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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要最近的研究发现，护士是美国最大的医疗服务提供者部门。护士中普遍存在偏见，最常见的偏见是种族和民族方面的。偏见会影响治疗决策，从而影响患者的治疗结果。护士是医疗保健服务主要提供者的环境，例如家庭医疗保健 (HHC) 护士每年到美国各地拜访超过 500 万名患者，种族偏见反映在其中。医疗文件；在医院环境中，有关黑人患者的临床记录的几率高出 50% 与白人患者相比，患者的语言包含污名化语言（即对患者进行负面描述的语言）在 HHC 环境中，我们还发现黑人和西班牙裔患者的临床记录高达 20%。至关重要的是，我们的研究发现，与白人和亚洲患者相比，使用侮辱性语言的可能性更高。临床记录中的侮辱性语言与参议员的消极态度和较低的治疗质量有关病人护理。一项有前途的技术——自然语言处理 (NLP)——有潜力帮助揭示与两家最大的护理服务提供商合作，数以百万计的 HHC 护理笔记中出现了侮辱性语言。美国的 HHC 服务（Louisiana Health Care Group 和 VNS Health，为超过 100,000 名患者提供服务联合每日人口普查），这项研究组建了一个由 HHC 护理、NLP、和临床决策支持，迈出设计以护士为中心的基于 NLP 的系统的第一步，以减少 stigmatiziNG 语言（“ENGAGE”）通过以下具体目标 1：扩展和完善本体论。适用于 HHC 的污名化语言目标 2：确定最佳的 NLP 方法来自动和准确识别地理位置分散的 HHC 机构的临床记录中的侮辱性语言。 3：比较不同种族和民族的污名化语言的流行程度目标 4：开发 NLP 驱动的语言。 ENGAGE 系统可减少 HHC 临床记录中的侮辱性语言。实现这些目标将导致 ENGAGE——一种技术驱动的行为改变干预措施这将有助于识别和消除 HHC 护士中的种族偏见。