Developing an Automatic Treatment Record Review System for Quality Assurance of Patient Treatment Delivery in Radiation Therapy

Author:

Huang Peng1,Xu Yingjie1,Huan Fukui1,Zhang Yanxin1,Ma Min1,Men Kuo1,Dai Jianrong1

Affiliation:

1. National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Abstract

Abstract

Background and Purpose Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists. However, it is time-consuming, tedious, and error-prone. To assist this process, a treatment record review system (TRRS) is developed to automatically review items related to treatment delivery record. Methods The treatment record is firstly extracted from oncology information system(OIS). Based on the daily patient treatment information, the original plan from the treatment planning system is identified. Then the original plan and the delivered plan are correlated. Eight review categories (parameter consistency, treatment completeness, treatment progression, image guidance, override, treatment couch, documentation, and treatment mode) are defined. Tailored rules are designed for various review items to automate the review process. As a result, for each treatment record on a daily basis, a review flag (pass, failure, warning, and N/A) is determined by TRRS. Finally, this system is evaluated using six months patient treatment records collected in our institute and compared to the manual process on the same database. Results TRRS automatically reviewed a total of 76651 treatment fractions from 4230 patients with an average of 574 treatments per day. The average abnormality rate was 0.76%. The average processing time per treatment record was 3.9 seconds and 282 seconds for the automatic and manual processes, respectively. Comparing with the manual process, the time efficiency of TRRS is improved by a factor of 72. The average numbers of anomalies detected by the automatic and manual processes are 21 and 13 per day, respectively. TRRS detects 61.5% more anomalies than those of the manual process. Conclusion TRRS is not only efficient in processing a large amount of treatment records on a daily basis but also effective in finding more anomalies than those of physics weekly check. The application of the automatic review system could significantly reduce the work of review physicists and let them focus on more important works related to patient safety.

Publisher

Research Square Platform LLC

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