Advanced Physics Chart Check Automation Method to Increase Safety and Efficiency
M Cho1*, J Son1, S Choi1,2, C Hong2, K Park1,2, (1) Yongin Severance Hospital, Yongin, ,KR, (2) Yonsei University College of Medicine, Seoul, 11, KR
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PO-GePV-T-211 (Sunday, 7/10/2022) [Eastern Time (GMT-4)]
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Purpose: As radiation oncology treatment planning evolves to a high level of complexity, chart check becomes more critical yet more cumbersome. To improve patient safety, work efficiency, and accuracy, we developed advanced automation methods for physics chart check.
Methods: According to the TG-275 and TG-315, our development team composed of radiation oncologists, medical physicists, and dosimetrists established a plan/chart checklist. Afterward, we developed a chart check automation program that collects and evaluates the items of checklist using information from electronic medical record system (EMR) and treatment planning system. To assess the feasibility of our in-house program, we compared time spent through the overall chart check process, using either manual or automated methods, in the same patient case. The check times were estimated in 200 VMAT plans, composed of four different tumor sites: brain, head and neck, breast, and lung cancers. Also, any errors discovered during the initial chart check were compared between two methods, to evaluate the error detection accuracy.
Results: The ranges of spent time were 35-50 minutes (average 41 minutes) and 13-20 minutes for the manual and the automated methods, respectively. In the same chart check process, automated methods had an average time reduction of 62% compared to manual methods. After the automated method, we found 3 cases (1.5%) to have errors that were not detected by the manual chart check. Those were minor documentation errors (missing information in EMR).
Conclusion: Our automated chart check program can significantly reduce manual check time and detect more potential errors. It saves time in the overall workflow, thus allowing more time to be allocated to those more important steps and improves patient safety significantly. Also, we are working on the automatic error reporting system by connecting our program with our hospital’s radiation oncology incident learning system (RO-ILS).
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A2C1010900).
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