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.

项目摘要 识别具有遗传性癌症敏感性的个体对于靶向癌症预防，筛查， 和治疗。需要评估不受影响的人的遗传风险的策略。可扩展和 可持续方法自动提取和分析家族史信息常规捕获的家族史信息 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。 患者的确定性增加需要与实施研究配对，以比较 提供遗传服务，包括患者指导的模型。因为获得服务继续是 来自少数族裔和种族和农村地区的人的障碍，检查对不同的反应 跨种群亚组的交付模型至关重要。这项研究将采用实施科学 测试基于可复制EHR的临床决策支持（CD）基础架构的框架 在两个医疗保健系统中识别来自48家初级保健诊所的未受影响的患者，犹他大学和新大学 约克大学，有资格获得癌症遗传服务（AIM 1）； （ii）比较两个遗传模型 使用诊所级随机化的随机试验设计为1,920名初级保健患者提供服务交付 （目标2和3）。我们假设CD基础设施将确定其他未曾发生的患者 以前被推荐（AIM 1）以及对基因检测的吸收（AIM 2）和遵守管理 建议（AIM 3）在模型之间等效。要解决AIM 1，我们将评估是否 CD方法确定了以前未被转诊的患者，这是否因 种族/种族和乡村。要解决AIM 2，我们将比较：一个以患者为导向的模型 通过CDS基础设施确定为满足测试标准的癌症风险 借助教育资源，并提供了通过患者门户选择基因测试的选择 增强的护理标准模型，在标准为时通过CD通知提供者和患者 MET和护理标准遗传咨询的可用性。我们将比较通过 模型以及这是否因种族/种族和乡村而有所不同。在AIM 3中，我们将比较两者的效果 结果返回后12个月遵守建议的交付模型，检查 种族/种族和乡村的影响。创新功能包括实施基于人群的CD 评估家族史信息，可在EHR中获得；比较患者指导的结果和 增强的护理标准交付模型；并专注于种族/种族和乡村的影响。这很高 有影响力的翻译研究以我们在癌症遗传学，临床信息学和 人口科学，并解决了直接临床意义的问题，包括增加世袭 适当患者的癌症基因检测并改善了服务欠缺的组的进入。