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）死亡率下降，但这种下降已经开始平稳，特别是 种族/族裔少数民族。识别为BC高风险的妇女可能会受益于化学预防，测试BC 易感基因，筛查和其他个性化降低风险的策略；但是，存在障碍包括 对人口中每个妇女进行风险评估所需的时间。电子健康记录（EHRS）， 填充风险评估模型的常见来源提出了挑战，包括丢失的数据和数据 与EHR相比，患者提供时键入更准确。我们以前提取了有关年龄的EHR数据， 种族/种族，卑诗省的家族史，良性乳房疾病和乳房密度，以计算卑诗省风险 9,514名妇女中的乳腺癌监测财团（BCSC）模型。比较自我报告和 EHR数据，更多的女性具有一级家族史（14.6％vs. 4.4％）和良性乳房活检 （21.3％比11.3％）通过患者报告的数据确定 或原位小叶癌（1.1％比2.3％）。 EHR缺少有关26.8％女性种族/民族的数据 以及卑诗省的一级家族史，为87.2％。及时，快速的医疗保健互操作性资源 （FHIR），应用程序编程界面（API）和新立法为自动化提供了优雅的解决方案 BC风险评估，将患者生成的健康数据和EHR数据整合在一起以利用优势 每种方法。在先前的工作中，我们使用迭代设计过程开发了Realrisks的决策援助 公平地提高可接受性和可用性。 Realrisks促进了对BC风险的理解并收集 根据Gail模型，BCSC和BRCAPRO计算患者输入的数据以计算BC风险。当fhir 我们更新了Realrisks，以自动填充来自BC风险计算的信息 EHR，并设计了一个原型接口 进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 FHIR的数据可以纳入自动化的BC风险计算中。增加的可能性 制定散发性和公平的策略，将EHR和PGDH数据整合在一起，以进行风险评估和 个性化的卑诗省风险降低，该R21的重点是完善和测试我们的多种种族的方法 女性。我们的目的是：1）进行用户评估以完善增强型号的Reanrisks； 2）评估效果 在多民族的试点研究中，对患者激活，风险感知和可用性的增强型房地产 高风险妇女； 3）确定将FHIR增强的Reanrisks实施到临床护理中的多层次障碍。 鉴于与卑诗省的致命性相关，需要集中精力来提供准确的风险评估和 关于降低风险策略的共同决策，尤其是在少数族裔女性中 被诊断为BC高级阶段。如果成功，在此应用程序中测试的方法可能会提供路线图 以公平的方式广泛改善数字访问卫生数据并降低BC死亡率。