Improvement in MR quality control workflow and outcomes with a web‐based database

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.


Quality control (QC) is an essential component of radiologic practice.
A well-designed and well-executed QC program allows for imaging service providers to identify problems in the early stage of their manifestation and to take proper corrective actions with minimal interruption to clinical service. In Europe and North America, regulatory agencies and accrediting bodies have imposed rigorous requirements on QC programs of imaging modalities under their governance. In the United States, the American College of Radiology (ACR) developed dedicated phantoms and QC procedures for various imaging modalities. [1][2][3] These procedures and phantoms are being used routinely at more than 38,000 ACR-accredited imaging facilities in practices of Mammography, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Nuclear Medicine (NM).
An effective QC program requires coherent and persistent efforts by radiologists, technologists, and qualified medical physicists (QMPs).
Specially trained QC technologists are the first-line workers in radiologic QC programs. Under the technical oversight of QMPs and the supervision of radiologists, they play a vital role in maintaining high quality performance of imaging equipment. They conduct basic QC testing on a daily or weekly basis, report quality issues to the supervising QMPs and radiologists, and initiate corrective actions following QMP's instructions. This mechanism, however, could be substantially compromised by human error or negligence. Scheduled QC tests could be neglected due to miscommunication or an unexpected increase in clinical workload. QC technologists could make mistakes in the evaluation of QC data or inadvertently overlook a test result outside the tolerance limits. Periodic QMP review of QC logs provides an opportunity to identify and address these issues, but it is usually difficult to conduct the QMP review at sufficiently high frequency. Consequently, quality issues could persist and accumulate in the clinical operation of radiologic imaging services for a prolonged period before they are identified, potentially leading to decreased patient care quality or safety and citations by regulatory agencies or accrediting bodies.
We hypothesize that the limitations of a traditional paper-based QC record discussed above can be overcome with a web-based QC system that allows for centralized management of multisite QC data, real-time detection of QC issues, and automated reporting to a QMP. To the best of our knowledge, the impact of web-based QC systems has not been systematically evaluated for clinical environments in scientific literature despite the presence of a few automated QC image analysis tools 4-7 and commercial enterprise solutions. 8,9 In this paper, we describe a custombuilt, web-based MR QC database, report our experience with implementing such a system in a large academic medical center in the United States, and assess its impact on our QC workflow and outcomes. MRIs, and manufacturer's daily QC for selected models of GE and Siemens scanners. The data entry forms use embedded macros to detect out-of-tolerance data fields in real time and label them with red color to alert the users (Fig. 1). At the end of each week, an Access macro scans all data entries in the previous week for missing records or QC failures.

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Each finding opens a case in the database, and the QMPs are notified by automatic email alerts. All active cases are displayed in chronological order on a QC Summary interface for QMPs' follow-up until their resolution (Fig. 2). The QMP's assessment result, the course and outcome of corrective actions taken, and any other relevant information can be documented in a text note attached to the record. F I G . 1. ACR weekly phantom data entry form. Specially trained QC technologists can add QC records to the MR QC database using Access web forms. This example is a web form for the collection of weekly ACR phantom data. Embedded macros were used to compare data entries with tolerance limits specified by a QMP. Passed, failed, and unfinished tests are respectively color coded as green, red, and yellow. A note can be added to the record to document QC technologist's comment, repeated testing results, or any other relevant information. ACR, American College of Radiology; QMPs, Qualified medical physicists.

