Do Words Matter? Stigmatizing Language and the Transmission of Bias in the Medical Record

Equipping Dermatologists to Address Structural and Social Drivers of Inequities-Structural Competency

This Viewpoint defines structural competency and applies its 5 core elements to a hypothetical case of atopic dermatitis in a pediatric patient.

Race and Ethnicity and Clinician Linguistic Expressions of Doubt in Hospital Admission Notes

Importance

Stigmatizing language in electronic health records (EHRs) can generate or reinforce negative stereotypes about patients from minoritized groups and erode their trust and experience. However, less is known about the use of such language to cast doubt on patient clinical history in hospital settings.

Objective

To compare the prevalence of language expressing doubt about patient clinical history (eg, experiences and symptoms) in hospital admission notes by patient race and ethnicity.

Design, Setting, and Participants

In this cohort study, natural language processing tools were applied to analyze 54 936 admission notes from 1249 clinicians about patients aged 18 years or older at admission and hospitalized at an academic health system in the Northeast US between January 1, 2018, and February 28, 2023. Data were analyzed from September 1, 2022, to July 31, 2023.

Exposure

Patient race and ethnicity (non-Hispanic Black, non-Hispanic White, and racial and ethnic minoritized groups excluding non-Hispanic Black [includes multiple racial and ethnic groups, such as Hispanic and Asian]) as recorded in the EHR based on self-report or registrar determination.

Main Outcome and Measure

Binary indicator for at least 1 term casting doubt on patient clinical history was defined using epistemic stance, a linguistic construct expressing a writer's degree of certainty in information. Terms were manually validated via iterative review of notes by the study team.

Results

Among 56 325 admission notes (mean [SD] age of patients, 55.9 [19.0] years; 30 913 notes among female patients [54.9%]; 25 649 notes among non-Hispanic Black patients [45.5%], 26 442 notes among non-Hispanic White patients [46.9%], and 2985 notes among members of racial and ethnic minoritized groups excluding non-Hispanic Black patients [5.3%]), we analyzed 54 936 admission notes that had no missing data. Among all analyzed admission notes, 39 023 notes (71.0%) contained doubt language. Notes written about non-Hispanic Black patients had increased odds of containing at least 1 word or phrase of doubt (adjusted odds ratio, 1.21; 95% CI, 1.14-1.28; P < .001) compared with notes among non-Hispanic White patients. Compared with notes among non-Hispanic White patients, notes written about members of racial and ethnic minoritized groups excluding non-Hispanic Black patients had similar odds of containing at least 1 term of doubt.

Conclusion and Relevance

In this study, language casting doubt on patient clinical history was more likely to be found in notes of non-Hispanic Black patients. These results suggest that with the implementation of policies allowing patients full access to their health records, examining clinical documentation standards may be associated with improved patient experience.

The last word: An analysis of power dynamics in clinical notes documenting against-medical-advice discharges

Against Medical Advice (AMA) discharges pose significant challenges to the healthcare system, straining patient-clinician relationships while contributing to avoidable morbidity and mortality. Furthermore, though these discharges culminate in patients' departure from hospitals, their effects reverberate long after, propagated by clinician notes stored in patients' medical records. These notes capture exceptionally fraught interactions between patients and providers, describing the circumstances surrounding breakdowns in clinical relationships. Additionally, they represent just one side of complex, contentious social interactions, for in describing AMA discharges, clinician notewriters quite literally have the last word. For these reasons, notes documenting AMA discharges provide insight into the ways in which clinicians conceptualize, characterize, and propagate power differentials in the contemporary healthcare system. Here, we present a qualitative thematic analysis of 185 notes documenting AMA discharges from a large urban US medical center, interpreting note dynamics through three sociological models of power analysis: (i) the distributive model of power promulgated by Max Weber, (ii) the collectivist power model characterized by Talcott Parsons and Hannah Arendt, and (iii) structural interpretations of power developed by Michel Foucault. We argue that in documenting AMA discharges, clinicians appear to conceive of their relationship with patients in almost exclusively distributive terms, which in turn contributes to an adversarial dynamic whereby both patients and clinicians ultimately suffer disempowerment. We furthermore argue that by facilitating clinicians' recognition of power's collectivist and structural dimensions, we may help transform breakdowns in patient-clinician relationships into opportunities for collaboration.

The Relationship Between Speech-Language Pathologists' Perceptions of Clinical Terminology and Its Use in Voice Therapy with Adults

OBJECTIVES

The terminology used by speech-language pathologists (SLPs) during voice therapy is important for treatment outcomes because it can impact both patient self-efficacy and adherence. However, little is known about what factors influence the terminology that SLPs choose to use. Understanding this gap is important to ultimately optimize voice therapy outcomes. Therefore, the primary aims of this study were to (1) assess the relationship between reported clinician-perceived positivity and (2) assess the relationship between clinician-perceived positivity and clinical endorsement for use. We hypothesized that clinician-perceived positivity would be one important factor driving how frequently clinicians use or avoid specific terms, and if they think the term should be used by other SLPs in clinical practice.

DESIGN/METHODS

This prospective study was conducted as an online survey of SLPs and SLP clinical fellows who evaluate and treat adult voice patients. The survey presented respondents with a total of 46 voice-related terms and prompted respondents to rate: (1) how frequently they use a specific voice-related term ("frequency of use"); (2) how positive or negative they perceive a specific voice-related term to be ("perceived positivity"); and (3) if they feel a specific voice-related term should versus should not be used in clinical practice ("clinical endorsement"). Inferential statistics were used to examine the relationship between perceived positivity and frequency of use, and perceived positivity and clinical endorsement. Summary statistics were used to describe frequency of use across all terms.

RESULTS

One hundred twelve respondents completed the survey. Clinician-perceived positivity of voice-related terminology was significantly related to its reported self-reported frequency of use (β = 1.946; 95% CI: 1.701-2.191; P < 0.0005) and clinical endorsement of use by others (β = 4.103; 95% CI: 3.750-4.456; P < 0.0005). Both of these relationships exhibited relatively large effect sizes.

