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: 10047252
• 负责人: CANDYCE H KROENKE
• 金额: $ 58.57万
• 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/10047252
• 关键词: Academy; Address; African American; Area; Asians; 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; Hispanics; Information Technology; Intervention; Language; Linear Regressions; Link; Literature; Logistic Regressions; Logistics; Marital Status; Measures; Medical; Medical Care Team; Medical Records; Medicine; Natural Language Processing; Newly Diagnosed; Not Hispanic or Latino; Operative Surgical Procedures; Outcome; Pacific Island Americans; 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; ethnic minority population; 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.

摘要 社会支持是健康的社会决定因素(SDoH)，是乳腺癌(BC)治疗的明确预测因素 和死亡结果。由于认识到社会支持对BC患者的预后至关重要， Kaiser Permanente North California(KPNC)的临床医生记录了有关社交网络的信息 自2005年EPIC问世以来，对电子健康记录(EHR)的支持。然而，没有基于电子病历的社交 目前存在支持措施，以帮助临床医生识别社会支持程度低的高危患者。这样的一个 这项措施对于解决卑诗省治疗和结果中的种族/民族差异具有很高的相关性。因此， 我们建议开发一个电子健康记录社会支持患者风险工具(EHR-Support)， 可用于确定卑诗省妇女的社会支持程度低的风险，以便转介到社会支持资源。 我们建议：1)根据理论和先前的文献，在KPNC的指导下，确定EHR中的术语 BC护理中的利益相关者，反映结构性和/或功能性社会支持，并与 BC治疗和结果；2)开发EHR支持，使用结构化、半结构化和非结构化 数据(包括文本的自然语言处理)，识别总体上处于低社会支持风险的患者 和种族/民族，并对照已公布的社会支持措施验证措施；以及3)评估 EHR支持及其成分变量与BC治疗(手术和化疗)的关系 延迟、不坚持激素治疗)和BC特定死亡率和总死亡率，以及#年按种族/民族分列的总体和 2006年间，在北加州凯撒永久医院被诊断为BC I-IV期的44,348名妇女- 2023年，包括3450名黑人，4441名西班牙裔，6571名亚洲女性和28589名非拉美裔白人女性。在一个 为了探索性目标，我们将与KP临床医生利益相关者一起制定实施EHR的步骤- 支持。我们将在确诊后两个月内审查100份医疗记录(每个种族/民族25份)， 由研究人员的专业知识和临床医生利益相关者提供信息，以开发用于描述患者的术语 支持。除了开发结构化数据之外，我们还将使用文本字段的自然语言处理来 进一步制定社会支持指标(目标1)。EHR--支持将从社会支持中计算 指标；我们将使用线性和Logistic回归来验证所开发的衡量标准是否符合已建立的 路径中的社会支持措施，这是一个由4505名患有BC和使用率的女性组成的成熟队列 分析和验证性因素分析方法以及ROC曲线以进一步评估分数(目标2)。 我们将使用线性、逻辑和COX比例风险回归来评估目标3中的关联。 电子病历和队列数据的独特融合为开发和验证电子病历提供了第一个机会： 基于社会支持的测量在不同的BC女性中进行，并根据一组广泛的协变量进行调整。这 这项工作是识别低社会支持风险升高的患者和加强社会支持的核心- 癌症研究需要改善临床护理，减少BC差异。