Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes.

INTRODUCTION: Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. METHODS: We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. RESULTS: For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. CONCLUSION: We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning.

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare.

INTRODUCTION: Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation. DESIGN: Retrospective data analysis. METHODS: We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models. RESULTS: Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC. CONCLUSION: As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided. CLINICAL RELEVANCE: The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

A qualitative analysis of stigmatizing language in birth admission clinical notes.

The presence of stigmatizing language in the electronic health record (EHR) has been used to measure implicit biases that underlie health inequities. The purpose of this study was to identify the presence of stigmatizing language in the clinical notes of pregnant people during the birth admission. We conducted a qualitative analysis on N = 1117 birth admission EHR notes from two urban hospitals in 2017. We identified stigmatizing language categories, such as Disapproval (39.3%), Questioning patient credibility (37.7%), Difficult patient (21.3%), Stereotyping (1.6%), and Unilateral decisions (1.6%) in 61 notes (5.4%). We also defined a new stigmatizing language category indicating Power/privilege. This was present in 37 notes (3.3%) and signaled approval of social status, upholding a hierarchy of bias. The stigmatizing language was most frequently identified in birth admission triage notes (16%) and least frequently in social work initial assessments (13.7%). We found that clinicians from various disciplines recorded stigmatizing language in the medical records of birthing people. This language was used to question birthing people's credibility and convey disapproval of decision-making abilities for themselves or their newborns. We reported a Power/privilege language bias in the inconsistent documentation of traits considered favorable for patient outcomes (e.g., employment status). Future work on stigmatizing language may inform tailored interventions to improve perinatal outcomes for all birthing people and their families.

Comorbidities of community-dwelling older adults with urinary incontinence: A scoping review.

BACKGROUND: Identifying comorbidities is a critical first step to building clinical phenotypes to improve assessment, management, and outcomes. OBJECTIVES: 1) Identify relevant comorbidities of community-dwelling older adults with urinary incontinence, 2) provide insights about relationships between conditions. METHODS: PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched. Eligible studies had quantitative designs that analyzed urinary incontinence as the exposure or outcome variable. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklists. RESULTS: Ten studies were included. Most studies had methodological weaknesses in the measurement of conditions. Comorbidities affecting the neurologic, cardiovascular, psychologic, respiratory, endocrine, genitourinary, and musculoskeletal systems were found to be associated with urinary incontinence. CONCLUSION: Existing literature suggests that comorbidities and urinary incontinence are interrelated. Further research is needed to examine symptoms, shared mechanisms, and directionality of relationships to generate clinical phenotypes, evidence-based holistic care guidelines, and improve outcomes.

Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review.

OBJECTIVES: To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings. DESIGN: Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. SETTING AND PARTICIPANTS: Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities). METHODS: PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised. RESULTS: Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators. CONCLUSIONS AND IMPLICATIONS: The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.

Social Determinants Documented Across Individuals from Different Racial and Ethnic Groups in Home Healthcare.

Home healthcare (HHC) enables patients to receive health services within their homes. Social determinants of health (SDOH) influence a patient's health and may disproportionately affect patients from racially and ethnically minoritized groups. This study describes differences in SDOH documentation in clinical notes among individuals from different racial or ethnic groups from one HHC agency in the northeastern United States. Compared to White patients, HHC episodes for patients across racially and ethnically minoritized groups had higher frequencies of SDOH documented. Further, our results suggest that race or ethnicity is significantly associated with SDOH documentation.

Identifying stigmatizing language in clinical documentation: A scoping review of emerging literature.