CONCLUSIONS

This study was an important first step at identifying factors that influence SLP's use of specific terminology in voice therapy. Specifically, an SLP's perceived positivity of clinical terminology strongly influenced the frequency with which they reported using that term in voice therapy and whether or not they thought that term should be used with patients by other SLPs in voice therapy. Future work should investigate clinician characteristics that might affect terminology use, include more diverse sampling, utilize self-selected terminology or audio recordings of therapy interactions, and assess direct effects of terminology use on patient outcomes.

A health equity ECHO for clinicians of individuals with SCD

SCD is the most common genetic disorder in the United States, affecting nearly 100 000 Americans, with the majority of affected individuals identifying as Black. Studies have shown that both children and adults with SCD face stigmatization resulting in low-quality care stemming from institutional racism and implicit bias by clinicians. Clinician biases often result from a lack of awareness of their own racial identity, failure to develop an anti-racist or social justice lens, and absence of skills to mitigate racist practices and policies. The Sickle Treatment and Outcomes Research in the Midwest (STORM) multi-state regional learning network developed a health equity training curriculum that was delivered as a continuing education learning series using the Project ECHO® virtual telementoring framework supplemented with an online learning management system. The curriculum addressed foundations of race, racism, and whiteness along with social justice strategies for the individual and institutional level and was piloted with 2 clinician cohorts to assess its feasibility, acceptability, and the potential impact on clinician self-efficacy. Feasibility and acceptability data, including qualitative feedback, suggest that this is a promising method for multidisciplinary clinicians and trainees for raising self-awareness about racism and bias, providing a safe community for self-reflection, and building skills to address inequities within healthcare settings.

Identifying Strategies for the Use of Gender and Sex Language in Clinical One-Liners

Purpose: The "one-liner," commonly used in clinical communications, summarizes a patient's identity, presenting condition, medical history, and clinical findings. Imprecise, inconsistent use of gender and sex information in one-liners threatens the provision of affirming care to transgender, nonbinary, gender-expansive, and intersex patients and may exacerbate health care disparities. This study aimed to generate guidance for communicating gender and sex information in one-liners. Methods: This is an explanatory sequential, equal status mixed methods study of transgender, nonbinary, gender-expansive, and intersex people and clinicians caring for this population. Survey participants rated one-liners on a five-point Likert-type scale of appropriateness, considering affirmation and clinical utility, and provided open-ended comments. We conducted two focus groups with survey respondents to explore survey results and performed a thematic analysis of survey comments and focus group transcripts. Results: Survey respondents included 57 clinicians and 80 nonclinicians. One-liners containing patient pronouns were rated most appropriate, and appropriate patient descriptors included self-described gender identity or gender-neutral terms. In scenarios where patient sex information was not pertinent to the chief concern (CC), one-liners containing no sex information were rated most appropriate. Four themes were identified: inclusion of sex information based on relevance to the CC, accurate patient representation, influence of clinical setting, and risk of harm from inaccurate one-liners. Conclusion: This study generated data to support the appropriate use of gender and sex language in one-liners. Clinicians, educators, and trainees may use these findings to compose one-liners that are affirming and clinically useful for patients of diverse gender and sex identities.

"Let Us Take Care of the Medicine": A Qualitative Analysis of Physician Communication When Caring for Febrile Infants

BACKGROUND

Guidelines for the management of febrile infants emphasize patient-centered communication. Although patient-centeredness is central to high-quality health care, biases may impact physicians' patient-centeredness. We aimed to 1) identify physicians' assumptions that inform their communication with parents of febrile infants and 2) examine physicians' perceptions of bias.

METHODS

We recruited physicians from 3 academic pediatric emergency departments (EDs) for semistructured interviews. We applied a constant comparative method approach to conduct a thematic analysis of interview transcripts. Two coders followed several analytical steps: 1) discovery of concepts and code assignment, 2) identification of themes by grouping concepts, 3) axial coding to identify thematic properties, and 4) identifying exemplar excerpts for rich description. Thematic saturation was based on repetition, recurrence, and forcefulness.

RESULTS

Fourteen physicians participated. Participants described making assumptions regarding 3 areas: 1) the parent's affect, 2) the parent's social capacity, and 3) the physician's own role in the parent-physician interaction. Thematic properties highlighted the importance of the physician's assumptions in guiding communication and decision-making. Participants acknowledged an awareness of bias and specifically noted that language bias influenced the assumptions that informed their communication.

CONCLUSIONS

ED physicians described subjective assumptions about parents that informed their approach to communication when caring for febrile infants. Given the emphasis on patient-centered communication in febrile infant guidelines, future efforts are necessary to understand how assumptions are influenced by biases, the effect of such behaviors on health inequities, and how to combat this.

Racial Differences in Stigmatizing and Positive Language in Emergency Medicine Notes

OBJECTIVE

Language used by providers in medical documentation may reveal evidence of race-related implicit bias. We aimed to use natural language processing (NLP) to examine if prevalence of stigmatizing language in emergency medicine (EM) encounter notes differs across patient race/ethnicity.

METHODS

In a retrospective cohort of EM encounters, NLP techniques identified stigmatizing and positive themes. Logistic regression models analyzed the association of race/ethnicity and themes within notes. Outcomes were the presence (or absence) of 7 different themes: 5 stigmatizing (difficult, non-compliant, skepticism, substance abuse/seeking, and financial difficulty) and 2 positive (compliment and compliant).

