Early detection of potential errors during patient treatment planning

Abstract

PurposeData errors caught late in treatment planning require time to correct, resulting in delays up to 1 week. In this work, we identify causes of data errors in treatment planning and develop a software tool that detects them early in the planning workflow.MethodsTwo categories of errors were studied: data transfer errors and TPS errors. Using root cause analysis, the causes of these errors were determined. This information was incorporated into a software tool which uses ODBC‐SQL service to access TPS's Postgres and Mosaiq MSSQL databases for our clinic. The tool then uses a read‐only FTP service to scan the TPS unix file system for errors. Detected errors are reviewed by a physicist. Once confirmed, clinicians are notified to correct the error and educated to prevent errors in the future. Time‐cost analysis was performed to estimate the time savings of implementing this software clinically.ResultsThe main errors identified were incorrect patient entry, missing image slice, and incorrect DICOM tag for data transfer errors and incorrect CT‐density table application, incorrect image as reference CT, and secondary image imported to incorrect patient for TPS errors. The software has been running automatically since 2015. In 2016, 84 errors were detected with the most frequent errors being incorrect patient entry (35), incorrect CT‐density table (17), and missing image slice (16). After clinical interventions to our planning workflow, the number of errors in 2017 decreased to 44. Time savings in 2016 with the software is estimated to be 795 h. This is attributed to catching errors early and eliminating the need to replan cases.ConclusionsNew QA software detects errors during planning, improving the accuracy and efficiency of the planning process. This important QA tool focused our efforts on the data communication processes in our planning workflow that need the most improvement.

Treatment planning for proton therapy: what is needed in the next 10 years?

“…Although automation in the context of treatment planning can involve many different steps, such as QA issues 55,56 or plan review, 57 it mainly consists of two distinct parts; volume delineation (or segmentation) and planning related issues. In the following, we give a short review of these items with emphasis on the specific issues relevant for PT.…”

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

“…One example is a study by Luk et al 31 who, using a Bayesian network‐based radiotherapy plan verification model, suggested a 4‐year training dataset to optimize the performance of the network, and yearly updates were considered sufficient to capture the evolution of clinical practice and maintain fidelity. Other recently published papers describe and quantify time savings and error reduction using different analyses 21,32–34 . A review of these new techniques and publications is instructive in looking at the future of initial chart check automation.…”

“…Concerning the increasing reliance on initial chart check automation, TG-275 provides an estimation of the types of checks that might be automated in the future, based on a review of prior publications up to 2016 [8][9][10][11][12][13][14][15][16][17][18][19] and other considerations. The feasibility of automation as indicated in TG-275, however, may be increasing with the rapid development of techniques in locally developed programs, 20,21 vendor solutions, and recent acceleration of machine learning efforts. [22][23][24][25][26][27][28][29][30] Some items previously deemed impossible for automation may become feasible through the development of new and easy-to-implement machine learning techniques.…”

“…Other recently published papers describe and quantify time savings and error reduction using different analyses. 21,[32][33][34] A review of these new techniques and publications is instructive in looking at the future of initial chart check automation.…”

Characterization of a Bayesian network‐based radiotherapy plan verification model

“…To address this issue of developing improved methods of error detection, we proposed a novel artificial intelligence (AI) approach using Bayesian networks (BN). This approach was taken because although automated rules-based approaches have been developed to assist the plan checking process [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] and have led to reduced error rates and increased efficiency, 22 it was felt that they have significant limitations and do not provide the sort of domain knowledge and human reasoning, which is essential to a comprehensive review of treatment plans.…”