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

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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.