SafetyNet: streamlining and automating QA in radiotherapy

Abstract

Proper quality assurance (QA) of the radiotherapy process can be time-consuming and expensive. Many QA efforts, such as data export and import, are inefficient when done by humans. Additionally, humans can be unreliable, lose attention, and fail to complete critical steps that are required for smooth operations. In our group we have sought to break down the QA tasks into separate steps and to automate those steps that are better done by software running autonomously or at the instigation of a human. A team of medical physicists and software engineers worked together to identify opportunities to streamline and automate QA. Development efforts follow a formal cycle of writing software requirements, developing software, testing and commissioning. The clinical release process is separated into clinical evaluation testing, training, and finally clinical release. We have improved six processes related to QA and safety. Steps that were previously performed by humans have been automated or streamlined to increase first-time quality, reduce time spent by humans doing low-level tasks, and expedite QA tests. Much of the gains were had by automating data transfer, implementing computer-based checking and automation of systems with an event-driven framework. These coordinated efforts by software engineers and clinical physicists have resulted in speed improvements in expediting patient-sensitive QA tests.

Efficiency and safety increases after the implementation of a multi‐institutional automated plan check tool at our institution

“…While PCT is described in more detail elsewhere, 10,11 in brief, it is a read‐only applications programming interface (API) plug‐in, written in C#, designed for the Eclipse TPS (Varian Medical Systems, Palo Alto, CA). Data elements not available through the API are accessed via SQL queries.…”

“…We were motivated to join a multi‐institutional collaboration to jointly develop a flexible automated checking framework designed with the versatility to be shared across the collaborating facilities while supporting the individual differences between practices. Our plan check tool (PCT) was initially developed at University of Michigan (UM), 10,11 but later co‐developed between UM and Memorial Sloan Kettering Cancer Center (MSKCC) .…”

Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

“…There have been a number of reports of using a rules‐based approach to chart checking . While there is an AI method based on rules (expert systems), these reports have not utilized this, but rather enumerate a number of fixed rules that have to be met, and if they are not, an error is flagged.…”

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.…”