RESULTS

The sample included notes from 26,363 unique patients. NH Black patient notes were less likely to contain difficult (odds ratio (OR) 0.80, 95% confidence interval (CI), 0.73-0.88), skepticism (OR 0.87, 95% CI, 0.79-0.96), and substance abuse/seeking (OR 0.62, 95% CI, 0.56-0.70) compared to NH White patient notes but more likely to contain non-compliant (OR 1.26, 95% CI, 1.17-1.36) and financial difficulty (OR 1.14, 95% CI, 1.04-1.25). Hispanic patient notes were less likely to contain difficult (OR 0.68, 95% CI, 0.58-0.80) and substance abuse/seeking (OR 0.78, 95% CI, 0.66-0.93). NH NA/AI patient notes had twice the odds as NH White patient notes to contain a stigmatizing theme (OR 2.02, 95% CI, 1.64-2.49).

CONCLUSIONS

Using an NLP model to analyze themes in EM notes across racial groups, we identified several inequities in the usage of positive and stigmatizing language. Interventions to minimize race-related implicit bias should be undertaken.

Health professionals implicit bias of patients with low socioeconomic status (SES) and its effects on clinical decision-making: a scoping review

OBJECTIVES

Research indicates that people with lower socioeconomic status (SES) receive inferior healthcare and experience poorer health outcomes compared with those with higher SES, in part due to health professional (HP) bias. We conducted a scoping review of the impact of HP bias about SES on clinical decision-making and its effect on the care of adults with lower SES.

DESIGN

JBI scoping review methods were used to perform a systematic comprehensive search for literature. The scoping review protocol has been published in BMJ Open.

DATA SOURCES

Medline, Embase, ASSIA, Scopus and CINAHL were searched, from the first available start date of the individual database to March 2023. Two independent reviewers filtered and screened papers.

ELIGIBILITY CRITERIA

Studies of all designs were included in this review to provide a comprehensive map of the existing evidence of the impact of HP bias of SES on clinical decision-making and its effect on the care for people with lower SES.

DATA EXTRACTION AND SYNTHESIS

Data were gathered using an adapted JBI data extraction tool for systematic scoping reviews.

RESULTS

Sixty-seven papers were included from 1975 to 2023. 35 (73%) of the included primary research studies reported an association between HP SES bias and decision-making. Thirteen (27%) of the included primary research studies did not find an association between HP SES bias and decision-making. Stereotyping and bias can adversely affect decision-making when the HP is fatigued or has a high cognitive load. There is evidence of intersectionality which can have a powerful cumulative effect on HP assessment and subsequent decision-making. HP implicit bias may be mitigated through the assertiveness of the patient with low SES.

CONCLUSION

HP decision-making is at times influenced by non-medical factors for people of low SES, and assumptions are made based on implicit bias and stereotyping, which compound or exacerbate health inequalities. Research that focuses on decision-making when the HP has a high cognitive load, would help the health community to better understand this potential influence.

First-Year Medical Students' Perceptions of Stigma Toward People With Opioid Use Disorder Before and After an Educational Intervention

BACKGROUND

Stigma among medical trainees toward people with opioid use disorder (OUD) compounds the problems associated with opioid addiction. People with OUD who experience overt and implicit stigma from healthcare providers are less likely to seek and receive treatment, further restricting their access to already limited resources. The objective of our study was to assess an educational strategy to mitigate stigma toward people with OUD among first-year medical students.

METHODS

This study assessed perceptions of stigma toward people with OUD among first-year medical students using an adaptation of a brief, validated opioid stigma scale before and after an educational intervention. The intervention consisted primarily of a recorded panel in which people with a history of OUD shared their experiences with stigma followed by small group discussions.

RESULTS

After the educational intervention, students were more likely to respond that (1) they believed most people held negative beliefs about people with OUD and (2) they personally disagreed with negative statements about people with OUD.

CONCLUSIONS

Educational interventions addressing stigma toward people with OUD are potentially effective and should be integrated into medical curricula. Such interventions are a crucial part of the effort to improve the medical care of people with OUD.

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.

Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda

The health inequalities experienced by ethnic minorities have been a persistent and global phenomenon. The diagnosis of different types of skin conditions, e.g., melanoma, among people of color is one of such health domains where misdiagnosis can take place, potentially leading to life-threatening consequences. Although Caucasians are more likely to be diagnosed with melanoma, African Americans are four times more likely to present stage IV melanoma due to delayed diagnosis. It is essential to recognize that additional factors such as socioeconomic status and limited access to healthcare services can be contributing factors. African Americans are also 1.5 times more likely to die from melanoma than Caucasians, with 5-year survival rates for African Americans significantly lower than for Caucasians (72.2% vs. 89.6%). This is a complex problem compounded by several factors: ill-prepared medical practitioners, lack of awareness of melanoma and other skin conditions among people of colour, lack of information and medical resources for practitioners' continuous development, under-representation of people of colour in research, POC being a notoriously hard to reach group, and 'whitewashed' medical school curricula. Whilst digital technology can bring new hope for the reduction of health inequality, the deployment of artificial intelligence in healthcare carries risks that may amplify the health disparities experienced by people of color, whilst digital technology may provide a false sense of participation. For instance, Derm Assist, a skin diagnosis phone application which is under development, has already been criticized for relying on data from a limited number of people of color. This paper focuses on understanding the problem of misdiagnosing skin conditions in people of color and exploring the progress and innovations that have been experimented with, to pave the way to the possible application of big data analytics, artificial intelligence, and user-centred technology to reduce health inequalities among people of color.

An Evidence-Based Approach to Constructing Multiple-Choice Test Questions for Knowledge Assessment

BACKGROUND

This article provides nursing educators practical tips and evidence-based strategies for effective construction of multiple-choice questions (MCQs). Well-designed MCQs that align with the intended learning objectives are critical for implementing sound assessment practices.

METHOD

This article offers a step-by-step approach to test construction, starting with the assessment blueprint and followed by important considerations when writing the specific components of the MCQ.

RESULTS

Appropriate inclusion of clinical context in the MCQ and a description of common flaws to avoid, with suggested remedies, are also addressed.

