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: 10594168
• 负责人: KIMBERLY A KAPHINGST
• 金额: $ 10.49万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594168
• 关键词: Address; Adherence; Administrative Supplement; Affective; Algorithms; Clinic; Cognitive; Computerized Medical Record; Data; Education; Electronic Health Record; Ethical Issues; Ethics; Ethnic Origin; Evaluation; Family; Family Cancer History; Future; Genetic; Genetic Models for Cancer; Genetic Risk; Genetic Services; Goals; Guidelines; Health Services Accessibility; Healthcare Systems; Hereditary Malignant Neoplasm; Individual; Infrastructure; Inherited; Language; Malignant Neoplasms; Modeling; Monitor; National Comprehensive Cancer Network; Natural Language Processing; New York; Outcome; Parents; Patient Care; Patient-Focused Outcomes; Patients; Phase; Policies; Predisposition; Primary Health Care; Published Comment; Race; Randomized; Randomized Clinical Trials; Recommendation; Recording of previous events; Rural; Sampling; Service delivery model; Specific qualifier value; Statistical Models; Testing; Text; Universities; Utah; base; cancer genetics; cancer prevention; clinical care; clinical decision support; cohort; community engagement; development policy; disparity reduction; electronic structure; evidence base; frontier; genetic testing; implementation framework; medically underserved; medically underserved population; novel; outreach; patient population; preference; racial disparity; randomized trial; residence; response; screening; service delivery; standard of care; tool; treatment strategy; trial comparing; trial design; underserved community; uptake

SUPPLEMENT ABSTRACT This application is being submitted in response to NOT-OD-22-026 as a proposed administrative supplement to the University of Utah (Utah)/New York University (NYU) U01 entitled “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” (U01 CA232826). The parent U01 is employing an electronic health record (EHR)-based clinical decision support (CDS) infrastructure to: (i) identify unaffected primary care patients in the Utah and NYU healthcare systems who qualify for cancer genetics services based on current guidelines (Aim 1); and (ii) compare two models of cancer genetics services delivery for identified patients in a randomized clinical trial (Aims 2 and 3). In the parent study, we have identified a cohort of 22,208 primary care patients in the two healthcare systems who are eligible for cancer genetic services, and we are planning for sustainability of this project. However, our prior data has shown disparities by race, ethnicity, and language preference in the pool identified by the current CDS algorithm compared to the underlying primary care patient populations. We have found that at least one contributing factor is systematic disparities in availability and comprehensiveness of cancer family history information available in the structured EHR fields upon which the current algorithm is based. These disparities raise critical ethical issues related to bias resulting from integration of the CDS algorithm. We therefore propose to: (Supplemental Aim 1) Investigate whether incorporating natural language processing (NLP) tools into the CDS algorithm reduces disparities in identification of eligible primary care patients. We have created an NLP-augmented algorithm that incorporates free-text comments and identifies 54% more patients than the current CDS algorithm. We will examine whether use of the NLP-augmented algorithm impacts disparities in identification by race, ethnicity, language preference, and rural/frontier residence compared with the underlying patient population. (Supplemental Aim 2) Identify patients who nearly meet criteria for cancer genetic evaluation and explore acceptability of outreach to those patients from medically underserved communities. We will develop a statistical model that can identify patients who nearly meet criteria for cancer genetic evaluation, which would allow us to target outreach to collect additional family history information. We will also conduct two community engagement studios, one in English and one in Spanish, to examine the acceptability of direct outreach after identification by an algorithm among individuals from medically underserved communities. Together these supplemental aims will investigate two potential approaches to address observed disparities in patient identification. These findings will directly inform the development of policies that explicitly include monitoring the impact of CDS algorithms and AI tools on patient outcomes and disparities in those outcomes during iterative phases of testing and implementation.

