Purpose: The chart check is arguably the most basic, ubiquitous clinical physics task. Yet teaching it is challenging. Direct observation of chart checks is time-consuming and tedious, given its repetitive nature. Many possible errors of which a trainee should be made aware occur infrequently and are unlikely to be seen caught in a small sample. Clinical pressures may discourage questions and discussion during observation. To address these challenges, we implemented a competitively-created, live dataset, informally named â€œThangsâ€? (for Therapy Anomaly Gathering System), to improve efficiency of chart check training.
Methods: Thangs is a shared spreadsheet into which physicists enter everything caught during clinical chart checks. To promote participation, completeness, and rapid growth, the project was framed as a competition to determine who the best chart checker on staff is. Every week, the physicist with the most entries is publicly lauded as Champion Error Finder (CHEF). Milestones (e.g., first to 100) are also recognized. An intranet app was created to support visualization, classification, and analysis.
Results: There is 100% participation from the physics staff. Notably, the hundreds of monthly entries exceed the number of monthly â€œnear-missesâ€? in our variance reporting system by over 10x. The success of this project allows residents, in a much more compressed timeframe, to access a much larger number and range of clinical chart checks and findings. Residents can also now practice on any clinical chart and look it up in Thangs afterwards to see if the same issues, if any, were identified, rather than forcing a physicist to re-do the check. Thangs also supports teaching FMEA, giving actual clinical data on Occurrence and Detectability, and is being used to support departmental quality goals and projects.
Conclusion: Competition was used to successfully create a dataset of physics check results for use in resident training.
Not Applicable / None Entered.