Optimizing Clinical Documentation Quality to Reduce Health Disparities in Home Healthcare: The ENGAGE Study

ABSTRACT Nurses are the largest sector of health providers in the United States (US). Recent studies have found that the quality of documentation varies significantly across patient populations, potentially contributing to health disparities. Documentation patterns are especially important in settings where nurses are the primary providers of healthcare services, such as home healthcare (HHC), where nurses visit more than 5 million patients in their homes across the US every year. Documentation patterns vary by patient demographics; in hospital settings, clinical notes show up to 50% variation in specific documentation patterns between Black and White patients. In the HHC setting, we also found that the likelihood of specific documentation patterns in clinical notes varied significantly across racial and ethnic groups, with Black and Hispanic patients having up to 20% higher prevalence of certain patterns. Critically, our studies found that these documentation patterns in the clinical notes are associated with differences in clinical assessments and lower quality of patient care. One promising technology, natural language processing (NLP), has the potential to help analyze documentation patterns 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, health disparities, and qualitative analysis to develop an NLP-based system to enhance documentation quality in home healthcare (ENGAGE: Excellence in Nursing Documentation) via the following specific aims. Aim 1: Identify and categorize documentation patterns applicable to HHC. Aim 2: Determine the optimal NLP approach to automatically and accurately identify documentation patterns of interest in the clinical notes of geographically dispersed HHC agencies. Aim 3: Examine variations in documentation patterns by patients' demographic and clinical characteristics. Aim 4: Develop an NLP-driven system to optimize documentation quality in HHC clinical notes. Accomplishing these aims will result in ENGAGE- a technology-driven intervention that will help to optimize HHC documentation quality and reduce health disparities.