CONCLUSION

Ultimately, the goal of this article is to equip nurse educators with the foundational tools to create high-quality MCQs that effectively assess knowledge acquisition by learners. [J Contin Educ Nurs. 2024;55(6):289-296.].

Examining Inclusive Language in Clinical Narratives in Medical Biochemistry Textbooks to Model Equitable Patient-Centered Care in Preclinical Undergraduate Medical Education

Purpose

When healthcare professionals use biased or stigmatizing language to describe people or conditions, it can impact the quality of care or erode the patient-physician relationship. It is not clear where healthcare professionals acquire biased and stigmatizing language in practice. This study focuses on examining language in educational materials used in training of medical students. Specifically, medical biochemistry textbooks were examined as they are often a first exposure to clinical narratives and communication standards. The aim of this project is to investigate whether medical biochemistry textbooks, widely recommended in preclinical UME, model inclusive language communication in clinical narratives.

Methods

To determine if educational materials follow inclusive writing guidelines, we conducted a modified document analysis on a sample of medical biochemistry textbooks when clinical scenarios were described. Three independent researchers separately reviewed the textbooks, coded the language using NVivo, and generated themes.

Results

Our results show that medical biochemistry textbooks contain language which is not in alignment with the best practices for inclusive language. Our analysis mapped codes to two primary themes of language misalignment. The first theme, "clinical language" (n = 92), included the following codes: difficult patient, general negative descriptive language, patient as failure, and questioning patient credibility. The second primary theme, "identity-first labeling" (n = 251), included 21 codes.

Conclusion

This study provides early evidence that the language used in medical biochemistry textbooks to describe people and conditions is not in alignment with inclusive language recommendations. This can reinforce the way future healthcare professionals speak to and about their patients.

Discordance Between HIV Risk Perception, Sexual Behavior, and Pre-exposure Prophylaxis Adherence Among Young Sexual and Gender Minorities in the United States

PURPOSE

In the United States, youth experience suboptimal HIV pre-exposure prophylaxis (PrEP) adherence. One common idea posits that this is due to their developing decision-making skills. However, quantitative evidence of this assumption is limited. We therefore examined whether individual decision-making factors, such as HIV risk perception and sexual behavior, predicted PrEP adherence in a national trial of young sexual and gender minorities (YSGMs).

METHODS

In 2019-2021, the Adolescent Medicine Trials Network for HIV Interventions 142 study enrolled 225 PrEP users (ages 16-24) throughout the country. Regression models estimated the associations between HIV risk perception (using a modified Perceived HIV Risk Scale), sexual behavior (condomless anal sex in ≤ 3 months), and self-reported oral PrEP adherence (≥4 pills in the past week) at the same time point (baseline) and longitudinally (3 months).

RESULTS

Baseline risk perception (risk ratio [RR]: 0.92, 95% confidence interval [CI]: 0.82, 1.04) and condomless anal sex (RR: 1.10, 95% CI: 0.97, 1.25) were not associated with PrEP adherence at the same time point and did not predict 3-month adherence (RR: 0.97, 95% CI: 0.85, 1.11; RR: 1.05, 95% CI: 0.93, 1.19, respectively). Baseline risk perception was not associated with condomless anal sex at either time point (baseline RR: 1.16, 95% CI: 0.94, 1.43; 3-month RR: 1.07, 95% CI: 0.90, 1.28).

DISCUSSION

In this national trial of YSGM, HIV risk perception and condomless anal sex did not predict PrEP adherence. Targeting individual-level perceptions and behaviors will likely insufficiently address youth's suboptimal PrEP use. Future research should identify YSGM-specific adherence drivers and train providers to recognize such motivations.

Caring for Hospitalized Adults With Opioid Use Disorder in the Era of Fentanyl: A Review

Importance

The rise of fentanyl and other high-potency synthetic opioids across US and Canada has been associated with increasing hospitalizations and unprecedented overdose deaths. Hospitalization is a critical touchpoint to engage patients and offer life-saving opioid use disorder (OUD) care when admitted for OUD or other medical conditions.

Observations

Clinical best practices include managing acute withdrawal and pain, initiating medication for OUD, integrating harm reduction principles and practices, addressing in-hospital substance use, and supporting hospital-to-community care transitions. Fentanyl complicates hospital OUD care. Fentanyl's high potency intensifies pain, withdrawal, and cravings and increases the risk for overdose and other harms. Fentanyl's unique pharmacology has rendered traditional techniques for managing opioid withdrawal and initiating buprenorphine and methadone inadequate for some patients, necessitating novel strategies. Further, co-use of opioids with stimulants drugs is common, and the opioid supply is unpredictable and can be contaminated with benzodiazepines, xylazine, and other substances. To address these challenges, clinicians are increasingly relying on emerging practices, such as low-dose buprenorphine initiation with opioid continuation, rapid methadone titration, and the use of alternative opioid agonists. Hospitals must also reconsider conventional approaches to in-hospital substance use and expand clinicians' understanding and embrace of harm reduction, which is a philosophy and set of practical strategies that supports people who use drugs to be safer and healthier without judgment, coercion, or discrimination. Hospital-to-community care transitions should ensure uninterrupted access to OUD care after discharge, which requires special consideration and coordination. Finally, improving hospital-based addiction care requires dedicated infrastructure and expertise. Preparing hospitals across the US and Canada to deliver OUD best practices requires investments in clinical champions, staff education, leadership commitment, community partnerships, quality metrics, and financing.

Conclusions and Relevance

The findings of this review indicate that fentanyl creates increased urgency and new challenges for hospital OUD care. Hospital clinicians and systems have a central role in addressing the current drug crisis.

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models

BACKGROUND

Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases.

RESEARCH QUESTION

Can we identify implicit bias in clinical notes, and are biases stable across time and geography?

STUDY DESIGN AND METHODS

To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence).

RESULTS

In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors.

