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Maximize cancer center capacity to improve infusion throughput and reduce wait times

Webinar Write Up: How Michigan Medicine’s Rogel Cancer Center found more capacity in fewer chairs

At the LeanTaaS Transform Winter 2021 event, University of Michigan Rogel Cancer Center’s Suzanne Burke, RN, BSN, Clinical Nurse Supervisor, University of Michigan Rogel Cancer Center, and Barbara Walters, MSA, PMP, Senior Project Manager, presented with LeanTaaS’ Akanksha Shukla, Senior Product Manager, iQueue for Infusion Centers, on using digital tools and a new approach to successfully maximize cancer center capacity. 

Missed the event? View the whole webinar, or read a transcript

The context:
Academic medical center Michigan Medicine is one of the largest healthcare complexes in the state. Their outreach, research, and patient care have ranked among the best in the nation in a broad range of adult and pediatric specialties. According to US News and World Report, they are ranked number 11 in the nation and number one in Michigan. Their health system consists of: 

  • Three hospitals and 125 clinics
  • 1000 licensed beds 
  • 2.6 million annual outpatient clinic visits

Michigan Medicine is part of the ANCC magnet recognition program. Michigan’s Rogel Cancer Center has: 

  • 27 disease-specialized oncology clinics 
  • Over 300 faculty members who provide over 140,000 outpatient visits annually 
  • Infusion clinics that provide chemotherapy and clinical trials for over 80,000 patients annually

The Rogel Cancer Center has earned national recognition from oncology quality organizations, including National Comprehensive Cancer Network.  

Rogel needed to maximize cancer center capacity and mitigate the operational and financial impact of COVID-19. Their adult infusion center was already experiencing a variety of issues prior to COVID, and the pandemic caused additional operational challenges. A main one of these was excessive overbooking, especially during the highly desirable 10am to 2pm timeframe, which in turn caused lengthy patient wait times. At these times the pharmacy struggled to keep up with demand. 

Despite operating all the way from 7:30am to 8:30pm daily, Rogel’s infusion centers saw extremely underutilized morning and afternoon hours, even with adequate nursing staffing. As the pandemic emerged, the need for social distancing in their infusion areas also caused them to lose valuable treatment chairs.  They struggled to accommodate the 10-14% growth in infusion patient volume they saw. 

Further pain points included lack of visibility into infusion center diagnostics, limited infusion schedule optimization available from their EMR, and a need for strategic focus to achieve Medicare profitability.  

What Michigan Medicine Rogel Cancer Center did: 

Michigan Medicine partnered with LeanTaaS’ iQueue for Infusion Centers to unlock and maximize cancer center capacity. Using the iQueue diagnostic tools, they were able to see where opportunities existed to schedule patients, or where there were challenges due the way that they were scheduling.

Capacity amounts to the total number of chairs multiplied by the total number of operating hours in a day.  But this number is not practically applicable, considering operational factors like nurse staffing, pharmacy, prop time, lab turnaround times, etc.  All these operational limiting factors will cause a center to have a ramp up in the morning and a ramp down in the evening. Using machine learning and predictive analytics, centers can account for other aspects which can be managed. 

One of these is same-day changes to scheduled appointments, which can either open  chairs during times when the center was scheduled to run out of chairs, or very quickly fill in previously available chairs.  

Using iQueue, Rogel was able to analyze historical data and so predict the daily add-on, no-show, and same-day cancel patterns for the center. Patients arriving early or late for their appointments can also impact capacity. Analyzing that historical arrival pattern allowed iQueue to learn to predict when a chair is scheduled to be used versus when it will actually be used.

Knowing these different rates of occurrences, and the degree to which they impact operations, is key to developing a machine learning model that maximizes utilization and unlocks previously unused capacity. For the Rogel Cancer Center, the iQueue template generation algorithm married the center’s true capacity with the unpredictability to generate strategic template designs.

Barbara Walters credits iQueue for being able to meet Rogel’s volume needs even with fewer chairs. She explained, “Reviewing our ever-changing data, we determined the best ways to optimize our templates. LeanTaaS provides ongoing support to the team throughout any kind of issues that arise.”

The results: 

Using iQueue for infusion Centers, Michigan Medicine Rogel Cancer Center realized strategic scheduling improvements with the following results:

  • 20% fewer chairs – 43 vs. 52 chairs used
  • 14% increase in the number of appointments per chair – 346 vs. 303
  • 7 added appointments per chair per month

Nurses are also working more steadily throughout the day with fewer ebbs and flows. There is a more consistent use of morning and evening hours, and a more predictable, sustainable workflow for pharmacy.

Looking ahead:

Michigan Medicine sees opportunities to continue to improve infusion center capacity with the following initiatives:

  • Expansion within Rogel, with additional chairs to open strategically
  • Additional site expansions
  • Sending schedule changes to iQueue in real-time
  • Smoothing schedules through the week, based on provider/clinic days
  • Ongoing training for new scheduling staff

What others can learn: 

Michigan Medicine sees the partnership with LeanTaaS as key to their continued success using iQueue for Infusion Center tools. 

  • Continuously collaborate. The LeanTaaS and Michigan Medicine team meet every other week to continue validating operational metrics, and discussing successes and challenges. The application continues to support them in their scheduling decisions. 
  • Machine learning and optimization isn’t a one-time event. Once the strategy and the template design is in place, it is important to have access to forward-looking tools to continually plan and know what is coming for approaching days and weeks. It’s possible to learn from the data and adapt staff scheduling and operating hours. Having dedicated data science tools at infusion center staff’s fingertips can help a center operate at its fullest.

For more details on Rogel Cancer Center’s journey to unlock capacity, view the whole Transform session here.

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