Improving Prostate MR Image Quality in Practice—Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative
Andrei S. Purysko, Kay Zacharias‐Andrews, Kandice Garcia Tomkins, Ismail Baris Turkbey, Francesco Giganti, Mythreyi Bhargavan, David B. Larson, Jeffrey Weinreb, Clare Tempany, Christopher Smith, Ann Hester, Kevin Chang, Sara Martin, Rajan Gupta, Erica Owenby, Logan McLean, Linda Campbell, Alessandro Furlan, Andrew Grills
Abstract
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.