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，健康的社会决定因素（SDoH），是乳腺癌（BC）治疗的决定性预测因子 和死亡率结果。由于认识到社会支持对BC患者的结局至关重要， 北方加州（KPNC）的临床医生记录了有关社会风险的信息， 自2005年Epic问世以来，电子健康记录（EHR）的支持。然而，没有基于EHR的社会 目前存在支持措施来帮助临床医生识别处于低社会支持高风险的患者。这样的 这项措施对于解决不列颠哥伦比亚治疗和结果中的种族/民族差异具有高度相关性。因此，我们认为， 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）， 可用于识别社会支持风险低的BC妇女，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，并由KPNC提供信息，确定EHR中的术语 不列颠哥伦比亚省护理的利益攸关方，反映了结构和/或功能性社会支持，并与 业务连续性处理和成果; 2）使用结构化、半结构化和非结构化的 识别患者总体社会支持低风险的数据（包括文本的自然语言处理） 和种族/民族，并根据已公布的社会支持措施验证措施;以及3）评估 EHR-SUPPORT及其组成变量与BC治疗（手术和化疗）的相关性 延迟、不依从激素治疗）和BC特异性死亡率和总死亡率，总体和按人种/种族列出， 2006年至2010年期间，在Kaiser Permanente北方加州，有44，348名女性被诊断为I-IV期BC 2023年，包括3，450名黑人，4，441名西班牙裔，6，571名亚洲女性和28，589名非拉丁白色女性。中 探索性的目标，我们将制定，与KP临床利益相关者，步骤，以实施EHR- 支持.我们将在诊断后两个月内审查100份病历（每个种族/民族25份）， 由研究者专业知识和临床医生利益相关者告知，以制定用于描述患者的术语 支持.除了开发结构化数据，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（目标1）。EHR-SUPPORT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开发的措施， Pathways的社会支持措施，Pathways是一个由4 505名患有BC和使用因素的妇女组成的成熟队列 分析和验证性因素分析方法以及ROC曲线，以进一步评估评分（目的2）。 我们将使用线性、逻辑和考克斯比例风险回归来评估目标3中的相关性。的 EHR和队列数据的独特融合为开发和验证EHR提供了第一个机会， 基于社会支持的措施，在不同的妇女与BC，调整了广泛的协变量。这 工作是识别低社会支持风险升高的患者和增强社会支持的核心- 癌症研究需要改善临床护理并减少BC差异。