Maximizing Efficacy in the Infusion Unit: Making Patients and Nurses Happy

Oncology nurses in the University of Miami Health System were able to significantly increase efficiency, reduce waiting time, and treat more patients each day in their outpatient oncology infusion suite by revamping scheduling and using a master template. They described their success at the National Comprehensive Cancer Network 20th Annual Conference, held recently in Hollywood, Florida.

“We were in a state of chaos. We looked at the needs of our nurses, and what our patients were saying, and, while there are always constraints in the system, we knew we had to make things better,” said Angela Olier-Pino, DNP, MBA, RN.

First author Gloria G. Campos, MSIE, an industrial engineer, explained, “We were trying to make sure we aligned treatment durations to how they were reflected on the schedule. We investigated patient waiting times and learned that their appointment times were not aligning with the times that patients were arriving. We put together a multidisciplinary team to review these time blocks, to determine what each treatment looked like.”

The group’s goal was to ensure that capacity matched scheduled treatment times. They wanted to reduce patient wait time (from arrival to treatment chair) and diminish the variance between the actual and scheduled chair times. By improving the scheduling of patient chair time, the nurses could better manage their resources and nursing capacity, and this would consequently reduce patient wait times.

A multidisciplinary team of nurses and pharmacists standardized the scheduling guidelines to approximate treatment chair time for patients and nursing acuity (ie, difficulty). They did this by examining nursing documentation and the time required for infusion, postinfusion monitoring, patient education, pharmacy and laboratory turnaround, medication and its reconstitution, and preinfusion examinations (such as echocardiograms).

They developed master templates to operationalize the scheduling guidelines, which ensured that capacity was maximized; four templates hosted 30-minute slots, with staggered start and end times. Nursing schedules were aligned to match the maximum capacity in the templates.

Improvements on Multiple Measures

Success was evaluated based on the impact on 3 key measures, comparing outcomes at baseline and after implementation. The first was scheduled versus actual treatment duration, for which the mean and the variance decreased significantly. This indicated less tendency to overestimate the duration of treatment and improved predictability, which optimized the use of resources.

“We were overestimating treatment durations by 20 minutes,” Campos noted. At baseline, the difference between scheduled versus actual treatment time was 21 minutes, which dropped to less than 10 minutes after implementation of the program, a 53% difference.

The second measure was the percentage of patients whose actual treatment duration was within 30 minutes of the scheduled treatment duration. Patients whose treatment length (total time in the chair) was predicted within a 30-minute margin of error increased significantly, allowing nurses to utilize chair time more accurately.

At baseline, 31% of patients had a treatment time that varied by 30 minutes or so from the predicted treatment duration. This dropped to 16% after implementation, a 50% difference.

The third measure was patient wait times (arrival time to the chair), which was reduced by more than 15 minutes. This increased the predictability of the total treatment duration, which allowed for timely use of resources.

The mean wait time was 45 minutes at baseline, dropping to 30 minutes under the new program, a 33% improvement per day per patient.

Better scheduling and efficiency allowed the nurses to treat 40 additional patients per day. The unit averaged 60 patients at baseline, but it now averages 100 within the same number of hours, with the same number of chairs, and with the same average of 17 nurses per day.

More Predictability Means Nurses Are Happier

Lauren Gjolaj, MBA, BSN, RN, added that the new structure makes the nurses’ workday more predictable, and while more patients are being treated the workload does not seem greater.

“The chaos has been reduced,” she said. “Before, patients were scheduled incorrectly, creating hours of crunch time and hours of downtime. The manner in which we see patients is more stable.”

The nurses staggered the infusion start and end times to ensure that chairs remained filled, and they matched nurses’ schedules to the start and end times to ensure the optimal number of staff for patients. They also figured nursing acuity into the scheduling. “The staff doing the scheduling were not all clinical, and we were not all speaking the same language, so we talked to the schedulers about how to best handle the different situations,” Gjolaj said. “This helped them make better decisions when filling in the [scheduling] template.”

Under the new system, the schedule was based not only on the number of patients per day per nurse, but on the acuity of the individual patient and regimen. This assured equal distribution of work among the nurses.

Olier-Pino added that the number of patients and nurses is now more appropriate, and nurses are rarely being asked to extend their shifts. “We were having to beg nurses to stay and to pay overtime,” she said. “Morale is much better now.”

Reference

Campos GG, et al. Improving efficiency and capacity via cancer treatment scheduling standardization. Presented at: National Comprehensive Cancer Network 20th Annual Conference; March 12-14, 2015; Hollywood, FL.