BACKGROUND: Racism and implicit bias underlie disparities in health care access, treatment, and outcomes. An emerging area of study in examining health disparities is the use of stigmatizing language in the electronic health record (EHR). OBJECTIVES: We sought to summarize the existing literature related to stigmatizing language documented in the EHR. To this end, we conducted a scoping review to identify, describe, and evaluate the current body of literature related to stigmatizing language and clinician notes. METHODS: We searched PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and Embase databases in May 2022, and also conducted a hand search of IEEE to identify studies investigating stigmatizing language in clinical documentation. We included all studies published through April 2022. The results for each search were uploaded into EndNote X9 software, de-duplicated using the Bramer method, and then exported to Covidence software for title and abstract screening. RESULTS: Studies (N = 9) used cross-sectional (n = 3), qualitative (n = 3), mixed methods (n = 2), and retrospective cohort (n = 1) designs. Stigmatizing language was defined via content analysis of clinical documentation (n = 4), literature review (n = 2), interviews with clinicians (n = 3) and patients (n = 1), expert panel consultation, and task force guidelines (n = 1). Natural language processing was used in four studies to identify and extract stigmatizing words from clinical notes. All of the studies reviewed concluded that negative clinician attitudes and the use of stigmatizing language in documentation could negatively impact patient perception of care or health outcomes. DISCUSSION: The current literature indicates that NLP is an emerging approach to identifying stigmatizing language documented in the EHR. NLP-based solutions can be developed and integrated into routine documentation systems to screen for stigmatizing language and alert clinicians or their supervisors. Potential interventions resulting from this research could generate awareness about how implicit biases affect communication patterns and work to achieve equitable health care for diverse populations.

Machine learning applied to electronic health record data in home healthcare: A scoping review.

OBJECTIVE: Despite recent calls for home healthcare (HHC) to integrate informatics, the application of machine learning in HHC is relatively unknown. Thus, this study aimed to synthesize and appraise the literature describing the application of machine learning to predict adverse outcomes (e.g., hospitalization, mortality) using electronic health record (EHR) data in the HHC setting. Our secondary aim was to evaluate the comprehensiveness of predictors used in the machine learning algorithms guided by the Biopsychosocial Model. METHODS: During March 2022 we conducted a literature search in four databases: PubMed, Embase, CINAHL, and Scopus. Inclusion criteria were 1) describing services provided in the HHC setting, 2) applying machine learning algorithms to predict adverse outcomes, defined as outcomes related to patient deterioration, 3) using EHR data and 4) focusing on the adult population. Predictors were mapped to the Biopsychosocial Model. A risk of bias analysis was conducted using the Prediction Model Risk Of Bias Assessment Tool. RESULTS: The final sample included 20 studies. Eighteen studies used predictors from standardized assessments integrated in the EHR. The most common outcome of interest was hospitalization (55%), followed by mortality (25%). Psychological predictors were frequently excluded (35%). Tree based algorithms were most frequently applied (75%). Most studies demonstrated high or unclear risk of bias (75%). CONCLUSION: Future studies in HHC should consider incorporating machine learning algorithms into clinical decision support systems to identify patients at risk. Based on the Biopsychosocial model, psychological and interpersonal characteristics should be used along with biological characteristics to enhance risk prediction. To facilitate the widespread adoption of machine learning, stakeholders should encourage standardization in the HHC setting.

Characterising symptom clusters in patients with atrial fibrillation undergoing catheter ablation.

OBJECTIVE: This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters. METHODS: We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward's hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher's exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status. RESULTS: A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients. CONCLUSIONS: We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients' symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes.

Do nurses document all discussions of patient problems and nursing interventions in the electronic health record? A pilot study in home healthcare.

Objective: To assess the overlap of information between electronic health record (EHR) and patient-nurse verbal communication in home healthcare (HHC). Methods: Patient-nurse verbal communications during home visits were recorded between February 16, 2021 and September 2, 2021 with patients being served in an organization located in the Northeast United States. Twenty-two audio recordings for 15 patients were transcribed. To compare overlap of information, manual annotations of problems and interventions were made on transcriptions as well as information from EHR including structured data and clinical notes corresponding to HHC visits. Results: About 30% (1534/5118) of utterances (ie, spoken language preceding/following silence or a change of speaker) were identified as including problems or interventions. A total of 216 problems and 492 interventions were identified through verbal communication among all the patients in the study. Approximately 50.5% of the problems and 20.8% of the interventions discussed during the verbal communication were not documented in the EHR. Preliminary results showed that statistical differences between racial groups were observed in a comparison of problems and interventions. Discussion: This study was the first to investigate the extent that problems and interventions were mentioned in patient-nurse verbal communication during HHC visits and whether this information was documented in EHR. Our analysis identified gaps in information overlap and possible racial disparities. Conclusion: Our results highlight the value of analyzing communications between HHC patients and nurses. Future studies should explore ways to capture information in verbal communication using automated speech recognition.