The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography
Copyright © 2020. Published by Elsevier Inc..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Journal of the American College of Radiology : JACR - 17(2020), 12 vom: 05. Dez., Seite 1626-1635 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sippo, Dorothy A [VerfasserIn] |
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| Links: |
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| Themen: |
Automated outcomes feedback |
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| Anmerkungen: |
Date Completed 21.06.2021 Date Revised 21.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jacr.2020.05.036 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM312831684 |
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| 245 | 1 | 4 | |a The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography |
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| 500 | |a Date Revised 21.06.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020. Published by Elsevier Inc. | ||
| 520 | |a OBJECTIVE: To evaluate a tomosynthesis screening mammography automated outcomes feedback application's adoption and impact on performance | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a CONCLUSIONS: Radiologists used a screening mammography automated outcomes feedback application. Its use decreased false-positive examinations, without evidence of reduced cancer detection | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Automated outcomes feedback | |
| 650 | 4 | |a informatics | |
| 650 | 4 | |a mammography | |
| 650 | 4 | |a quality improvement | |
| 650 | 4 | |a screening | |
| 700 | 1 | |a Sullivan, Amy M |e verfasserin |4 aut | |
| 700 | 1 | |a Cohen, Amy |e verfasserin |4 aut | |
| 700 | 1 | |a Mercaldo, Sarah F |e verfasserin |4 aut | |
| 700 | 1 | |a Bahl, Manisha |e verfasserin |4 aut | |
| 700 | 1 | |a Lehman, Constance D |e verfasserin |4 aut | |
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