INTERPRETATION

Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

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.

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.

Discussing and Teaching About Race and Health Inequities

Health inequities are prevalent in our medical institutions and result in unfair access to and delivery of health care. Some of the most profound health disparities are related to race, which has erroneously been used to make biological inferences to explain disease states in medicine. Our profession continues to shift away from such race-based medical narratives, which do not examine how social determinants of health, social injustice, systemic racism, and existing power structures shape health outcomes toward a health equity mindset and race-conscious medicine. Clinician educators are responsible for teaching and engaging with learners around issues of inequity in medicine, although many may feel they lack the knowledge or skills to do so. Opportunities for conversations on health equity abound, either as a response to statements made by clinical peers or patients, or through direct clinical care of affected populations. In this paper, we focus our discussion of health equity around the topic of race corrections in spirometry, which is one of several salient areas of conversation in the field of pulmonary medicine undergoing reconciliation. We review basic definitions and concepts in health equity and apply three strategies to engage in conversations around equity with colleagues and learners: actively learning and reflecting on health inequities, recognizing and naming inequities, and consciously role-modeling equity-conscious language and care. We also will summarize strategies for implementing health equity concepts into the continuum of medical education and our clinical learning environments.

A Content Analysis of HIV-Related Stigmatizing Language in the Scientific Literature, From 2010-2020: Findings and Recommendations for Editorial Policy

Despite negative effects of HIV-related stigma on people with HIV, some scientific literature continues to use stigmatizing terms. Our study aimed to explore the use of HIV-related stigmatizing language in the scientific literature between 2010 and 2020 based on 2015 UNAIDS terminology guidelines. We searched for articles with the stigmatizing term "HIV/AIDS-infected" or any variations that were peer-reviewed, published between 2010 and 2020, and in English or with an English translation. Our search yielded 26,476 articles that used the stigmatizing term of interest. Frequencies on the variables of interest (journal, year, and country) were run. The use of these terms increased from 2010 to 2017 and decreased from 2018 to 2020. Most journals using the terms were HIV/AIDS specific or on infectious diseases, but the journal with the greatest frequency of use was on general science and medicine. Thirty-six percent of the articles emanated from the United States. To reduce the use of stigmatizing language in the HIV literature, action should be taken by authors, reviewers, editors,educators, and publishers should create formal policies promoting use of non-stigmatizing language.

Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

BACKGROUND

Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions.

OBJECTIVE

This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes.

METHODS

Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR.

RESULTS

In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days.

CONCLUSIONS

This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.

Opportunity to Integrate the American Medical Association's Inclusive Language Guidance

Inclusive language is a cornerstone for inclusive, just, and equitable health care. While the American Medical Association released inclusive language guidance in 2021, it was unclear the extent to which physician practice organizations and their affiliated journals have adopted and promoted inclusive language. In our analysis, we found a lack of inclusive language resources across many physician practice organizations and their affiliated journals. Moreover, when guidance was provided by such entities, it was sometimes limited or not reflective of the American Medical Association recommendations. As such, many practice organizations and their journals have the opportunity to promote inclusive language.

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.

Ketamine for Pain in Sickle Cell Disease Reduces Opioid Usage

CONTEXT

Pain attributable to sickle cell disease (SCD) is often unpredictable, recurrent, and requires complex treatments. Subanesthetic ketamine infusion has been studied in other diseases and disorders, but there is still limited data on its efficacy in pain management for SCD.

OBJECTIVES

The primary objective is to determine if subanesthetic ketamine infusion reduces pain scores and opioid requirements in hospitalized pediatric patients with SCD.

RESULTS

Forty-six admissions among 22 patients between February 2018 and December 2019 were analyzed. We observed decrease in pain scores within 24 hours of ketamine initiation in 34 of 46 admissions (mean pain score per patient before ketamine initiation: 2.2-9.7, mean pain score per patient after ketamine initiation: 0-9.7; P < .05). We observed a decrease in pain scores in the remaining 12 admissions after greater than 24 hours of ketamine initiation. Opioid usage declined after ketamine infusion, with a difference of means in oral morphine equivalents before and after ketamine of 122.8 mg/day. The side effects observed with ketamine infusion included hallucinations in 11 (23.9%) admissions. Only four (8.7%) admissions required cessation of the infusion due to side effects. The readmission rate at two weeks and four weeks after first ketamine infusion was the same (12.5%) at both time points. For all patients in the cohort, the introduction of ketamine into pain regimens did not reduce the number of admissions in the year following ketamine initiation relative to the year prior.

CONCLUSION

In pediatric patients with SCD, subanesthetic ketamine was safe as a continuous infusion and effectively reduced both pain scores and opioid requirements.

"Buprenorphine, It Works so Differently": Adults with Sickle Cell Disease Describe Transitioning to Buprenorphine for Treatment of Chronic Pain

A growing body of literature describes the use of buprenorphine for the treatment of chronic pain in people with sickle cell disease. The experiences of people with sickle cell disease who have tried buprenorphine have not yet reported. This qualitative descriptive study was conducted to explore perspectives on buprenorphine for chronic pain in sickle cell disease. We interviewed 13 participants with sickle cell disease who had been prescribed buprenorphine and had a clinic visit between December 1, 2020, and April 2022 in our Sickle Cell Center for Adults. Interviews were recorded, transcribed, and analyzed using thematic analysis. Eleven out of 13 participants were taking buprenorphine at the time of the interview, with a mean treatment duration of 33 months (SD 18, range 7-78 months). Five major themes were identified: 1) dissatisfaction with full agonist opioids; 2) navigating uncertainty with autonomy in deciding to try buprenorphine; 3) functional and relational changes after starting buprenorphine, 4) enduring systemic barriers to pain treatment, and 5) trusting treatment relationships are necessary when approaching patients about buprenorphine. The experience of adulthood living with sickle cell disease before and after starting buprenorphine is qualitatively different with significant improvements in social functioning. PERSPECTIVE: This study examined the experience of adults with sickle cell disease and chronic pain transitioning from full agonist opioids to buprenorphine. It is the first qualitative study of buprenorphine in people with sickle cell disease, contributing to a small but growing literature about buprenorphine and sickle cell disease.