2.B | Implementation and Impact Assessment
In November and December of 2017, we deployed the MR QC database at our institution, a large U.S. academic medical center that serves a metropolitan area and surrounding suburban communities.
The database covered 17 clinical MRIs distributed in 9 imaging facilities ( Table 1). Operation of these scanners was overseen by a group of four QMPs certified by the American Board of Radiology (ABR) in diagnostic medical physics. Identical quality standards were applied to all types of facilities. All facilities have designated QC technologist (s), who were provided with a detailed user manual and in-person training opportunities before the web-based database was launched.
In order to assess the impact of the web-based QC approach, we collected and analyzed all weekly ACR QC findings detected by the database within one year (52 weeks) after its implementation. For comparison purposes, we also thoroughly reviewed all paper QC records from the year prior to the implementation to identify QC issues that occurred in that period. Daily QC data were not included in our analysis due to varied scopes of manufacturers' routine QC tests. A QMP reviewed all findings to determine their root causes.
For issues requiring correction, the nature, timing, and extent of corrective actions were identified from relevant service reports, email communications, and interviews with the QC technologists involved.
During the study period, there were no personnel, procedural, or standard changes that could potentially confound our analysis. All contents of paper and electronic forms were kept identical. Two scanners were replaced by or upgraded to more advanced models after the database implementation. The total downtime due to scanner replacement and upgrade was 5 scanner-weeks. In total, 884 scanner-weeks of paper QC records and 879 scanner-weeks of electronic QC records were included in our analysis. When multiple QC records were available within a single week due to baseline data collection after the new installation, only the last record was included in our analysis. The web-based database underwent a service disruption for one week due to a major upgrade of the SharePoint service and consecutive database migration. All QC technologists were instructed to keep paper copies of their QC records and to add them to the database after the service was resumed. These expected delays were not counted as QC issues in our analysis. When a F I G . 2. The QC Summary interface. This interface provides an overview of weekly QC status. QC records can be filtered by MRI unit or by time. Each missing or failed QC is labeled as an "open case" with a red case indicator. QMPs can track these cases with the "Review/Followup" function and amend their reports with assessment of the underlying problem or progress of corrective actions. Once an issue is resolved, the corresponding case(s) can be closed by a QMP. The case indicator is set back to green at the time of case closure. QMPs, Qualified medical physicists.   Fig. 4).
According to a QMP's assessment, corrective actions were deemed necessary for 60 (47%) QC issues occurring before the implementation and 110 (70%) issues occurring after the implementation. All but one of the issues detected by the database were addressed with proper corrective actions. The only exception occurred during the service disruption and database migration. Contrarily, the rate of correction was significantly lower prior to the implementation. Only 13 issues identified in the paper records were corrected (22%; P < 0.0001). The web-based database also  Fig. 5).
The majority of corrective actions were taken in house. Only five cases before (8%) and nine cases after (7%) implementation required service correction. Services taken to address these issues include: laser calibration (three cases before and five cases after implementation), coil check/tuning (two cases after implementation), coil replacement (two cases after implementation, associated with the same coil and unrelated to the check/tunings mentioned above), and re-engagement of table clutch (one case before implementation). No records of corrective action were found for one case occurring prior to the implementation. There seemed to be a trend of decreasing time to service resolution (from 51 ± 45 days to 20 ± 18 days), but the difference did not reach statistical significance, which is likely due to the small sample size (P = 0.09).

| DISCUSSION
In radiologic QC programs, the QC technologists are expected to be able to effectively identify quality issues in their daily work and to efficiently relay them to supervisors or QMPs for further assessment and resolution. Our data, however, revealed that a nontrivial level of operator negligence and human error existed in the operation of a large institution despite its experienced imaging personnel, full access to physics expertise, and continuous promotion of a quality culture. The report rate was also found to be surprisingly low. These factors must be taken into account in the design, implementation, and execution of QC programs.
Even though quality issues requiring immediate service correction are expected to be rare in practice, negligence of such an issue could lead to serious consequences, including compromised patient care, increased safety risks to the patient and staff, and associated legal liabilities. While staff education is a useful way to cultivate a quality culture in an organization, it may be less effective in correcting inadvertent behaviors. Reducing the possibility of human error through process automation would be a more viable solution to this problem.
Our web-based database addresses human error and negligence in several ways. Automatic evaluation of test results not only adds an additional check step in the QC workflow but also reduces the risk of applying wrong or outdated tolerance limits to the data. This is particularly important for QC tests with scanner-specific or changeable tolerance limits, such as the transmitter gain or center frequency test.   | 103 which may be more suitable for their environment. Secondly, in this study we only focused on the improvement of QC workflow from QMPs' perspective. Our automated QC system could be further enhanced by integrating an automatic image analysis tool as described in the literature. [4][5][6] Lastly, we only studied weekly QC tests required by the ACR. Although daily QC is not an ACR requirement, it is recommended by all major MRI manufacturers and is widely used in clinical facilities. 5,10,11 It is aso a crucial component in our MR QC program. Our database has the capacity to integrate daily QC data from selected MRI models, but those data were not included in this study because different manufacturers' recommended tests vary significantly in their scopes. The web-based QC approach's impact on daily QC needs to be assessed with future research.

| CONCLUSION
Our study demonstrated the feasibility of developing a web-based QC solution for a radiologic imaging modality and implementing it in a complex clinical environment. Our data clearly demonstrated that the webbased database can simplify the QC workflow, reduce human errors, enable early detection of quality issues, and facilitate timely resolution of problems that may affect the quality of clinical MRI studies.

CONF LICT OF I NTEREST
The authors have no conflict of interest to disclose.