The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography

Dorothy A Sippo; Amy M Sullivan; Amy Cohen; Sarah F Mercaldo; Manisha Bahl; Constance D Lehman

doi:10.1016/j.jacr.2020.05.036

The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography

J Am Coll Radiol. 2020 Dec;17(12):1626-1635. doi: 10.1016/j.jacr.2020.05.036. Epub 2020 Jul 21.

Authors

Dorothy A Sippo¹, Amy M Sullivan², Amy Cohen³, Sarah F Mercaldo⁴, Manisha Bahl⁴, Constance D Lehman⁵

Affiliations

¹ Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: dsippo@post.harvard.edu.
² Associate Director for Education Research, Program in Medical Education, Harvard Medical School, Boston, Massachusetts.
³ Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
⁴ Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
⁵ Chief, Breast Imaging Division, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.

PMID: 32707191
DOI: 10.1016/j.jacr.2020.05.036

Abstract

Objective: To evaluate a tomosynthesis screening mammography automated outcomes feedback application's adoption and impact on performance.

Methods: This prospective intervention study evaluated a feedback application that provided mammographers subsequent imaging and pathology results for patients that radiologists had personally recalled from screening. Deployed to 13 academic and 5 private practice attending radiologists, adoption was studied from March 29, 2018, to March 20, 2019. Radiologists indicated if reviewed feedback would influence future clinical decisions. For a subset of eight academic radiologists consistently interpreting screening mammograms during the study, performance metrics were compared pre-intervention (January 1, 2016, to September 30, 2017) and post-intervention (October 1, 2017 to June 30, 2018). Abnormal interpretation rate, positive predictive value of biopsies performed, sensitivity, specificity, and cancer detection rate were compared using Pearson's χ² test. Logistic regression models were fit, adjusting for age, race, breast density, prior comparison, breast cancer history, and radiologist.

Results: The 18 radiologists reviewed 68.5% (1,398 of 2,042) of available feedback cases and indicated that 17.4% of cases (243 of 1,398) could influence future decisions. For the eight academic radiologist subset, after multivariable adjustment with comparison to pre-intervention, average abnormal interpretation rate decreased (from 7.5% to 6.7%, adjusted odds ratio [aOR] 0.86, P < .01), positive predictive value of biopsies performed increased (from 40.6% to 51.3%, aOR 1.48, P = .011), and specificity increased (from 93.0% to 93.9%, aOR 1.17, P < .01) post-intervention. There was no difference in cancer detection rate per 1,000 examinations (from 5.8 to 6.1, aOR 1.01, P = .91) or sensitivity (from 81.2% to 78.7%, aOR 0.84, P = .47).

Conclusions: Radiologists used a screening mammography automated outcomes feedback application. Its use decreased false-positive examinations, without evidence of reduced cancer detection.

Keywords: Automated outcomes feedback; informatics; mammography; quality improvement; screening.

MeSH terms

Breast Neoplasms* / diagnostic imaging
Early Detection of Cancer
Feedback
Female
Humans
Mammography*
Mass Screening
Prospective Studies
Sensitivity and Specificity