Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery models

利用电子病历基础设施来识别有资格接受遗传性癌症基因检测的初级保健患者，并评估新型癌症遗传学服务提供模式

• 批准号: 10241936
• 负责人: KIMBERLY A KAPHINGST
• 金额: $ 102.48万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-18 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241936
• 关键词: Address; Adherence; Affective; Cancer Patient; Categories; Characteristics; Clinic; Clinical Informatics; Cognitive; Collection; Communication; Comprehension; Computerized Medical Record; Development; Distress; Effectiveness; Electronic Health Record; Ethnic Origin; Ethnic group; Evaluation; Family; Family history of; Genetic Counseling; Genetic Models; Genetic Models for Cancer; Genetic Risk; Genetic Services; Health Personnel; Health Services Accessibility; Healthcare Systems; Hereditary Malignant Neoplasm; Improve Access; Individual; Infrastructure; Inherited; Malignant Neoplasms; Methods; Minority Groups; Modeling; New York; Patient Care; Patient-Focused Outcomes; Patients; Population; Population Sciences; Predisposition; Primary Health Care; Provider; Race; Randomized; Randomized Controlled Trials; Reaction; Recommendation; Recording of previous events; Regrets; Resources; Service delivery model; Subgroup; Testing; Translational Research; Uncertainty; Underserved Population; Universities; Utah; base; cancer diagnosis; cancer genetics; cancer prevention; cancer risk; care providers; clinical care; clinical decision support; clinically significant; design; education resources; genetic counselor; genetic risk assessment; genetic testing; implementation framework; implementation study; innovation; meetings; novel; open source; patient portal; patient response; population based; racial and ethnic; racial minority; randomized trial; response; risk perception; rural area; rurality; screening; service delivery; standard of care; tool; treatment strategy; trial design; uptake

PROJECT SUMMARY Identifying individuals with inherited cancer susceptibility is critical for targeted cancer prevention, screening, and treatment. Strategies to assess the genetic risk of unaffected individuals are needed. Scalable and sustainable methods to automatically extract and analyze family history information routinely captured in the electronic health record (EHR) can identify primary care patients appropriate for cancer genetic services. Increased patient ascertainment needs to be paired with implementation studies to compare models of delivering genetic services, including patient-directed models. Because access to services continues to be a barrier for those from minority racial and ethnic groups and rural areas, examining responses to different delivery models across population subgroups is essential. This study will employ an implementation science framework to test a replicable EHR-based clinical decision support (CDS) infrastructure to: (i) automatically identify unaffected patients from 48 primary care clinics in two healthcare systems, University of Utah and New York University, who qualify for cancer genetic services (Aim 1); and (ii) compare two models of genetic services delivery for 1,920 primary care patients using a randomized trial design with clinic-level randomization (Aims 2 and 3). We hypothesize that the CDS infrastructure will identify additional patients who have not been previously referred (Aim 1) and that uptake of genetic testing (Aim 2) and adherence to management recommendations (Aim 3) will be equivalent between the models. To address Aim 1, we will evaluate whether the CDS approach identifies patients who have not previously been referred, and whether this varies by race/ethnicity and rurality. To address Aim 2, we will compare: a patient-directed model in which those identified by the CDS infrastructure as meeting testing criteria will be informed of their cancer risks, provided with educational resources, and offered the option to select genetic testing through a patient portal to an enhanced standard of care model in which providers and patients are notified through CDS when criteria are met and of the availability of standard of care genetic counseling. We will compare uptake of genetic testing by model and whether this differs by race/ethnicity and rurality. In Aim 3, we will compare the effects of the two delivery models on adherence to recommendations 12 months after return of results, examining differences in effects by race/ethnicity and rurality. Innovative features include implementation of population-based CDS assessment of family history information available in the EHR; comparison of outcomes of patient-directed and enhanced standard of care delivery models; and focus on impact of race/ethnicity and rurality. This highly impactful translational research builds on our unique strengths in cancer genetics, clinical informatics, and population sciences, and addresses issues of immediate clinical significance, including increasing hereditary cancer genetic testing in appropriate patients and improving access for underserved groups.

项目概要 识别具有遗传性癌症易感性的个体对于有针对性的癌症预防、筛查、 和治疗。需要评估未受影响个体的遗传风险的策略。可扩展和 自动提取和分析家庭历史信息的可持续方法 电子健康记录（EHR）可以识别适合癌症遗传服务的初级保健患者。 增加患者确定需要与实施研究相结合，以比较模型 提供遗传服务，包括以患者为导向的模型。因为获得服务仍然是一个 针对来自少数种族和族裔群体以及农村地区的人的障碍，研究对不同群体的反应 跨人群的交付模式至关重要。本研究将采用实施科学 框架来测试可复制的基于 EHR 的临床决策支持 (CDS) 基础设施，以：(i) 自动 从犹他大学和新罕布什尔州两个医疗保健系统的 48 个初级保健诊所中确定未受影响的患者 约克大学，有资格获得癌症遗传服务（目标 1）； (ii) 比较两种遗传模型 使用临床水平随机化的随机试验设计为 1,920 名初级保健患者提供服务 （目标 2 和 3）。我们假设 CDS 基础设施将识别更多尚未接受过治疗的患者 先前提到的（目标 1）以及基因检测的采用（目标 2）和遵守管理 模型之间的建议（目标 3）将是等效的。为了实现目标 1，我们将评估是否 CDS 方法可识别之前未转诊的患者，以及这是否会因情况而异 种族/民族和乡村。为了实现目标 2，我们将比较： 以患者为导向的模型，其中 由 CDS 基础设施确定为符合测试标准的，将被告知其癌症风险，前提是 提供教育资源，并提供通过患者门户网站选择基因检测的选项 增强的护理标准模型，当标准达到时，通过 CDS 通知提供者和患者 是否符合标准以及遗传咨询护理标准的可用性。我们将比较基因检测的采用率 模型以及这是否因种族/民族和农村地区而异。在目标3中，我们将比较两者的效果 结果返回后 12 个月内遵守建议的交付模型，检查 种族/民族和乡村的影响。创新功能包括实施基于人群的 CDS 评估电子病历中提供的家族史信息；以患者为导向的结果与 提高护理服务模式的标准；并关注种族/民族和乡村的影响。这高度 有影响力的转化研究建立在我们在癌症遗传学、临床信息学和 人口科学，并解决具有直接临床意义的问题，包括增加遗传性 对适当患者进行癌症基因检测，并改善服务不足群体的检测机会。