Literature DB >> 26892191

Audit feedback on reading performance of screening mammograms: An international comparison.

S Hofvind1, R L Bennett2, J Brisson3, W Lee4, E Pelletier5, A Flugelman6, B Geller7.   

Abstract

OBJECTIVE: Providing feedback to mammography radiologists and facilities may improve interpretive performance. We conducted a web-based survey to investigate how and why such feedback is undertaken and used in mammographic screening programmes.
METHODS: The survey was sent to representatives in 30 International Cancer Screening Network member countries where mammographic screening is offered.
RESULTS: Seventeen programmes in 14 countries responded to the survey. Audit feedback was aimed at readers in 14 programmes, and facilities in 12 programmes. Monitoring quality assurance was the most common purpose of audit feedback. Screening volume, recall rate, and rate of screen-detected cancers were typically reported performance measures. Audit reports were commonly provided annually, but more frequently when target guidelines were not reached.
CONCLUSION: The purpose, target audience, performance measures included, form and frequency of the audit feedback varied amongst mammographic screening programmes. These variations may provide a basis for those developing and improving such programmes.
© The Author(s) 2016.

Entities:  

Keywords:  Audit; breast screening; feedback; mammography

Mesh:

Year:  2016        PMID: 26892191     DOI: 10.1177/0969141315610790

Source DB:  PubMed          Journal:  J Med Screen        ISSN: 0969-1413            Impact factor:   2.136


  4 in total

1.  Consensus Reads: The More Sets of Eyes Interpreting a Mammogram, the Better for Women.

Authors:  Solveig Hofvind; Christoph I Lee
Journal:  Radiology       Date:  2020-02-11       Impact factor: 11.105

2.  Mammography self-evaluation online test for screening readers: an Italian Society of Medical Radiology (SIRM) initiative.

Authors:  Beniamino Brancato; Francesca Peruzzi; Calogero Saieva; Simone Schiaffino; Sandra Catarzi; Gabriella Gemma Risso; Andrea Cozzi; Serena Carriero; Massimo Calabrese; Stefania Montemezzi; Chiara Zuiani; Francesco Sardanelli
Journal:  Eur Radiol       Date:  2021-09-04       Impact factor: 5.315

3.  Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program.

Authors:  Marthe Larsen; Camilla F Aglen; Christoph I Lee; Solveig R Hoff; Håkon Lund-Hanssen; Kristina Lång; Jan F Nygård; Giske Ursin; Solveig Hofvind
Journal:  Radiology       Date:  2022-03-29       Impact factor: 29.146

4.  A machine learning model based on readers' characteristics to predict their performances in reading screening mammograms.

Authors:  Ziba Gandomkar; Sarah J Lewis; Tong Li; Ernest U Ekpo; Patrick C Brennan
Journal:  Breast Cancer       Date:  2022-02-05       Impact factor: 3.307

  4 in total

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