Documentation of Screening for Firearm Access by Healthcare Providers in the Veterans Healthcare System: A Retrospective Study

Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data

OBJECTIVE

Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related.

MATERIALS AND METHODS

We accessed and digitized court records from the state of Washington (n = 1472). Human review established a gold standard label for firearm involvement (yes/no). We developed a key term search and trained supervised machine learning classifiers for this labeling task. Results were evaluated in a held-out test set.

RESULTS

The decision tree performed best (F1 score: 0.82). The key term list had perfect recall (1.0) and a modest F1 score (0.65).

DISCUSSION AND CONCLUSION

This case report highlights the accuracy, feasibility, and potential time-saved by using NLP to identify firearm involvement in alleged violent crimes based on digitized narratives from court documents.

Determining who healthcare providers screen for firearm access in the United States

Healthcare providers are well positioned to screen for firearm access to reduce risk of suicides, yet there is a limited understanding of how often and for whom firearm access screening occurs. The present study examined the extent to which providers screen for firearm access and sought to identify who has been screened in the past. The representative sample included 3510 residents from five US states who reported whether they have been asked about their access to firearms by a healthcare provider. The findings demonstrate that most participants have never been asked by a provider about firearm access. People who have been asked were more likely to be White, male, and firearm owners. Those with children under 17 years old in the home, that have been in mental health treatment, and report a history of suicidal ideation were more likely to be screened for firearm access. Although there are interventions for mitigating firearm related risks in healthcare settings, many providers may be missing the opportunity to implement these because they do not ask about firearm access.

A case for developing domain-specific vocabularies for extracting suicide factors from healthcare notes

The onset and persistence of life events (LE) such as housing instability, job instability, and reduced social connection have been shown to increase risk of suicide. Predictive models for suicide risk have low sensitivity to many of these factors due to under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE in clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical Language System (UMLS) selection, content analysis by subject matter experts (SME) and the Gravity Project, to data-driven expansion through contextual word embedding using Word2Vec. Our analysis covers EHR from the Veterans Affairs (VA) Corporate Data Warehouse (CDW) and measures the prevalence of LE across time for patients with known underlying cause of death in the National Death Index (NDI). We found that NLP methods had higher sensitivity of detecting LE relative to structured EHR (S-EHR) variables. We observed that, on average, suicide cases had higher rates of LE over time when compared to patients who died of non-suicide related causes with no previous history of diagnosed mental illness. When used to discriminate these outcomes, the inclusion of NLP derived variables increased the concentration of LE along the top 0.1%, 0.5% and 1% of predicted risk. LE were less informative when discriminating suicide death from non-suicide related death for patients with diagnosed mental illness.

Above and Beyond the Call of Duty: Rural Home Care and Hospice Nurses' Experiences Caring for Veterans

Approximately 4.7 million Veterans live in rural areas and face challenges receiving support and care for complex service-connected healthcare needs. The purpose of this qualitative descriptive study was to explore nurses' experiences caring for Veterans in rural home care/hospice settings. Semistructured interviews (N = 14) were conducted between August 2020 and March 2021. Content and thematic analysis resulted in three central themes: Mission readiness: Ensuring safe home environments, Partnering with Veterans: Knowing when to lead and when to follow, and It's my duty: Caring with purpose and honor. Study findings highlight the need for cultural competence in both rural and Veteran cultures. Implications for practice reinforce currently emerging national priorities, including personal safety considerations and need for further professional development associated with screening for firearms and gun safety. Other implications for home care/hospice agencies, as well as nursing education, are discussed.