补充摘要 该申请正在响应于NOT-OD-22-026作为拟议的行政补充 到犹他大学（犹他大学）/纽约大学（纽约大学）U01标题为“利用电子医疗 记录基础设施，以识别有资格参加基因测试的基础护理患者和 评估新颖的癌症遗传学服务输送模型”（U01 CA232826）。父级U01正在采用 电子健康记录（EHR）基于临床决策支持（CD）基础设施：（i）确定不受影响的 犹他州和纽约大学医疗保健系统的初级保健患者有资格获得癌症遗传学服务 根据当前的准则（AIM 1）； （ii）比较确定的癌症遗传学服务的两种模型 随机临床试验中的患者（目标2和3）。在家长研究中，我们确定了22,208的队列 两个医疗保健系统中有资格获得癌症遗传服务的初级保健患者，我们是 计划该项目的可持续性。但是，我们先前的数据显示了种族，种族和 与基本主要的主要CDS算法确定的池中的语言偏好 护理患者人群。我们发现，至少有一个促成因素是系统分布 结构化EHR领域可用的癌症家族史信息的可用性和全面性 当前算法基于的算法。这些分布引发了与偏见有关的关键道德问题 来自CD算法的集成。因此，我们建议：（补充目标1）调查是否是否 将自然语言处理（NLP）工具纳入CDS算法可减少分布 识别合格的初级保健患者。我们创建了一种纳入NLP的算法，该算法合并 自由文本评论并确定患者比当前的CD算法多54％。我们将检查是否 使用NLP的算法的使用会影响种族，种族，语言识别的分布 与潜在的患者人群相比，偏好和粗糙/边境住宅。 （补充目标2） 确定几乎符合癌症遗传评估标准的患者 那些来自医学欠缺社区的患者。我们将开发一个统计模型，可以识别 几乎符合癌症遗传评估标准的患者，这将使我们能够靶向推广 收集其他家族史信息。我们还将开展两个社区参与工作室，一个 英语和西班牙语中的一个，以检查算法识别后直接外展的可接受性 在医学上服务不足的社区的个人中。这些补充目标将共同调查 解决患者识别中观察到的差异的两种潜在方法。这些发现将直接 告知明确包括监视CD算法和AI工具的影响的政策的制定 关于在测试和实施的迭代阶段，这些结果的患者结果和差异。

项目成果

Barriers to family history collection among Spanish-speaking primary care patients: a BRIDGE qualitative study.

讲西班牙语的初级保健患者收集家族史的障碍：一项 BRIDGE 定性研究。

• DOI: 10.1016/j.pecinn.2022.100087
• 发表时间: 2022
• 期刊: PEC innovation
• 作者: Liebermann,Erica;Taber,Peter;Vega,AlexisS;Daly,BrianneM;Goodman,MelodyS;Bradshaw,Richard;Chan,PriscillaA;Chavez-Yenter,Daniel;Hess,Rachel;Kessler,Cecilia;Kohlmann,Wendy;Low,Sara;Monahan,Rachel;Kawamoto,Kensaku;DelFiol,Gui
• 通讯作者: DelFiol,Gui

Association of Disparities in Family History and Family Cancer History in the Electronic Health Record With Sex, Race, Hispanic or Latino Ethnicity, and Language Preference in 2 Large US Health Care Systems.

• DOI: 10.1001/jamanetworkopen.2022.34574
• 发表时间: 2022-10-03
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Chavez-Yenter, Daniel;Goodman, Melody S.;Chen, Yuyu;Chu, Xiangying;Bradshaw, Richard L.;Chambers, Rachelle Lorenz;Chan, Priscilla A.;Daly, Brianne M.;Flynn, Michael;Gammon, Amanda;Hess, Rachel;Kessler, Cecelia;Kohlmann, Wendy K.;Mann, Devin M.;Monahan, Rachel;Peel, Sara;Kawamoto, Kensaku;Del Fiol, Guilherme;Sigireddi, Meenakshi;Buys, Saundra S.;Ginsburg, Ophira;Kaphingst, Kimberly A.
• 通讯作者: Kaphingst, Kimberly A.