Evaluation of a Chemotherapy Advanced Preparation Pilot Program to Improve Throughput in an Oncology Infusion Clinic

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SAP Partners | Health System / Oncology | <b>Mayo Clinic</b>

Pharmacy workflow changes in the preparation of chemotherapy products can reduce patient chair time.

Background

Demand for outpatient cancer care access continues to increase as detection, identification, and treatments become more sensitive and patient-specific. Chair availability and treatment time remain predetermined variables within outpatient oncology clinics.

One technique used at other institutions focuses on pharmacy workflow, specifically utilizing advanced preparation of chemotherapy products.1-3 This technique has been found to decrease pharmacy turnaround time between 45% and 48.4%, with minimal medication and cost waste.4,5

Additionally, patient and nursing satisfaction is improved when pharmacy turnaround time is reduced.6 This decrease in chair time can result in additional time slots for patients.

This pilot focused on a pharmacy workflow change in the preparation of chemotherapy products. Prior to this pilot, chemotherapy products were not compounded until the oncology provider signed the orders and the nurse reviewed and released the orders upon the patient’s arrival.

Following the release of the orders, the pharmacist would verify them, and the products would be prepared for administration. During this pilot, selected chemotherapy and supportive care products were prepared prior to the nurse releasing the orders.

The pharmacist reviewed all provider signed orders the morning of treatment and verified orders that met all criteria for advanced preparation. The pilot was conducted to determine whether advanced preparation of specific regimens resulted in a significant decrease in patient chair time.

Methods

This pre-post quasi-experimental study compared pre-pilot patients who did not have their chemotherapy prepared in advance to post-implementation patients who had their chemotherapy prepared in advance. Treatment regimens included in this pilot were selected due to high utilization and low cost.

Selected regimens include azacitidine days 2 through 5, etoposide days 2 and 3, single-agent fluorouracil pumps, gemcitabine, fosaprepitant, FOLFOX, FOLFIRI, and FOLFIRINOX. Patient-specific criteria for advanced preparation included most recent body surface area within 8% of treatment plan body surface area and lab results within treatment plan parameters which were deemed appropriate based on pharmacist clinical judgement.

Eligible patients included patients 18 years of age and older with a malignant hematologic or solid tumor who receive care from Mayo Clinic Health System-Northwest Wisconsin Oncology Department and whose treatment included one of the previously listed medications or regimens.

Patients were excluded if labs were not done prior to day of treatment and if orders were unsigned by the provider at the time of pharmacist order review. Additionally, patients were excluded if lab results prior to treatment were not appropriate for treatment based on treatment plan parameters or pharmacist clinical judgement.

The primary outcome was total patient chair time, as measured from order release to medication administration stop time. Data from the electronic health record was used to calculate estimated total chair time.

Secondary outcomes include the total amount of medications wasted and the total cost of medications wasted.

For each regimen, a 2-sample t-test comparing the mean pre-pilot chair time to the post-implementation chair time was computed. The following table lists the desired mean difference to detect with 80% power (type I error=0.05) for each regimen with the required sample size in each period. The computations used standard deviations obtained from preliminary data.

It was expected, based on medication administration trends prior to pilot implementation, that the required sample sizes for statistical significance would be achievable within this 3-month pilot period, except for single-agent fluorouracil pumps. Single-agent fluorouracil is less commonly administered, and the calculated sample size was significantly larger compared to the other regimens.

The mean differences in time were selected to represent a significant reduction in patient chair time based on typical length of administration. A 15-minute difference was selected for single agent medication administrations, while a 30-minute difference was selected for chemotherapy regimens with multiple medications, as administration time is significantly longer for these regimens.

Results

The pre-pilot period from September 10, 2019, to December 31, 2019, was compared to the post-implementation period from September 15, 2020, to December 31, 2020. There were 545 regimens included in the pre-pilot data, whereas there were 281 regimens included in the post-implementation data.

