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

整合 EHR 和患者生成的健康数据，以在不同的多种族人群中进行乳腺癌风险评估和决策支持

• 批准号: 10510135
• 负责人: Rita Kukafka
• 金额: $ 23.92万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-21 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10510135
• 关键词: Advocate; Affect; Atypia; Benign; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Breast Cancer Surveillance Consortium; Breast Diseases; Breast biopsy; Cancer-Predisposing Gene; Cessation of life; Chemoprevention; Clinical; Data; Data Reporting; Decision Aid; Diagnosis; Early Diagnosis; Electronic Health Record; Eligibility Determination; Ethnic Origin; Evaluation; Family; Family history of; Fast Healthcare Interoperability Resources; Feasibility Studies; Genetic Counseling; Goals; Health; High Risk Woman; Interview; Malignant Neoplasms; Measures; Methods; Minority Women; Modeling; Participant; Pathogenicity; Patient Self-Report; Patient-Focused Outcomes; Patients; Pilot Projects; Policies; Population; Process; Questionnaires; Race; Recommendation; Recording of previous events; Risk; Risk Assessment; Risk Reduction; Running; Sampling; Source; Statutes and Laws; Structure; Study models; Testing; Text; Time; United States; Update; Variant; Woman; Work; advanced breast cancer; application programming interface; base; brca gene; breast density; cancer prevention; clinical care; clinical decision support; design; digital; early screening; ethnic minority; follow-up; health data; high risk; improved; interoperability; iterative design; lifetime risk; lobular breast carcinoma in situ; malignant breast neoplasm; mortality; multi-ethnic; prototype; racial and ethnic; risk perception; risk prediction model; risk sharing; screening; shared decision making; support tools; usability; web site

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.

虽然乳腺癌 (BC) 死亡率有所下降，但这种下降趋势已开始趋于稳定，特别是在乳腺癌患者中 少数种族/族裔。被确定为 BC 高风险的女性可能会受益于化学预防和 BC 检测 易感基因、筛查和其他个性化风险降低策略；然而，存在障碍，包括 对人口中的每位女性进行风险评估所需的时间。电子健康记录 (EHR)、 填充风险评估模型的通用来源提出了挑战，包括缺失数据和数据 与 EHR 相比，患者提供的类型更准确。我们之前提取了有关年龄的 EHR 数据， 种族/民族、BC 家族史、良性乳腺疾病和乳腺密度，根据以下内容计算 BC 风险 乳腺癌监测联盟 (BCSC) 模型的研究对象为 9,514 名女性。比较自我报告和 EHR 数据，更多女性有 BC 一级家族史（14.6% vs. 4.4%）和良性乳腺活检 （21.3% vs. 11.3%）是根据患者报告的数据确定的，但 EHR 数据确定了更多患有异型性的女性 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 BC 一级家族史占 87.2%。恰巧、快速的医疗保健互操作性资源 (FHIR)、应用程序编程接口 (API) 和新立法为自动化提供了优雅的解决方案 BC 风险评估整合了患者生成的健康数据和 EHR 数据以发挥优势 每种方法的。在之前的工作中，我们使用迭代设计流程开发了 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 风险的了解并收集 患者输入的数据根据​​盖尔模型、BCSC 和 BRCAPRO 计算 BC 风险。当FHIR 可用后，我们更新了 RealRisks，以自动填充来自 BC 风险计算的信息 EHR，并设计了一个原型界面，向请求查看和修改的患者显示这些数据 运行风险评估之前的数据。我们最近进行了一项可行性研究，以证明 EHR 来自 FHIR 的数据可以纳入自动 BC 风险计算中。为了增加可能性 制定可传播且公平的战略，整合 EHR 和 PGDH 数据以进行风险评估和 个性化 BC 风险降低，R21 的重点是在不同的多种族人群中完善和测试我们的方法 女性。我们的目标是：1）进行用户评估以完善 FHIR 增强的 RealRisks； 2）评估效果 在多种族试点研究中，FHIR 增强的 RealRisks 对患者激活、风险感知和可用性的影响 高危女性； 3) 确定在临床护理中实施 FHIR 增强型 RealRisks 的多层次障碍。 鉴于 BC 具有致命性，需要集中精力提供准确的风险评估和 关于降低风险策略的共同决策，特别是对于更有可能遭受风险的少数族裔妇女而言 诊断为晚期 BC。如果成功，此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化获取并以公平的方式降低 BC 死亡率。