite NCI-designated Cancer Centers in the entire country and one of 33 NCCN Member Institutions.

Problem

  • History of patient and staff dissatisfaction with long wait times
  • Nurses feeling rushed and pressured to perform essential functions such as documentation and education, because of uneven schedules and the way patients arrive throughout the day
  • Extended wait times especially in the middle of the day

Solution

Leadership at the cancer center initially deployed iQueue for Infusion Centers on its 4th floor unit with 21 chairs to optimize their scheduling templates, provide daily management guidance about what to expect each day, and to understand why days did not go as planned. The center achieved outstanding results at this pilot location as shown below, and as a result, leadership extended the use of iQueue for Infusion Centers to many of its other floors and locations, bringing the total number of chairs managed through the solution to 181.

Utilization Curve Before

Penn before curve
  • Frequent “mid-day” peaks and slow mornings and evenings
  • Frequent overflow in waiting rooms – long patient waiting times

Utilization Curve After

Penn after curve
  • Even workload throughout the day allows for more predictable schedules
  • Unlock capacity to help deal with unexpected delays and add-ons

Results

The following results were calculated by comparing the six month period post-iQueue launch to the analogous six months in the previous calendar year.

0%
Increase in Patient Volumes
0%
Increase in Patient Hours
0%
Decrease in Average Wait Times
0%
Decrease in Average Wait Times During Peak Hours
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