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.

项目总结 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。需要制定战略来评估未受影响个体的遗传风险。可扩展且 使用可持续的方法自动提取和分析在 电子健康记录(EHR)可以识别适合癌症基因服务的初级保健患者。 增加患者确诊率需要与实施研究相结合，以比较 提供遗传服务，包括以患者为导向的模型。因为访问服务仍然是一种 为那些来自少数族裔和民族以及农村地区的人设置障碍，审查对不同 跨人口亚群的交付模式至关重要。这项研究将采用实施学。 测试可复制的基于电子病历的临床决策支持(CDS)基础设施的框架，以：(I)自动 从犹他大学和纽约大学两个医疗保健系统的48个初级保健诊所确定未受影响的患者 约克大学，谁有资格获得癌症遗传服务(目标1)；和(2)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1920名初级保健患者提供服务 (目标2和3)。我们假设CDS基础设施将识别出更多没有 以前提到(目标1)和接受基因测试(目标2)和坚持管理 两个模型之间的建议(目标3)将是相同的。为了解决目标1，我们将评估是否 CDS方法确定以前没有转诊过的患者，以及这一点是否因 种族/民族和乡村。为了解决目标2，我们将比较：以患者为导向的模式，其中 CDS基础设施确定符合检测标准的，将被告知其癌症风险，条件是 使用教育资源，并提供通过患者门户选择基因检测的选项 增强的护理标准模式，当标准符合以下条件时，通过CDS通知提供者和患者 MET和提供标准护理遗传咨询的问题。我们将比较基因检测的吸收情况 模型，以及这是否因种族/民族和乡村而不同。在目标3中，我们将比较两者的效果 在结果返回12个月后遵守建议的交付模式，检查 种族/民族和乡村的影响。创新功能包括实施基于人口的CDS EHR中可获得的家族史信息的评估；以患者为导向的和以患者为导向的和 提高提供护理模式的标准；注重种族/族裔和乡村的影响。如此之高 有影响力的翻译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 在适当的患者中进行癌症基因测试，并改善服务不足群体的可及性。