Finding
This work clarifies where waste is introduced into simulation appointment scheduling, and supporting workflows.
Paper
Understanding Process Breakdowns During CT-Simulation Encounters.
Observational Study
Citations: 0
Full text
Semantic ScholarAbstract
PURPOSE/OBJECTIVE(S) The CT Simulator is a limited resource within radiation oncology departments; one unit typically exists within a practice location, with multiple physicians competing for timeslots to conduct simulation procedures on their patients. At our multi-site academic practice, CT simulators operate nine hours a day with no break in the schedule. Cases are scheduled at 30, 60- and 90-min increments. Historically, simulation cases start late and/or run past the allotted time. Delays can occur prior to starting or intra-procedural and reduce patient satisfaction. We began collecting data to determine the cause for the delays at each clinical site in order to identify operational inefficiencies preventing simulation appointments from starting on time. Identification of such barriers will provide understanding of waste in the CT-Simulation workflows, reveal hidden available capacity, and assess opportunities for process improvement. MATERIALS/METHODS A data collection tool was utilized across four clinic sites within Radiation Oncology. In Phase I of data collection, the following parameters were captured: date, time, CT-SIM start time, attending physician, treatment site, efficiency indicators (1-33); totaling 429 cases. Efficiency indicators captured varied but we focused on four main areas (waiting, competing for resources, error in scheduling, and lack of resources). Data was independently collected by radiation therapists throughout 2020 calendar year. In Phase II of data collection two new indicators were added; End Time and Procedure Type. We utilized STATA version 16 to conduct summaries and tabulations. RESULTS Appointment durations are based on historical norms, which inadvertently allow time for waste. From arrival to start of CT-Sim the average was 51.62 minutes, the median was 42.50 minutes; the max was 180 minutes. In total we recorded 784 efficiency indicators. On average two efficiency indicators were captured per patient, meaning compounding delay rate (two or more delay indicators). Delays related to competing resources was the number one reason for delays; with a combined frequency of (31.25%). CONCLUSION This work clarifies where waste is introduced into simulation appointment scheduling, and supporting workflows. This work will serve as data to help convert waste to available capacity and develop value focused standardized workflows.
Authors
M. Washington, W. Chin, V. Patel
Journal
International journal of radiation oncology, biology, physics