A qualitative analysis of stigmatizing language in birth admission clinical notes

Examining Linguistic Differences in Electronic Health Records for Diverse Patients With Diabetes: Natural Language Processing Analysis

Background

Individuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias.

Objective

We used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias.

Methods

In this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-Hispanic, Black non-Hispanic, or Hispanic or Latino. We hypothesized that Sentiment Analysis and Social Cognition Engine (SEANCE) components (ie, negative adjectives, positive adjectives, joy words, fear and disgust words, politics words, respect words, trust verbs, and well-being words) and mean word count would be indicators of bias if racial differences emerged. We performed linear mixed effects analyses to examine the relationship between the outcomes of interest (the SEANCE components and word count) and patient race and ethnicity, controlling for patient age. To validate this approach, we asked clinicians to indicate the extent to which they thought variation in the use of SEANCE language domains for different racial and ethnic groups was reflective of bias in EHR notes.

Results

We examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient -0.02, SE 0.007), trust verbs (coefficient -0.009, SE 0.004), and joy words (coefficient -0.03, SE 0.01) than those for White non-Hispanic patients.

Conclusions

This approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.

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.

Striving for health equity: Stigmatizing language in inpatient pharmacy notes - A pilot study

BACKGROUND

As pharmacy evolves, pharmacists have an increasing role in documentation. Publications examining the actions of other health professionals show that negative perception in written notes translates to patients receiving lower quality of care, resulting in worse health outcomes, suggesting that the use of stigmatizing language towards patients has concerning consequences.

OBJECTIVES

To identify the prevalence of stigmatizing language in inpatient pharmacy progress. notes based on patient specific characteristics and diagnoses.

METHODS

This retrospective pilot study reviewed inpatient pharmacy progress notes of a Midwestern (United States) tertiary academic institution from May to June 2023. Stigmatizing words and phrases associated with specified disease states were separated into the categories of general language, substance use disorders, and mental health. Notes of patients on internal medicine, family medicine, kidney/pancreas transplant, liver transplant, and gastroenterology services were included.

RESULTS

Stigmatizing language was found in 22% (n = 43) of notes. The words "abuse" and "dependence" had the highest prevalence. Patients diagnosed with substance use disorders experienced stigmatizing language at a high rate, exaggerated further if their note lacked a documented diagnosis.

CONCLUSIONS

This study demonstrated that stigmatizing language is present in pharmacy documentation. Providing context and resources of the proper documentation to reflect equitable healthcare is crucial for patient care.

Board talk: How members of executive hospital boards influence the positioning of nursing in crisis through talk

Talk by members of executive hospital boards influences the organizational positioning of nurses. Talk is a relational leadership practice. Using a qualitative-interpretive design we organized focus group meetings wherein members of executive hospital boards (7), nurses (14), physicians (7), and managers (6), from 15 Dutch hospitals, discussed the organizational positioning of nursing during COVID crisis. We found that members of executive hospital boards consider the positioning of nursing in crisis a task of nurses themselves and not as a collective, interdependent, and/or specific board responsibility. Furthermore, members of executive hospital boards talk about the nursing profession as (1) more practical than strategic, (2) ambiguous in positioning, and (3) distinctive from the medical profession. Such talk seemingly contrasts with the notion of interdependence that highlights how actors depend on each other in interaction. Interdependence is central to collaboration in hospital crises. In this paper, therefore, we depart from the members of executive hospital boards as leader and "positioner," and focus on talk-as a discursive leadership practice-to illuminate leadership and governance in hospitals in crisis, as social, interdependent processes.

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.

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.

The Power of Language in Hospital Care for Pregnant and Birthing People: A Vision for Change

Language is commonly defined as the principal method of human communication made up of words and conveyed by writing, speech, or nonverbal expression. In the context of clinical care, language has power and meaning and reflects priorities, beliefs, values, and culture. Stigmatizing language can communicate unintended meanings that perpetuate socially constructed power dynamics and result in bias. This bias may harm pregnant and birthing people by centering positions of power and privilege and by reflecting cultural priorities in the United States, including judgments of demographic and reproductive health characteristics. This commentary builds on relationship-centered care and reproductive justice frameworks to analyze the role and use of language in pregnancy and birth care in the United States, particularly regarding people with marginalized identities. We describe the use of language in written documentation, verbal communication, and behaviors associated with caring for pregnant people. We also present recommendations for change, including alternative language at the individual, clinician, hospital, health systems, and policy levels. We define birth as the emergence of a new individual from the body of its parent, no matter what intervention or pathology may be involved. Thus, we propose a cultural shift in hospital-based care for birthing people that centers the birthing person and reconceptualizes all births as physiologic events, approached with a spirit of care, partnership, and support.