3368 - Implementation of an Automated Quality Analytics Dashboard to Reduce Planning Bottlenecks

Purpose/Objective(s):

The simulation (sim) to treatment (tx) workflow is a highly coordinated series of tasks distributed across the Radiation Oncology team that requires timely task completion to protect and optimize quality and safety checks. In our complex tertiary/quaternary care environment coordination of multidisciplinary care and on time start for chemoradiation pose additional challenges. As the first task, contouring requires expert review and timely completion to ensure sufficient time for downstream tasks to avoid rushed planning, reduced quality assurance time, and potential tx delays. We built an automated Quality Dashboard (QD) providing an overview of department and individual performance on contouring time and other quality metrics. We hypothesize that these tools identify bottlenecks and empower quicker Plan Do Study Act cycles for continuous process improvement, safer planning, and prevention of tx delays.

Materials/Methods:

An in-house QD was developed using a commercial analytics platform interfaced with our Oncology Informatics System to extract key metrics including: distribution of clinical urgency of cases, distribution of tx techniques, percent (%) of sim/planning directives entered before sim/planning, % of prescriptions approved prior to the day of tx, % of IGRT reviewed prior to subsequent fraction, and % of contours completed in the allotted time (on time). These metrics can all be stratified by time, provider, and clinical urgency. The QD provides an overview of key time stamps that lead to urgency in the 48 hours prior to tx. Initial goals were to provide data to encourage timely completion of tasks which were provided in an automated monthly individual report for each provider that included: monthly % of contours completed on time, comparison of individual on time performance relative to anonymized peers, and descriptive statistics of individual vs department contour turnaround time and hours overdue.

Results:

Data from the past two years showed on time completion of contours had a direct impact on dosimetry, reducing the rate of on time planning from 58% (on time contours) to 21% (late contours). It also showed that when contours were late, planning time was compressed with 48% of plans completed within one business day and 69% within two, compared to 34% and 53% with on time contours. Following deployment of the QD, department leaders analyzed bottlenecks to discuss workflow adjustments that would prioritize quality and support timely contour completion.

Conclusion:

The QD with automated individualized contouring performance reports provided valuable insight into bottlenecks and workflow inefficiencies. By identifying trends in contour completion times and highlighting potential areas for improvement, the QD assists in understanding contouring challenges and supports high quality contouring with fewer delays to downstream tasks. Future efforts will focus on targeting various quality metrics across the sim to tx workflow for continuous process improvement.