An analysis of documentation language and word choice among forensic mental health nurses

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.

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.

Nurses' use of pro re nata medication in adult acute mental healthcare settings: An integrative review

This integrative review explores the current pro re nata (PRN) medication practice in acute adult mental health settings. PRN medication is commonly used in acute mental health settings but there is lack of evidence of effectiveness of this practice. PRN medications have a number of adverse effects and increase the risk of morbidity in patients with a mental illness. Articles were identified from MEDLINE, CINAHL, Scopus, PubMed, PsycINFO, and Web of Science database. The STROBE critical appraisal tool was used to evaluate the quality of evidence, and inductive thematic analysis was used to extract main themes. Five themes regarding prescription practices, poor documentation, reasons to administer, medication misuse, and insufficient use of non-pharmacological interventions emerged among the 12 eligible articles. The study identified PRN medication practice gaps in adult mental health settings included insufficient documentation practice, underuse of therapeutic non-pharmacological interventions, and significant variability in PRN medication practice across the mental health professionals due to different levels of knowledge and experience.

Investigating the impact of cognitive bias in nursing documentation on decision-making and judgement

The clinical documentation of patients' mental status, behaviour and functioning is a fundamental aspect of inpatient mental health care. It is an important source of information-sharing with the interprofessional team and used by other clinicians within the circle of care to guide their decision-making process. Given the body of evidence highlighting concerns about the quality of nursing documentation and the growing literature demonstrating the presence of bias in healthcare, it is critically important that we examine the impact of this bias in nursing practice. The primary objective of this study was to determine whether clinical decisions and judgements change when nurses read documentation that is either biased or neutral. Using a quantitative, observational study that used surveys to collect data, participants were exposed to two patient vignettes and six clinical notes associated with each patient (notes were written with either biased or neutral language) and asked to make clinical decisions and judgements. Results from 199 nurse participants from a tertiary mental health hospital revealed a notable relationship between the type of notes read (biased vs. neutral) and clinical practice, namely, participants reading biased notes were less likely to offer health teaching when administering pro re nata (PRN) medication for sleep. We also found differences in decision-making and judgements based on the type of note read depending on years of experience and type of education. The results indicate that biased language in nursing documentation can influence other clinicians' decisions and judgements about patients, thereby indicating a cascade of bias.

Exploration of note writing by mental health nurses using a video scenario

AIMS AND OBJECTIONS

We aimed to explore the content and language of nursing documentation and gain insight into the internal processes of nurses while notetaking.

BACKGROUND

Documentation is a core competency of mental health nursing, has clinical and ethical importance and is the integral to the efficient and effective care provided to patients. However, issues related to the content and quality of nursing notes continues to be a concern and there remains gaps in our understanding about the internal processes that nurses engage in when writing notes.

DESIGN

We used a mixed method design that included a content analysis with note review and interviews.

METHODS

After watching a video, psychiatric nurses (n = 27) wrote a note and then were interviewed about their note taking process. We used the COREQ guidelines for reporting our data.

RESULTS

Participants relied on four main themes when determining what to include in their notes-what happened and what the patient said or did, plus anything different than baseline, and safety concerns. Analyses revealed the presence of bias in the notetaking and participants were not familiar with effective strategies to mitigate these during the documentation process. Lastly, we found that notes are typically consistent in using some of the SOAPE format with notes focused on direct observations and the use of 'facts', while assessment and construction of treatment plans are used to a lesser extent.

CONCLUSIONS

Our results provide insight into the decision-making process of nursing staff regarding their documentation practices: overall they appear unaware of the importance of their notes, and believe that capturing the facts about their patients is important, while devaluing their own input and interpretations.

RELEVANCE TO CLINICAL PRACTICE

Our results provide evidence that mental health nurses may need additional training regarding documentation, more specifically about what to include, word choice and bias.