Integrating EHR and patient-generated health data for breast cancer risk assessment and decision support in a diverse multiethnic population

虽然乳腺癌（BC）死亡率有所下降，但这种下降已经开始趋于平稳，特别是在 种族/少数民族。被确定为BC高风险的妇女可能受益于化学预防，BC检测 易感基因、筛查和其他个性化的降低风险策略;然而，存在障碍，包括 对人口中每名妇女进行风险评估所需的时间。电子健康记录（EHR）， 用于填充风险评估模型的共同来源带来了挑战，包括数据缺失， 与电子病历相比，由患者提供的类型更准确。我们之前提取了关于年龄的EHR数据， 种族/民族、BC家族史、良性乳腺疾病和乳腺密度，以根据 乳腺癌监测联盟（BCSC）在9，514名妇女中进行了研究。比较自我报告和 EHR数据显示，有一级BC家族史（14.6% vs. 4.4%）和良性乳腺活检的女性更多 (21.3%与11.3%），但EHR数据确定了更多的女性患有乳腺癌 或小叶原位癌（1.1% vs.2.3%）。EHR缺少26.8%女性的种族/民族数据 一级BC家族史者占87.2%。健康、快速的医疗保健互操作性资源 （FHIR）、应用程序编程接口（API）和新的立法为自动化的 业务连续性风险评估，整合患者生成的健康数据和EHR数据， 每一种方法。在之前的工作中，我们使用迭代设计过程开发了RealRisks决策辅助工具， 公平地最大化可接受性和可用性。RealRisks促进对BC风险的理解，并收集 根据Gail模型、BCSC和BRCAPRO计算BC风险的患者输入数据。当FHIR 当可用时，我们更新了RealRisks，以自动填充BC风险计算信息， EHR，并设计了一个原型界面，向患者显示这些数据，并要求进行审查和修改 在进行风险评估之前，我们最近进行了一项可行性研究，证明EHR 来自FHIR的数据可以被并入自动BC风险计算。为了增加 制定可传播和公平的战略，整合EHR和PGDH数据进行风险评估， 个性化的BC风险降低，这个R21的重点是完善和测试我们的方法在不同的多民族 妇女我们的目标是：1）进行用户评估，以完善FHIR增强的RealRisks; 2）评估 FHIR增强的RealRisks对患者激活、风险感知和多种族试验研究中的可用性的影响 高风险妇女;和3）确定多层次的障碍，以实施FHIR增强RealRisks到临床护理。 鉴于与BC相关的致命性，需要集中精力提供准确的风险评估， 关于减少风险战略的共同决策，特别是在少数民族妇女中， 诊断为晚期乳腺癌如果成功，本应用程序中测试的方法可能会提供路线图 广泛改善健康数据的数字访问并以公平的方式降低BC省死亡率。

While breast cancer (BC) mortality has declined, this decline has begun to plateau, particularly among racial/ethnic minorities. Women identified as high-risk for BC may benefit from chemoprevention, testing for BC susceptibility genes, screening, and other personalized risk-reducing strategies; however, barriers exist including the time required to conduct risk assessment of each woman in a population. Electronic health records (EHRs), a common source for populating risk assessment models present challenges, including missing data, and data type more accurate when provided by patients compared to EHRs. We previously extracted EHR data on age, race/ethnicity, family history of BC, benign breast disease, and breast density to calculate BC risk according to the Breast Cancer Surveillance Consortium (BCSC) model among 9,514 women. Comparing self-reported and EHR data, more women with a first-degree family history of BC (14.6% vs. 4.4%) and benign breast biopsies (21.3% vs. 11.3%) were identified with patient-reported data, but EHR data identified more women with atypia or lobular carcinoma in situ (1.1% vs. 2.3%). The EHR had missing data on race/ethnicity for 26.8% of women and on first-degree family history of BC for 87.2%. Opportunely, Fast Healthcare Interoperability Resources (FHIR), application programming interfaces (APIs), and new legislation offer an elegant solution for automated BC risk assessment that integrates both patient-generated health data and EHR data to harness the strengths of each approach. In prior work, we developed the RealRisks decision aid using an iterative design process to equitably maximize acceptability, and usability. RealRisks promotes understanding of BC risk and collects patient-entered data to calculate BC risk according to the Gail model, BCSC, and BRCAPRO. When FHIR became available, we updated RealRisks to automatically populate information for BC risk calculation from the EHR, and designed a prototype interface that shows this data to patients with a request to review and modify data before running the risk assessments. We recently conducted a feasibility study to demonstrate that EHR data from FHIR could be incorporated into automated BC risk calculation. To increase the likelihood of developing disseminatable and equitable strategies that integrate EHR and PGDH data for risk assessment and personalized BC risk-reduction, the focus of this R21 is to refine and test our approach among diverse multiethnic women. Our aims are: 1) conduct user evaluations to refine FHIR-enhanced RealRisks; 2) assess the effect of the FHIR-enhanced RealRisks on patient activation, risk perception, and usability in a pilot study of multiethnic high-risk women; and 3) identify multilevel barriers to implementing FHIR-enhanced RealRisks into clinical care. Given the mortally associated with BC, focused efforts are needed to provide accurate risk assessment and shared decision-making about risk-reducing strategies, especially in minority women who are more likely to be diagnosed with advanced stage BC. If successful, the approach tested in this application may provide a roadmap for broadly improving digital access to health data and reducing BC mortality in an equitable manner.