Utilizing a Paper Simulation to Evaluate Scheduling Workflow

Abstract

<h3>Purpose/Objective(s)</h3> Increased demand for Gamma Knife (GK) radiosurgery treatment necessitated the installation of a second treatment machine to continue providing lifesaving treatment in a timely manner. The addition of a second machine brought with it the potential to double treatment volume, but the department was unable to allocate additional pre and post procedural space. Workflow and scheduling of patients was evaluated to ensure streamlined operations during the rollout of the additional machine. The objective of the Continuous Improvement (CI) project was to minimize the incidence of treatments surpassing 6:00 PM. <h3>Materials/Methods</h3> A cross functional team (represented by nursing, radiation therapists, radiation oncologists, surgeons, and physicists) employed A3 methodology to develop a current state process map for the different treatment modalities used in GK. Using the process map, the team constructed a proposed scheduling guideline that accounted for an increased patient volume without additional pre/post procedural space. Data were collected from the Brain Tumor Institute database for 325 patients regarding procedure type and duration, patient zip code, and number of days surpassing 6:00 PM. This information was used to develop a GK sample patient population. The CI process involved creating a visual representation to convey patient flow through the GK department using a poster board and patient tokens. Procedure times were generated by a randomized normal distribution of the GK sample population, and used in scenarios of varying capacity, complexity, and timing of pre-procedure imaging. The simulation included having the team complete a pre-planning huddle to determine the flow of the day that included patient order and arrival times. The simulation progressed patients through the day in 30-minute intervals while generating randomized unplanned delays formulated from clinician feedback. <h3>Results</h3> The proposed scheduling guideline was simulated at 60% capacity with mild complexity and 50% of imaging completed prior to the treatment day. A subsequent simulation was conducted at 100% capacity with moderate complexity and no imaging completed prior. This resulted in five modifications to the guideline and both simulations completing prior to 6:00 PM. Following the go-live, the number of patients treated in GK increased from 113 in 2021 to 157 in 2022 between January 1^st and February 24^th. The number of days that treatments surpassed 6:00 PM during the respective time periods reduced from 4 to 2, representing a 50% year over year reduction, despite a volume increase of 39%. <h3>Conclusion</h3> Utilization of a simulation activity to evaluate a proposed scheduling guideline provided a tangible experience with optimizing workflow and efficiency. This allowed the CI team to gain a practical understanding of the impacts of adding a second treatment machine prior to the new machine's implementation.