Pain Catastrophizing: Controversies, Misconceptions and Future Directions

Recent reports have pointed to problems with the term "pain catastrophizing." Critiques of the term pain catastrophizing have come from several sources including individuals with chronic pain, advocates for individuals with chronic pain, and pain scholars. Reports indicate that the term has been used to dismiss the medical basis of pain complaints, to question the authenticity of pain complaints, and to blame individuals with pain for their pain condition. In this paper, we advance the position that the problems prompting calls to rename the construct of pain catastrophizing have little to do with the term, and as such, changing the term will do little to solve these problems. We argue that continued calls for changing or deleting the term pain catastrophizing will only divert attention away from some fundamental flaws in how individuals with pain conditions are assessed and treated. Some of these fundamental flaws have their roots in the inadequate training of health and allied health professionals in evidence-based models of pain, in the use of psychological assessment and intervention tools for the clinical management of pain, and in gender equity and antiracism. Critiques that pain scholars have leveled against the defining, operational, and conceptual bases of pain catastrophizing are also addressed. Arguments for reconceptualizing pain catastrophizing as a worry-related construct are discussed. Recommendations are made for remediation of the problems that have contributed to calls to rename the term pain catastrophizing. PERSPECTIVE: The issues prompting calls to rename the construct of pain catastrophizing have their roots in fundamental flaws in how individuals with pain are assessed and treated. Efforts to address these problems will require more than a simple change in terminology.

Pilot study using machine learning to improve estimation of physical abuse prevalence

BACKGROUND

International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes have been shown to underestimate physical abuse prevalence. Machine learning models are capable of efficiently processing a wide variety of data and may provide better estimates of abuse.

OBJECTIVE

To achieve proof of concept applying machine learning to identify codes associated with abuse.

PARTICIPANTS AND SETTING

Children <5 years, presenting to the emergency department with an injury or abuse-specific ICD-10-CM code and evaluated by the child protection team (CPT) from 2016 to 2020 at a large Midwestern children's hospital.

METHODS

The Pediatric Health Information System (PHIS) and the CPT administrative databases were used to identify the study sample and injury and abuse-specific ICD-10-CM codes. Subjects were divided into abused and non-abused groups based on the CPT's evaluation. A LASSO logistic regression model was constructed using ICD-10-CM codes and patient age to identify children likely to be diagnosed by the CPT as abused. Performance was evaluated using repeated cross-validation (CV) and Reciever Operator Characteristic curve.

RESULTS

We identified 2028 patients evaluated by the CPT with 512 diagnosed as abused. Using diagnosis codes and patient age, our model was able to accurately identify patients with confirmed PA (mean CV AUC = 0.87). Performance was still weaker for patients without existing ICD codes for abuse (mean CV AUC = 0.81).

CONCLUSIONS

We built a model that employs injury ICD-10-CM codes and age to improve accuracy of distinguishing abusive from non-abusive injuries. This pilot modelling endeavor is a steppingstone towards improving population-level estimates of abuse.

Opportunities for Nurses to Decrease the Stigma Associated with Housing Instability and Homelessness

The number of people experiencing homelessness (PEH) in the United States has increased in the past 5 years. PEH have a higher disease burden and early mortality compared to people who are housed. Stigma adds to the burden of disease and disease management for PEH. In this article the authors review stigma, define housing and homelessness, describe the health and health care disparities PEH experience, and using the socio-ecological model as a framework, offer opportunities for nurses to intervene in efforts to decrease the stigma that PEH and housing instability encounter to improve health outcomes.

Addressing bias in artificial intelligence for public health surveillance

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been implemented to analyse large volumes of text from social media platforms to gain insights on disease symptoms, understand barriers to care and predict disease outbreaks. However, AI-based decisions may contain biases that could misrepresent populations, skew results or lead to errors. Bias, within the scope of this paper, is described as the difference between the predictive values and true values within the modelling of an algorithm. Bias within algorithms may lead to inaccurate healthcare outcomes and exacerbate health disparities when results derived from these biased algorithms are applied to health interventions. Researchers who implement these algorithms must consider when and how bias may arise. This paper explores algorithmic biases as a result of data collection, labelling and modelling of NLP algorithms. Researchers have a role in ensuring that efforts towards combating bias are enforced, especially when drawing health conclusions derived from social media posts that are linguistically diverse. Through the implementation of open collaboration, auditing processes and the development of guidelines, researchers may be able to reduce bias and improve NLP algorithms that improve health surveillance.

Guiding Principles for Writing About Immigrants and Immigrant Health

This perspective identifies harmful phrasing and frames in current clinician and researcher work relating to immigrant health and provides equity-centered alternatives. Recommendations are organized within two broad categories, one focused on shifting terminology toward more humanizing language and the second focused on changing frames around immigration discourse. With regards to shifting terminology, this includes: 1) avoiding language that conflates immigrants with criminality (i.e., "illegal"); 2) using person-first language (i.e., "person applying for asylum" or "detained person" rather than "asylum-seeker" or "detainee"); 3) avoiding comparisons to "native" populations to mean non-foreign-born populations, as this contributes to the erasure of Native Americans and indigenous people; 4) avoiding hyperbolic and stigmatizing "crisis" language about immigrants; and 5) understanding inherent limitations of terms like "refugee," "asylum seeker," "undocumented" that are legal not clinical terms. With regards to challenging dominant frames, recommendations include: 6) avoiding problematization of certain borders compared to others (i.e., U.S.-Mexico versus U.S.- Canada border) that contributes to selectively subjecting people to heightened surveillance; 7) recognizing the heterogeneity among immigrants, such as varying reasons for migration along a continuum of agency, ranging from voluntary to involuntary; 8) avoiding setting up a refugee vs. migrant dichotomy, such that only the former is worthy of sympathy; and 9) representing mistrust among immigrants as justified, instead shifting focus to clinicians, researchers, and healthcare systems who must build or rebuild trustworthiness. Ensuring inclusive and humanizing language use and frames is one critical dimension of striving for immigrant health equity.

