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Extraction of Electronic Health Record Data using Fast Healthcare Interoperability Resources for Automated Breast Cancer Risk Assessment.
McGuinness, Julia E; Zhang, Tianmai M; Cooper, Kevin; Kelkar, Arusha; Dimond, Jill; Lorenzi, Virginia; Crew, Katherine D; Kukafka, Rita.
Affiliation
  • McGuinness JE; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Zhang TM; Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Cooper K; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
  • Kelkar A; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Dimond J; Sassafras Tech Collective, Ann Arbor, MI, USA.
  • Lorenzi V; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
  • Crew KD; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
  • Kukafka R; Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
AMIA Annu Symp Proc ; 2021: 843-852, 2021.
Article in En | MEDLINE | ID: mdl-35308910
ABSTRACT
Women at high risk for breast cancer may benefit from enhanced screening and risk-reduction strategies. However, limited time during clinical encounters is one barrier to routine breast cancer risk assessment. We evaluated if electronic health record (EHR) data downloaded using Fast Healthcare Interoperability Resources (FHIR) is sufficient for breast cancer risk calculation in our decision support tools, RealRisks and BNAV. We accessed EHR data using FHIR for six patient advocates, and downloaded and parsed XML documents. We searched for relevant clinical variables, and evaluated if data was sufficient to calculate risk using validated models (Gail, Breast Cancer Screening Consortium [BCSC], BRCAPRO). While only one advocate had sufficient EHR data to calculate risk using the BCSC model only, we identified variables including age, race/ethnicity, mammographic density, and prior breast biopsy in most advocates. EHR data from FHIR could be incorporated into automated breast cancer risk calculation in clinical decision support tools.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms / Electronic Health Records Type of study: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Female / Humans Language: En Journal: AMIA Annu Symp Proc Journal subject: INFORMATICA MEDICA Year: 2021 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Breast Neoplasms / Electronic Health Records Type of study: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Female / Humans Language: En Journal: AMIA Annu Symp Proc Journal subject: INFORMATICA MEDICA Year: 2021 Document type: Article Affiliation country: United States