Identifying and reducing stigmatizing language in home healthcare: the ENGAGE study

识别和减少家庭医疗保健中的侮辱性语言：ENGAGE 研究

• 批准号: 10769571
• 负责人: Scott M Sittig
• 金额: $ 71.53万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-24 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10769571
• 关键词: Affect; Algorithms; Area; Asian; Attitude; Authorization documentation; Black race; Care given by nurses; Caring; Categories; Censuses; Characteristics; Clinical; Clinical Trials; Collaborations; Computational algorithm; Data; Documentation; Electronic Health Record; Ethnic Origin; Exhibits; Exposure to; Geographic Locations; Geography; Goals; Health; Health Care Visit; Health Personnel; Healthcare; Hispanic; Home; Home Care Services; Home Health Care Agencies; Hospitalization; Hospitals; Human; Intervention; Interview; Language; Length; Linguistics; Louisiana; Medical; Methods; Midwestern United States; Natural Language Processing; New York; Nurses; Nursing Services; Ontology; Outcome; Outpatients; Pain; Patient Care; Patient-Focused Outcomes; Patients; Physicians; Preparation; Prevalence; Provider; Quality of Care; Race; Review Literature; Sampling; Self Care; Series; Social Class; Statistical Data Interpretation; Stereotyping; Stigmatization; System; Technology; Training; United States; Visit; Visiting Nurse; Work; authority; behavior change; black patient; clinical care; clinical decision support; compliance behavior; design; ethnic minority; ethnic minority population; experimental study; health care service; health care settings; health disparity; high risk; implicit bias; improved; minority patient; negative affect; nicotine patch; patient population; phrases; pilot test; racial bias; racial minority; sociodemographics

Project Summary/Abstract Nurses are the largest sector of health providers in the United States (US). Recent studies have found widespread biases among nurses, with the most common bias being in the area of race and ethnicity. Nurses' biases affect treatment decisions, thereby affecting patient outcomes. Nurses' biases are especially critical in settings where nurses are the main provider of healthcare services, such as home healthcare (HHC) where nurses visit more than 5 million patients in their homes across the US every year. Racial biases are reflected in medical documentation; in hospital settings, clinical notes about Black patients have up to 50% higher odds of containing stigmatizing language (i.e., language that negatively characterizes patients) than White patients' notes. In the HHC setting, we also found that clinical notes of Black and Hispanic patients had up to 20% higher odds of including stigmatizing language than White and Asian patients. Critically, our studies found that stigmatizing language in the clinical notes is associated with negative clinicians' attitudes and lower quality of patient care. One promising technology—natural language processing (NLP)—has the potential to help uncover stigmatizing language in millions of HHC nursing notes. In collaboration with two of the largest providers of HHC services in the US (Louisiana Health Care Group and VNS Health, with more than 100,000 patients on the combined daily census), this study assembles an interdisciplinary team of experts in HHC nursing, NLP, and clinical decision support to build the first step in designing a nurse-centered NLP-based system to rEduce stigmatiziNG languAGE ("ENGAGE") via the following specific aims. Aim 1: Expand and refine the ontology of stigmatizing language applicable to HHC. Aim 2: Determine the optimal NLP approach to automatically and accurately identify stigmatizing language in the clinical notes of geographically dispersed HHC agencies. Aim 3: Compare the prevalence of stigmatizing language by race and ethnicity. Aim 4: Develop an NLP-driven ENGAGE system to reduce stigmatizing language in HHC clinical notes. Accomplishing these aims will result in ENGAGE- a technology-driven behavior change intervention that will help to identify and eliminate racial biases among HHC nurses.

项目摘要/摘要 护士是美国最大的医疗服务提供者。最近的研究发现 护士中普遍存在偏见，最常见的偏见是在种族和民族领域。护士的 偏见会影响治疗决策，从而影响患者的结局。护士的偏见在以下方面尤为严重 护士是医疗保健服务的主要提供者的环境，例如家庭医疗保健(HHC) 美国各地的护士每年都会在病人家中探望500多万名病人。种族偏见反映在 医疗记录；在医院环境中，关于黑人患者的临床记录有高出50%的几率 包含污蔑语言(即，对患者进行负面描述的语言)比白人患者的 笔记。在HHC设置中，我们还发现黑人和西班牙裔患者的临床记录高达20% 与白人和亚洲患者相比，使用污名化语言的几率更高。关键的是，我们的研究发现 临床笔记中的污名化语言与临床医生的负面态度和较低的质量有关 病人护理。 一种很有前途的技术--自然语言处理(NLP)--有可能帮助发现 羞辱数百万HHC护理笔记中的语言。与两家最大的 美国的HHC服务(路易斯安那医疗集团和VNS Health)，有超过100,000名患者在 合并每日人口普查)，这项研究聚集了HHC护理，NLP， 和临床决策支持构建以护士为中心的NLP系统的第一步，以减少 通过以下具体目的污蔑语言(“参与”)。目标1：扩展和提炼 适用于HHC的污名化语言。目标2：确定最优的NLP方法，以自动和 准确识别地理分散的HHC机构的临床笔记中的污名化语言。目标 3：比较不同种族和民族使用污名化语言的情况。目标4：开发NLP驱动的 引入系统，以减少HHC临床记录中的污名化语言。 实现这些目标将导致敬业--一种技术驱动的行为改变干预 这将有助于识别和消除HHC护士中的种族偏见。