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

资料摘要 本申请是作为对NOT-OD-22-026的响应而提交的，作为拟议的行政补充 犹他州（犹他州）大学/纽约大学（NYU）U 01题为“利用电子医疗 记录基础设施，以确定有资格接受遗传性癌症基因检测的初级保健患者， 评估新的癌症遗传学服务提供模式”（U 01 CA 232826）。母公司U 01正在使用 基于电子健康记录（EHR）的临床决策支持（CDS）基础设施，以：（i）识别未受影响的 犹他州和纽约大学医疗保健系统中有资格获得癌症遗传学服务的初级保健患者， （ii）比较两种癌症遗传学服务模式， 随机临床试验中的患者（目的2和3）。在母研究中，我们确定了一个22，208人的队列 两个医疗系统中有资格获得癌症遗传服务的初级保健患者，我们 规划该项目的可持续性。然而，我们先前的数据显示，种族、民族和 与基础主要语言相比，当前CDS算法识别的语言偏好 照顾病人群体。我们发现，至少有一个促成因素是系统性差异， 结构化EHR字段中可用的癌症家族史信息的可用性和全面性 当前算法所基于的。这些差异引起了与偏见有关的关键道德问题， 从CDS算法的集成。因此，我们建议：（补充目标1）调查是否 将自然语言处理（NLP）工具结合到CDS算法中减少了 确定合格的初级保健患者。我们已经创建了一个NLP增强算法， 自由文本注释和识别比当前CDS算法多54%的患者。我们将研究是否 NLP增强算法的使用影响了种族、民族、语言 偏好和农村/边境居住与基础患者人群相比。（补充目标2） 确定几乎符合癌症遗传评估标准的患者，并探索外展的可接受性， 这些病人来自医疗条件差的社区。我们将建立一个统计模型， 几乎符合癌症遗传评估标准的患者，这将使我们能够将外展目标对准 收集更多的家族史信息。我们还将举办两个社区参与工作室，一个在 一份是英文，一份是西班牙文，以审查通过算法确定后直接外联的可接受性 在医疗服务水平低下的社区中。这些补充目标将共同研究 两种潜在的方法来解决观察到的患者识别差异。这些发现将直接 为明确包括监测CDS算法和人工智能工具影响的政策制定提供信息 在测试和实施的迭代阶段，患者的结果和这些结果的差异。

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.