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Use of an Online Breast Cancer Risk Assessment and Patient Decision Aid in Primary Care Practices
Author(s) -
Karen Eden,
Ilya Ivlev,
Katherine L Bensching,
Gabriel J. Franta,
Alyssa R. Hersh,
James T. Case,
Rongwei Fu,
Heidi D Nelson
Publication year - 2020
Publication title -
journal of women's health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.195
H-Index - 98
eISSN - 1931-843X
pISSN - 1540-9996
DOI - 10.1089/jwh.2019.8143
Subject(s) - medicine , breast cancer , family medicine , risk assessment , medline , mammography , decision aids , clinical decision support system , gynecology , cancer , decision support system , alternative medicine , pathology , computer security , computer science , political science , law , artificial intelligence
Background: U.S. Preventive Services Task Force (USPSTF) recommendations for mammography screening, genetic counseling and testing for pathogenic BRCA1/2 mutations, and use of risk-reducing medications require assessment of breast cancer risk for clinical decision-making, but efficient methods for risk assessment in clinical practice are lacking. Materials and Methods: A cross-sectional study evaluating a web-based breast cancer risk assessment and decision aid (MammoScreen) was conducted in an academic general internal medicine clinic. All eligible women, 40-74 years of age without previous diagnosis of breast or ovarian cancer and who were enrolled in the Epic MyChart patient portal were invited. MammoScreen uptake and completion rates and consistency between breast cancer risk determined by MammoScreen and existing risk information in the Epic record were measured. Patient and physician experiences were summarized from interviews. Results: Of 448 invited participants, 339 (75.7%) read their MyChart invitation and 125 (36.9%) who read invitations enrolled in the study; 118 (94.4% of enrolled) completed MammoScreen. Twenty-one women were categorized as above-average risk from either MammoScreen data or the chart review and 7 (33.3%) were identified by both sources. Physicians and patients believed MammoScreen was easy to use and was helpful in identifying risks and facilitating shared decision-making. Conclusions: Breast cancer risk assessment and mammography screening decision support were efficiently implemented through a web-based tool for patients sent through an electronic patient portal. Integration of patient decision aids with risk algorithms in clinical practice may help support the implementation of USPSTF recommendations that include risk assessment and shared decision-making.

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