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

Autor:	Purysko AS; Head, Section of Abdominal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, Ohio; Physician Leader, Prostate MR Image Quality Improvement Collaborative, American College of Radiology Learning Network. Electronic address: puryska@ccf.org., Zacharias-Andrews K; American College of Radiology, Reston, Virginia., Tomkins KG; Department of Radiology, Stanford University School of Medicine, Stanford, California., Turkbey IB; Head, Magnetic Resonance Imaging Section and the Artificial Intelligence Resource, Molecular Imaging Branch, Molecular Imaging Program, National Cancer Institute, Bethesda, Maryland. Electronic address: https://twitter.com/radiolobt., Giganti F; Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Division of Surgery & Interventional Science, University College London, London, UK. Electronic address: https://twitter.com/giga_fra., Bhargavan-Chatfield M; Executive Vice President for Quality and Safety, American College of Radiology, Reston, Virginia. Electronic address: https://twitter.com/MythreyiC., Larson DB; Senior Vice Chair for Strategy and Clinical Operations, Department of Radiology, Stanford University School of Medicine, Stanford, California; Chair, Commission on Quality and Safety, American College of Radiology. Electronic address: https://twitter.com/larson_david_b.
Jazyk:	angličtina
Zdroj:	Journal of the American College of Radiology : JACR [J Am Coll Radiol] 2024 Sep; Vol. 21 (9), pp. 1464-1474. Date of Electronic Publication: 2024 May 09.
DOI:	10.1016/j.jacr.2024.04.008
Abstrakt:	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. (Copyright © 2024 American College of Radiology. All rights reserved.)
Databáze:	MEDLINE
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