Impact of a Centralized Database System on Radiation Therapy Quality Assurance Management at a Large Health Care Network: 5 Years’ Experience

Abstract

PurposeThis study reports the impact of using a centralized database system for major equipment quality assurance (QA) at a large institution. Methods and MaterialsA centralized database system has been implemented for radiation therapy machine QA in our institution at 6 campuses with 11 computed tomographies and 22 linear accelerators (LINACs). The database system was customized to manage monthly and annual computed tomography and LINAC QA. This includes providing the same set of QA procedures across the enterprise, digitally storing all measurement records, and generating trend analyses. Compared with conventional methods (ie, paper forms), the effectiveness of the database system was quantified by changes in the compliance of QA tests and perceptions of staff to the efficiency of data retrieval and analyses. An anonymized questionnaire was provided to physicists enterprise-wide to assess workflow changes. ResultsWith the implementation of the database system, the compliance of QA test completion improved from 80% to >99% for the entire institution. This resonates with the 56% of physicists who found the database system helpful in guiding them through QA, and 25% of physicists found the contrary, and 19% reported no difference (n = 16). Meanwhile, 40% of physicists reported longer times needed to record data using the database system compared with conventional methods, and another 40% suggested otherwise. In addition, 87% and 80% found the database more efficient to analyze and retrieve previous data, respectively. This was also reflected by the shorter time taken to generate year-end QA statistics using the software (5 vs 30 min per LINAC). Overall, 94% of physicists preferred the centralized database system over conventional methods and endorsed continued use of the system. ConclusionsA centralized database system is useful and can improve the effectiveness and efficiency of QA management in a large institution. With consistent data collection and proper data storage using a database, high-quality data can be obtained for failure modes and effects analyses as per TG 100.