Westwood Treatment is a 39-chair infusion department that specializes in bio, chemo, and immunotherapies for patients of the University of Kansas Cancer Center. Increasing patient demand and complexity, as well as limitations on resources, has driven cancer center leadership to look at opportunities to better manage the needs of our customers and discern how to better accommodate the growing volume.

Using an optimized scheduling templates which ensured the appropriate number of patient starts, at any given time of day, with both maximum resource utilization and patient acuity dispersion. These optimized templates had to be built into the Cadence scheduling module of the EMR. Template ownership was assigned to the treatment nursing and clinical staff (rather than scheduling staff) who oversaw both the original build and continued maintenance.

Building those templates in EMR required novel utilization of the Cadence module, which was a significant departure from prior infusion scheduling practices. Rather than having 39 separate infusion chair resources assigned to the department, instead, a single department-wide resource was created which would represent all 39 chairs. Combinations of slot openings, appointment blocks, and visit type restrictions were then employed to guarantee the appropriate number of patient starts in accordance with the optimized templates. Schedulers required thorough re-education, training, and at-elbow support to become comfortable navigating the new look and feel of infusion scheduling.
A nursing allocation tool was developed within the predictive scheduling application. This tool provided for the automatic assignment of patients to nurses based upon certain safety constraints (including patient acuity as well as limitations on number of first treatment patients, the overall number of patient starts, and the number of hypersensitivity risk medications at any given time). The nursing allocation tool allowed for easily transparent color-coding to ensure that patient acuity was easily identifiable.
Following these measures, a go-live date was set and patients began arriving into the optimized templates on January 7, 2019. After feedback from the nursing and clinical treatment team following the initial go-live, templates underwent further optimization, with additional adjustments and modifications, and were implemented on March 11, 2019.

A period of 1/7/2019 – 6/1/2019 was compared to the same period of the prior year (1/8/2018 – 6/2/2018) to determine success of outcomes. All analyses were limited to weekdays, as no substantial changes were made to weekend templates.

Daily average completed appointments increased by 16.3% (or roughly 16 patients per day), while daily average scheduled hours increased by 9.63% (or roughly 29 treatment hours). Same-day add-ons increased by 38.9% (or roughly 1.5 patients per day).

It was important to follow several balancing measures to ensure that increases in utilization did not negatively impact patient care, and (despite the observed increases in utilization) chair wait time (or time between check-in and rooming) decreased by 14.5%, drug wait time (or time between rooming and first medication) decreased by 8%, and appointments running longer than expected decreased by 4.24%. No negative impacts to patient experience were observed.

Thu Janes DNP, RN, NE-BC

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