The most common advanced prepared medication was fosaprepitant (N=171). Azacitidine, fosaprepitant, etoposide, fluorouracil, FOLFOX, and gemcitabine all achieved statistical significance (Table 2). The average amount of time saved for all regimens was 31.4 minutes.

During this pilot, only 6 products were wasted (Table 3). Fosaprepitant was the most wasted product (N=4). The total estimated cost of waste, based on average acquisition cost (AAC), during post-implementation was $326.

Discussion

Calculated sample sizes were not met for FOLFIRI, FOLFIRINOX, gemcitabine, and fluorouracil pumps. Despite this, all regimens achieved a clinically significant reduction in patient chair time. Additionally, minimal waste was identified through this process, which demonstrated that this could be a revenue generating workflow change as additional access can be created through a reduction in total patient chair time.

Fosaprepitant was the most wasted medication during the pilot. Daily fosaprepitant need was determined the morning of treatment, with the patient treatment schedule evaluated to determine the daily number of expected fosaprepitant doses needed.

The total quantity of needed fosaprepitant doses minus 1 were then prepared in advance for the day and stocked in the oncology clinic automated dispensing device. This dose preparation determination provided a buffer in case of treatment cancellation or patient no-show.

Despite this buffer, fosaprepitant was wasted most frequently because of patient cancellations and treatment plan changes. All other advanced prepared regimens were prepared as patient-specific products the morning of treatment based on the patient schedule for the day.

Limitations

This pilot was underpowered as FOLFIRI, FOLFIRINOX, gemcitabine, and fluorouracil did not meet sample size calculations. It was initially estimated that calculated sample sizes would be able to be met within the pilot period based on pre-pilot implementation data. However unsigned provider orders and lack of patient labs inhibited pharmacists’ ability to prepare in advance many eligible patients’ chemotherapy regimens.

It was noticed that many patients have labs drawn and then see the provider the same day as treatment. This workflow limited the volume of patients who could be captured in this pilot and hindered the pilot’s ability to reach the predetermined sample sizes.

Conclusion

Advanced preparation of chemotherapy products can significantly reduce patient chair time, which can result in additional availability of treatment time with the same volume of chairs and staffing resources. In conclusion, this process change contained cost from wasted products, while also significantly reducing patient chair time and as such, the pharmacy has continued with this process change.

References

1. Lamm MH, Eckel S, Daniels R, Amerine LB. Using lean principles to improve outpatient adult infusion clinic chemotherapy preparation turnaround times. Am J Health Syst Pharm. 2015;72:1138-1146. doi:10.2146/ajhp140453

2. Fonte G, Bolooki J, Gjolaj LN, et al. Successful use of problem-solving methodology to reduce pharmacy chemotherapy processing time. J Am Pharm Assoc (2003). 2020;60(6):e349-e356. doi:10.1016/j.japh.2020.07.012

3. Soh TIP, Tan YS, Hairom Z, et al. Improving wait times, for elective chemotherapy through pre-preparation: a quality-improvement project at the National University Cancer Institute of Singapore. J Oncol Pract. 2015;11(1):e89-e94. doi:10.1200/JOP.2014.000356

4. Blackmer J. (Man Blackmer J, Amoline K, Amanacher J, et al. Leveraging advanced preparation of oncology medications: decreasing turnaround-times in an outpatient infusion center. J Oncol Pharm Pract. 2021;27(6):1454-1460. doi: 10.1177/1078155220960222

5. Gupta A, Li J, Tawfik B, et al. Reducing wait time between admission and chemotherapy initiation. J Oncol Pract. 2018;14(5):e316-e323. doi:10.1200/JOP.17.00028

6. Kallen MA, Terrell JA, Lewis-Patterson P, Hwang JP. Improving wait time for chemotherapy in an outpatient clinic at a comprehensive cancer center. J Oncol Pract. 2012;8(1):e1-e7.doi:10.1200/JOP.2011.000281

Acknowledgement: Ross Dierkhising, MS

Disclosure statements: No conflicts of interest to report.

Funding source: None