Overview of Depression

Depressive disorders encompass a spectrum of diagnoses and are more common in women and transgender individuals. Diagnosis involves thorough history-taking and exclusion of underlying medical disorders. The emergency physician should assess the risk of self-harm and consider environmental and social factors prior to disposition.

Metaphorical use of "headache" and "migraine" in media: A longitudinal study of 1.3 million articles in major publications

BACKGROUND

Stigmatization and trivialization of headache confront individuals with headache disorders, but the degree to which media may contribute is incompletely understood.

OBJECTIVE

The objective of this study was to quantify the frequency of disparaging metaphorical use of the words "headache" and "migraine" in articles and summaries of major publications.

METHODS

This longitudinal study analyzed a dataset of 1.3 million articles and summaries written by authors and editors of 38 major publications. Data cover written publications from 1998 up to 2017. The use of the words "headache" or "migraine" in articles and summaries by major publications was rated by two authors (P.Z. and A.V.) as either "metaphorical" or "medical" based on their contextual application. Pearson's chi-squared test was applied to assess differences in the frequency of metaphorical use of "headache" in comparison to "migraine." Secondary outcomes were the source of publication and time of publication.

RESULTS

A total of 6195 and 740 articles included the words "headache" or "migraine," respectively; 7100 sentences contained the word "headache" and 1652 sentences contained the word "migraine." Among a random sample of 1000 sentences with the word "headache," there was a metaphorical use in 492 (49.2% [95% CI, 46.1-52.3]) sentences. Among a random sample of 1000 sentences with the word "migraine," there was a metaphorical use in 45 (4.5% [95% CI, 3.2-5.8]) sentences. The five most prevalent sources were CNN, Fox News, The New York Times, The Guardian, and The Washington Post. There was an overall increase in the number of articles containing the words "headache" or "migraine" from database inception until analysis (1998 up to 2017). The database included no articles containing either "headache" or "migraine" in 1998; in 2016, this number was 1480 articles.

CONCLUSIONS

In this longitudinal study, major publications applied a metaphorical use of "headache" about half of the time. The metaphorical use of "headache" is 11-fold greater than the metaphorical use of "migraine" in the same media sample. These depictions may contribute to the trivialization of headache and the stigmatization of individuals with headache disorders. Studies with individuals affected by headache disorders are needed to clarify potential influences.

Best practice recommendations for the management of anxiety during the pegvaliase journey

BACKGROUND

Pegvaliase, an enzyme substitution therapy, is a treatment option for phenylketonuria (PKU). Due to the neuropathophysiology and disease burden of PKU, individuals can experience baseline anxiety unrelated to pegvaliase therapy. In addition, there are aspects of pegvaliase therapy that may be anxiety-inducing for those considering or receiving treatment. The aim of this manuscript is to present best practice recommendations for the identification and management of anxiety symptoms that can occur along the pegvaliase journey.

METHODS

A modified Delphi approach was used to seek consensus among a multidisciplinary panel of experts. To this end, an in-person meeting was held that was preceded by a medical specialist- and patient-specific survey to develop preliminary recommendations on ways to address anxiety along the pegvaliase journey. After the meeting, an additional survey was conducted to rank the proposed solutions and mitigation strategies from which a set of recommendations was developed. All recommendations were voted on with the aim of consensus generation, defined as achieving ≥75% agreement among experts.

RESULTS

The panel reached consensus on a total of 28 best practice recommendations for the management of anxiety during the pre-treatment, induction and titration, early maintenance (pre-efficacy), and late maintenance (post-efficacy) stages. The recommendations offer strategies to identify and address the most common causes of pegvaliase-related anxiety, including self-injection, side effects, the titration schedule, prescribed dietary changes, and variable time to efficacy. Overall, managing anxiety in those considering or receiving pegvaliase involves patient-centered communication, shared decision-making, and personalized treatment plans.

CONCLUSIONS

The best practice recommendations described herein can guide healthcare providers in proactively addressing anxiety during the different stages of pegvaliase treatment, and support providers with initiating and managing pegvaliase in individuals who may experience baseline and treatment-related anxiety.

Evaluation of Nursing Coursework for Biased Content

ABSTRACT

Bias-free content in nursing education prepares students to provide equitable care. The process of assessing content promotes the dismantling of systemic bias in health care, advancing social justice, diversity, equity, and inclusion. Unfortunately, there are no published studies to guide the evaluation of nursing curricula for the presence of bias. This innovative project aimed to identify bias in a course and develop a structure to guide the wider evaluation of the curriculum to identify and remove biased content. A modified version of the Upstate Bias Checklist was applied to a 15-week, three-semester hour, prelicensure, graduate-level nursing course.

Perceptions of unilluminated occupations a survey of Danish occupational therapists

BACKGROUND

In occupational therapy and -science positive aspects of occupation are highlighted. Recently, this discourse has been questioned, as it might leave out occupations - referred to as unilluminated occupations (UO) - that hold value to people, without fitting the positive ideal.

AIM

To translate UO into Danish and to examine how occupational therapists (OTs) view and address UO.

METHODS

A survey developed in USA was translated into Danish and distributed to OTs across Denmark. We added questions on the Danish wording of the concepts, including a content validity index (CVI). Data was subjected to a descriptive analysis.

RESULTS

The respondents agreed that OTs must include examining the purpose and meaning of UO, however without having a responsibility to support performing these UO. The applied translation: high risk (risikable), unhealthy (usunde), unethical (uetiske), immoral (umoralske), and unacceptable (uacceptable) were deemed relevant in Danish, established by CVIs on 0.72-0.90.

