An Electronic Health Record-Based Tool to Identify Newly Diagnosed Breast Cancer Patients at Risk of Low Social Support

一种基于电子健康记录的工具，用于识别新诊断的乳腺癌患者面临社会支持低的风险

• 批准号: 10408831
• 负责人: CANDYCE H KROENKE
• 金额: $ 53.83万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10408831
• 关键词: Academy; Address; African American; Area; Asian; Asian Pacific Islander; Black race; Breast Cancer Patient; Breast Cancer Treatment; California; Cancer Research Network; Caring; Clinical; Code; Cohort Studies; Data; Data Collection; Development; Diagnosis; Documentation; Electronic Health Record; Emergency Situation; Ethnic Origin; Ethnic group; Funding; Health; Healthcare Systems; Hispanic; Information Technology; Intervention; Language; Linear Regressions; Link; Literature; Logistic Regressions; Logistics; Marital Status; Measures; Medical; Medical Care Team; Medical Records; Medicine; Minority Women; Natural Language Processing; Newly Diagnosed; Not Hispanic or Latino; Operative Surgical Procedures; Outcome; Pathway interactions; Patient Care; Patient risk; Patient-Focused Outcomes; Patients; Perception; Pilot Projects; Population; Provider; Publishing; Questionnaires; ROC Curve; Race; Recommendation; Research; Research Personnel; Resources; Risk; Services; Social Conditions; Social Network; Social support; Source; Structure; Text; Treatment outcome; Woman; Work; analytical method; anticancer research; base; cancer care; cancer health disparity; chemotherapy; clinical care; clinically relevant; cohort; ethnic diversity; hazard; health care settings; high risk; hormone therapy; improved; improved outcome; malignant breast neoplasm; mortality; patient response; racial and ethnic; racial and ethnic disparities; racial diversity; social; social computing; social health determinants; social relationships; social structure; structured data; theories; tool; unstructured data

ABSTRACT Social support, a social determinant of health (SDoH), is a definitive predictor of breast cancer (BC) treatment and mortality outcomes. Because of the recognition that social support is critical to BC patient outcomes, clinicians within Kaiser Permanente Northern California (KPNC) have documented information on social support in the electronic health record (EHR) since the advent of Epic in 2005. However, no EHR-based social support measure currently exists to help clinicians identify patients at high risk of low social support. Such a measure has high relevance for addressing racial/ethnic disparities in BC treatment and outcomes. Therefore, we propose to develop an Electronic Health Record Social Support Patient Risk Tool (EHR-SUPPORT) that could be used to identify women with BC at risk of low social support for referral to social support resources. We propose to: 1) Identify terms in the EHR, based on theory and prior literature, and informed by KPNC stakeholders in BC care, that reflect structural and/or functional social support, and have been associated with BC treatment and outcomes; 2) Develop EHR-SUPPORT, using structured, semi-structured, and unstructured data (to include natural language processing of text) that identifies patients at risk of low social support, overall and by race/ethnicity, and validate the measure against published social support measures; and 3) Evaluate associations of EHR-SUPPORT and its component variables with BC treatment (surgery and chemotherapy delays, nonadherence to hormonal therapy) and BC-specific and total mortality, overall and by race/ethnicity in 44,348 women diagnosed with stage I-IV BC within Kaiser Permanente Northern California between 2006- 2023 including 3,450 Black, 4,441 Hispanic, 6,571 Asian women, and 28,589 non-Latina white women. In an exploratory aim, we will develop, with KP clinician stakeholders, steps to the implementation of EHR- SUPPORT. We will review 100 medical records (25 in each race/ethnic group) within two months of diagnosis, informed by investigator expertise and clinician stakeholders, to develop terms used to describe patient support. In addition to developing structured data, we will use natural language processing of text fields to further develop social support indicators (Aim 1). EHR-SUPPORT will be computed from social support indicators; we will use linear and logistic regression to validate the developed measure against established social support measures in Pathways, a well-established cohort of 4,505 women with BC and use factor analytic and confirmatory factor analytic methods as well as ROC curves to further evaluate the score (Aim 2). We will use linear, logistic, and Cox proportional hazards regression to evaluate associations in Aim 3. The unique convergence of EHR and cohort data provides the first opportunity to develop and validate an EHR- based social support measure in in diverse women with BC, adjusted for an extensive set of covariates. This work is central to identifying patients at elevated risk of low social support and to enhancing social support- cancer research needed to improve clinical care and reduce BC disparities.

抽象的 社会支持是健康的社会决定因素（SDOH），是乳腺癌（BC）治疗的确定性预测指标 和死亡率结果。由于认识到社会支持对卑诗省患者的结果至关重要，所以 Kaiser Permanente北加州（KPNC）内的临床医生已经记录了有关社会的信息 自2005年EPIC出现以来，电子健康记录（EHR）的支持。但是，没有基于EHR的社会 目前存在支持措施，以帮助临床医生确定社会支持低的高风险患者。这样的 措施对于解决卑诗省治疗和结果中的种族/族裔差异具有很高的相关性。所以， 我们建议开发电子健康记录社会支持患者风险工具（EHR-Support） 可以用来识别有卑诗省妇女的社会支持较低的风险，以转介到社会支持资源中。 我们建议：1）基于理论和先前的文献确定EHR中的术语，并由KPNC告知 卑诗省护理中的利益相关者，反映了结构性和/或功能性社会支持，并且与 卑诗省治疗和结果； 2）使用结构化，半结构和非结构化开发EHR支持 数据（包括文本的自然语言处理），以确定有社会支持低的风险的患者 并通过种族/种族，并验证该措施，以抵制已发表的社会支持措施； 3）评估 EHR支持及其成分变量与BC治疗的关联（手术和化学疗法 延迟，不遵守激素疗法）以及卑诗省特异性和总死亡率，总体和种族/种族 44,348名妇女在2006年之间被诊断出北加州的Kaiser Permanente内部I-IV阶段 2023年，其中包括3,450个黑人，4,441个西班牙裔，6,571名亚洲妇女和28,589名非拉丁裔白人妇女。在 探索目的，我们将与KP临床医生利益相关者一起发展，以实施EHR- 支持。我们将在诊断后的两个月内检查100个病历（每个种族/种族中的25个）， 由研究者专业知识和临床医生利益相关者告知，以开发用于描述患者的术语 支持。除了开发结构化数据外，我们还将使用文本字段的自然语言处理 进一步制定社会支持指标（AIM 1）。 EHR支持将根据社会支持计算 指标；我们将使用线性和逻辑回归来验证已开发的措施，以针对已建立 途径中的社会支持措施，这是一个公认的4,505名患有BC和使用因子的女性的队列 分析和验证性因素分析方法以及ROC曲线以进一步评估得分（AIM 2）。 我们将使用线性，逻辑和COX比例危害回归来评估AIM 3中的关联。 EHR和队列数据的独特收敛提供了开发和验证EHR-的第一个机会 基于BC女性的基于社会支持措施，并针对广泛的协变量进行了调整。这 工作是确定社会支持较低风险并增强社会支持的较高风险的核心 - 癌症研究需要改善临床护理并减少BC差异。