The ACR Mammography Positioning Improvement Collaborative: A Multi-Center Improvement Program within a Learning Network Framework.

PURPOSE/OBJECTIVE: To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program. METHODS: The sites comprising the first cohort of the Collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement (QI) initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions. A standardized image quality scoring system was also established. The impact of the interventions implemented at each site was assessed by tracking the percentage of screening mammograms meeting overall passing criteria over time. RESULTS: Six organizations were selected to participate as the first cohort, beginning with participation in the improvement program. Interventions developed and implemented at each site during the program resulted in improvement in the average percentage of screening mammograms meeting overall passing criteria per week from a collaborative mean of 51% to 86%, with four of six sites meeting or exceeding the target mean performance of 85% by the end of the improvement program. Afterwards, all respondents to the post-program survey indicated that the program was a positive experience. CONCLUSION: Using a structured improvement program within a learning network framework, the first cohort of the Collaborative demonstrated that improvement in mammography positioning performance can be achieved at multiple sites simultaneously, and validated the hypothesis that local sites' shared experiences, insights, and learnings would not only improve performance but would also build a community of improvers collaborating to create the best experience for technologists, staff, and patients.

Improving Prostate MR Image Quality in Practice-Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative.

OBJECTIVE: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. METHODS: Teams from five organizations in the United States were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of examinations meeting quality criteria (ie, PI-QUAL score ≥4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of examinations with PI-QUAL ≥4 to at least 85%. RESULTS: Across 2,380 examinations audited, the mean weekly rates of prostate MR examinations meeting image quality criteria increased from 67% (range: 60%-74%) at baseline to 87% (range: 80%-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personnel training, and development of an auditing process mechanism. CONCLUSION: A learning network model, in which organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a road map for improvement on a broader scale.

The ACR Learning Network: Facilitating Local Performance Improvement Through Shared Learning.

PURPOSE: The ACR Learning Network was established to test the viability of the learning network model in radiology. In this report, the authors review the learning network concept, introduce the ACR Learning Network and its components, and report progress to date and plans for the future. METHODS: Patterned after institutional programs developed by the principal investigator, the ACR Learning Network was composed of four distinct improvement collaboratives. Initial participating sites were solicited through broad program advertisement. Candidate programs were selected on the basis of assessments of local leadership support, experience with quality improvement initiatives, intraorganizational relationships, and access to data and analytic support. Participation began with completing a 27-week formal quality improvement training and project support program, with local teams reporting weekly progress on a common performance measure. RESULTS: Four improvement collaborative topics were chosen for the initial cohort with the following numbers of participating sites: mammography positioning (6), prostate MR image quality (6), lung cancer screening (6), and follow-up on recommendations for management of incidental findings (4). To date, all sites have remained actively engaged and have progressed in an expected fashion. A detailed report of the results of the improvement phase will be provided in a future publication. CONCLUSIONS: To date, the ACR Learning Network has successfully achieved planned milestones outlined in the program's plan, with preparation under way for the second and third cohorts. By providing a shared platform for improvement training and knowledge sharing, the authors are optimistic that the network may facilitate widespread performance improvement in radiology on a number of topics for years to come.