CONCLUSIONS AND SIGNIFICANCE

Although most respondents were positive towards examining and acknowledging clients' purpose and meaning of UO, no clear consensus when addressing UO in interventions was seen. Further research might shed light on ways to approach UO both in assessment and intervention. The Danish terms appeared relevant to encompass UO.

Treating black patients as "knowers"

Recent trends in healthcare policy from high-volume service models to "high-value" delivery systems have refocused the need for patient-centered approaches to quality care. However, benchmarks of how to define and evaluate successful patient-centeredness have not been sufficiently established. Such ill-defined evaluation criteria can further exacerbate systemic inequities in maximum quality health care delivery, especially based on the intersectional diversity of various patient populations. In this context, applying a phenomenology of medicine framework or perspective-driven analysis is useful in defining cross-cultural patient-centeredness. This reframing from a naturalistic or objective/biological viewpoint to a phenomenological viewpoint may aid in placing greater epistemic or knowledge authority in the hands of vulnerable and/or marginalized patients- allowing these patients to become key "knowers" in the clinical interaction. Moreover, treating Black patients as "knowers" emphasizes the prioritization of patient values at the core of providing valuable healthcare. Such an academic, policy, and clinical approach to medicine agrees with well-established principles of medical ethics. In addition, the framework of a phenomenology of medicine can better facilitate physician-patient communication and interaction by delineating often muddled hermeneutics.

Multidisciplinary Care of Alcohol-related Liver Disease and Alcohol Use Disorder: A Narrative Review for Hepatology and Addiction Clinicians

PURPOSE

Models of integrated, multidisciplinary care are optimal in the setting of complex, chronic diseases and in the overlap of medical and mental health disease, both of which apply to alcohol-related liver disease (ALD). Alcohol use disorder (AUD) drives nearly all cases of ALD, and coexisting mental health disease is common. ALD is a complex condition with severe clinical manifestations and high mortality that can occasionally lead to liver transplantation. As a result, integrated care for ALD is an attractive proposition. The aim of this narrative review was to: (1) review the overlapping and concerning trends in the epidemiology of AUD and ALD; (2) use a theoretical framework for integrated care known as the "five-component model" as a basis to highlight the need for integrated care and the overlapping clinical manifestations and management of the 2 conditions; and (3) review the existing applications of integrated care in this area.

METHODS

We performed a narrative review of epidemiology, clinical manifestations, and management strategies in AUD and ALD, with a particular focus on areas of overlap that are pertinent to clinicians who manage each disease. Previously published models were reviewed for integrating care in AUD and ALD, both in the general ALD population and in the setting of liver transplantation.

FINDINGS

The incidences of AUD and ALD are rising, with a pronounced acceleration driven by the Coronavirus Disease 2019 pandemic. Hepatologists are underprepared to diagnose and treat AUD despite its high prevalence in patients with liver disease. A patient who presents with overlapping clinical manifestations of both AUD and ALD may not fit neatly into typical treatment paradigms for each individual disease but rather will require new management strategies that are appropriately adapted. As a result, the dimensions of integrated care, including collective ownership of shared goals, interdependence among providers, flexibility of roles, and newly created professional activities, are highly pertinent to the holistic management of both diseases.

IMPLICATIONS

Integrated care models have proliferated as recognition grows of the dual pathology of AUD and ALD. Ongoing coordination across disciplines and research in the fields of hepatology and addiction medicine are needed to further elucidate optimal mechanisms for collaboration and improved quality of care.

Cute Kid? Patient Obesity Status and the Use of Nonmedical Descriptors in Presentations by Pediatric Residents

Nonmedical descriptors, adjectives that are not related to a medical condition, such as "cute," are often used in presentations in pediatrics. We hypothesize that patterns of their use may reflect obesity bias. Descriptors used by pediatric residents presenting cases of children <9 years in an outpatient clinic during the 2018-2019 and 2019-2020 academic years were recorded. The primary outcome was the association of the use of positive nonmedical descriptors with children's obesity status using logistic regression. Positive descriptors were used in 14% of 994 presentations. Most addressed the appearance of the child with variations of "cute" and "adorable." There was no variation in use of positive descriptors by obesity status. On multivariate logistic regression, the odds of using positive descriptors were higher among female residents, and positive descriptor use declined with patient age. Negative descriptors were rare and often focused on weight.

Unveiling the power of neutral and inclusive language in tuberculosis prevention and care

Language plays a crucial role in shaping discourses and responses related to disease, particularly tuberculosis (TB). Stigmatising language and attitudes surrounding TB can lead to discrimination and marginalisation of affected individuals, creating barriers to seeking proper diagnosis and treatment. The terminology used to describe TB-affected individuals can be disempowering and criminalising, reinforcing an "othering" of those affected. To combat this, engaging with TB-affected communities is essential to co-construct a neutral and inclusive vocabulary that respects the dignity of individuals and fosters empathy and support. Thus, an inclusive language approach empowers affected individuals as active participants in their health management, encouraging open communication and the development of support networks. By adopting a neutral and inclusive language system, healthcare providers and communities can work together to remove unnoticed hurdles and align with the World Health Organization's TB care and prevention goals.

An Intervention-Based Approach to Achieve Racial Equity in Gynecologic Oncology

Racial inequities within gynecologic oncology exist at every step of the cancer continuum. Although the disparities have been well described, there is a significant gap in the literature focused on eliminating inequities in gynecologic cancer outcomes. The goal of this narrative review is to highlight successful, evidence-based interventions from within and outside of gynecologic oncology that alleviate disparity, providing a call to action for further research and implementation efforts within the field. These solutions are organized in the socioecologic framework, where multiple levels of influence-societal, community, organizational, interpersonal, and individual-affect health outcomes.