Improvement in MR quality control workflow and outcomes with a web‐based database. Issue 5 (19th April 2020)

• Record Type: Journal Article
• Authors: Yang, Xiangyu; Little, Kevin; Jiang, Xia; Hintenlang, David
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 5(2020)
• Page Start: 98
• Page End: 104
• Publication Date: 2020-04-19
• Subjects: automated quality control -- human error -- MRI -- web‐based database; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12879
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: To describe a custom‐built, web‐based MR Quality Control (QC) database, and to assess its impact on the QC workflow and outcomes in a large U.S. academic medical center. Methods: The MR QC database was built with Microsoft Access 2010 and published on a Microsoft Sharepoint website owned and maintained by the authors' institution. Authorized users can access the database remotely with mainstream web browsers on any institutional computers. QC technologists were granted access to add, review, and print daily and weekly QC records. Qualified medical physicists (QMPs) were granted additional access to edit, review, and approve existing QC records and to change tolerance limits. A macro was utilized to conduct an automatic weekly review of QC status and to email the results to a QMP. This web‐based QC database was implemented on 17 clinical MRIs at the authors' institution. Weekly ACR QC findings within one year before and after implementation were compared. Results: We analyzed 158 QC issues detected by the web‐based database and 127 QC issues identified in conventional paper records before we implemented the database. The web‐based database significantly reduced the number of QC issues due to technologist error (before/after: 59/24 cases, P < 0.0001) but did not affect the number of QC issues related to scanner performance (before/after: 49/46 cases, P = 1). Further analysis revealed that the web‐based database significantly reduced the average time for the QMPs to identify a QC issue (before/after: 177 ± 110/2 ± 2 days, P < 0.0001) and time to correction (before/after: 81 ± 102/7 ± 8 days, P < 0.0001). The correction rate also significantly increased (before/after: 22%/99%, P < 0.0001). Conclusion: The web‐based QC database provides a positive impact on our MR QC workflow and outcomes. It simplifies QC workflow, enables early detection of quality issues, and facilitates quick resolution of problems that may affect the quality of clinical MRI studies.