Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
AMIA Annu Symp Proc. 2022 Feb 21;2021:843-852. eCollection 2021.
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.
高危乳腺癌女性可能受益于增强筛查和降低风险策略。然而,临床就诊时间有限是常规乳腺癌风险评估的障碍之一。我们评估了使用快速医疗互操作性资源 (FHIR) 下载的电子健康记录 (EHR) 数据是否足以用于我们的决策支持工具 RealRisks 和 BNAV 中的乳腺癌风险计算。我们使用 FHIR 为六位患者代言人访问 EHR 数据,并下载和解析 XML 文档。我们搜索了相关的临床变量,并评估了数据是否足以使用经过验证的模型 ( Gail 、乳腺癌筛查联盟 [BCSC] 、 BRCAPRO ) 计算风险。虽然只有一位代言人有足够的 EHR 数据可以使用 BCSC 模型计算风险,但我们在大多数代言人中确定了包括年龄、种族/民族、乳房 X 光密度和既往乳腺活检等变量。FHIR 的 EHR 数据可以纳入临床决策支持工具中的自动乳腺癌风险计算。