SU‐F‐T‐226: QA Management for a Large Institution with Multiple Campuses for FMEA

Abstract

Purpose: To redesign our radiation therapy QA program with the goal to improve quality, efficiency, and consistency among a growing number of campuses at a large institution. Methods: A QA committee was established with at least one physicist representing each of our six campuses (22 linacs). Weekly meetings were scheduled to advise on and update current procedures, to review end‐to‐end and other test results, and to prepare composite reports for internal and external audits. QA procedures for treatment and imaging equipment were derived from TG Reports 142 and 66, practice guidelines, and feedback from ACR evaluations. The committee focused on reaching a consensus on a single QA program among all campuses using the same type of equipment and reference data. Since the recommendations for tolerances referenced to baseline data were subject to interpretation in some instances, the committee reviewed the characteristics of all machines and quantified any variations before choosing between treatment planning system (i.e. treatment planning system commissioning data that is representative for all machines) or machine‐specific values (i.e. commissioning data of the individual machines) as baseline data. Results: The configured QA program will be followed strictly by all campuses. Inventory of available equipment has been compiled, and additional equipment acquisitions for the QA program are made as needed. Dosimetric characteristics are evaluated for all machines using the same methods to ensure consistency of beam data where possible. In most cases, baseline data refer to treatment planning system commissioning data but machine‐specific values are used as reference where it is deemed appropriate. Conclusion: With a uniform QA scheme, variations in QA procedures are kept to a minimum. With a centralized database, data collection and analysis are simplified. This program will facilitate uniformity in patient treatments and analysis of large amounts of QA data campus‐wide, which will ultimately facilitate FMEA.

Medical Institution Security Risk Management

“…With a centralized database, data collection and analysis can be simplified. This plan will help to unify the treatment of patients and the analysis of a large number of QA data across the school, which will eventually promote FMEA [23]. He used the propensity score matching method from County A (intervention group) and County B (control group), and selected 1,673 pairs of hypertension patients.…”