Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study.

This study provides insight into New York City residents' perceptions about violence after the outbreak of Coronavirus disease (COVID-19) based on information from communities in New York City Housing Authority (NYCHA) buildings. In this novel analysis, we used focus group and social media data to confirm or reject findings from qualitative interviews. We first used data from 69 in-depth, semi-structured interviews with low-income residents and community stakeholders to further explore how violence impacts New York City's low-income residents of color, as well as the role of city government in providing tangible support for violence prevention during co-occurring health (COVID-19) and social (anti-Black racism) pandemics. Residents described how COVID-19 and the Black Lives Matter movement impacted safety in their communities while offering direct recommendations to improve safety. Residents also shared recommendations that indirectly improve community safety by addressing long term systemic issues. As the recruitment of interviewees was concluding, researchers facilitated two focus groups with 38 interviewees to discuss similar topics. In order to assess the degree to which the themes discovered in our qualitative interviews were shared by the broader community, we developed an integrative community data science study which leveraged natural language processing and computer vision techniques to study text and images on public social media data of 12 million tweets generated by residents. We joined computational methods with qualitative analysis through a social work lens and design justice principles to most accurately and holistically analyze the community perceptions of gun violence issues and potential prevention strategies. Findings indicate valuable community-based insights that elucidate how the co-occurring pandemics impact residents' experiences of gun violence and provide important implications for gun violence prevention in a digital era.

Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

OBJECTIVES: Home health care patients who are at risk for becoming Incapacitated with No Evident Advance Directives or Surrogates (INEADS) may benefit from timely intervention to assist them with advance care planning. This study aimed to develop natural language processing algorithms for identifying home care patients who do not have advance directives, family members, or close social contacts who can serve as surrogate decision-makers in the event that they lose decisional capacity. DESIGN: Cross-sectional study of electronic health records. SETTING AND PARTICIPANTS: Patients receiving post-acute care discharge services from a large home health agency in New York City in 2019 (n = 45,390 enrollment episodes). METHODS: We developed a natural language processing algorithm for identifying information documented in free-text clinical notes (n = 1,429,030 notes) related to 4 categories: evidence of close relationships, evidence of advance directives, evidence suggesting lack of close relationships, and evidence suggesting lack of advance directives. We validated the algorithm against Gold Standard clinician review for 50 patients (n = 314 notes) to calculate precision, recall, and F-score. RESULTS: Algorithm performance for identifying text related to the 4 categories was excellent (average F-score = 0.91), with the best results for "evidence of close relationships" (F-score = 0.99) and the worst results for "evidence of advance directives" (F-score = 0.86). The algorithm identified 22% of all clinical notes (313,290 of 1,429,030) as having text related to 1 or more categories. More than 98% of enrollment episodes (48,164 of 49,141) included at least 1 clinical note containing text related to 1 or more categories. CONCLUSIONS AND IMPLICATIONS: This study establishes the feasibility of creating an automated screening algorithm to aid home health care agencies with identifying patients at risk of becoming INEADS. This screening algorithm can be applied as part of a multipronged approach to facilitate clinician support for advance care planning with patients at risk of becoming INEADS.

Home Care Clinicians' Perspectives on Advance Care Planning for Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates.

Advance care planning is important and timely for patients receiving home health services; however, opportunities to facilitate awareness and engagement in this setting are often missed. This qualitative descriptive study elicited perspectives of home health nurses and social workers regarding barriers and facilitators to creating advance care plans in home health settings, with particular attention to patients with few familial or social contacts who can serve as surrogate decision-makers. We interviewed 15 clinicians employed in a large New York City-based home care agency in 2021-2022. Participants reported a multitude of barriers to supporting patients with advance care planning at the provider level (eg, lack of time and professional education, deferment, discomfort), patient level (lack of knowledge, mistrust, inadequate support, deferment, language barriers), and system level (eg, discontinuity of care, variations in advance care planning documents, legal concerns, lack of institutional protocols and centralized information). Participants noted that greater socialization and connection to existing educational resources regarding the intended purpose, scope, and applicability of advance directives could benefit home care patients.

Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.

BACKGROUND: Child abuse and neglect, once viewed as a social problem, is now an epidemic. Moreover, health providers agree that existing stereotypes may link racial and social class issues to child abuse. The broad adoption of electronic health records (EHRs) in clinical settings offers a new avenue for addressing this epidemic. To reduce racial bias and improve the development, implementation, and outcomes of machine learning (ML)-based models that use EHR data, it is crucial to involve marginalized members of the community in the process. OBJECTIVE: This study elicited Black and Latinx primary caregivers' viewpoints regarding child abuse and neglect while living in underserved communities to highlight considerations for designing an ML-based model for detecting child abuse and neglect in emergency departments (EDs) with implications for racial bias reduction and future interventions. METHODS: We conducted a qualitative study using in-depth interviews with 20 Black and Latinx primary caregivers whose children were cared for at a single pediatric tertiary-care ED to gain insights about child abuse and neglect and their experiences with health providers. RESULTS: Three central themes were developed in the coding process: (1) primary caregivers' perspectives on the definition of child abuse and neglect, (2) primary caregivers' experiences with health providers and medical documentation, and (3) primary caregivers' perceptions of child protective services. CONCLUSIONS: Our findings highlight essential considerations from primary caregivers for developing an ML-based model for detecting child abuse and neglect in ED settings. This includes how to define child abuse and neglect from a primary caregiver lens. Miscommunication between patients and health providers can potentially lead to a misdiagnosis, and therefore, have a negative impact on medical documentation. Additionally, the outcome and application of the ML-based models for detecting abuse and neglect may cause additional harm than expected to the community. Further research is needed to validate these findings and integrate them into creating an ML-based model.

Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study.

OBJECTIVE: The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect. MATERIALS AND METHODS: We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect. RESULTS: Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation. DISCUSSION: Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect. CONCLUSIONS: Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.

Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions.

Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to machine learning-based risk models development and evaluation: (1) biases in the data; (2) clinical documentation system design issues; (3) lack of centralized evidence base for child abuse and neglect; (4) lack of "gold standard "in assessment and diagnosis of child abuse and neglect; (5) challenges in evaluation of risk prediction performance; (6) challenges in testing predictive models in practice; and (7) challenges in presentation of machine learning-based prediction to clinicians and patients. We provide recommended solutions to each of the 7 ethical challenges and identify several areas for further policy and research.

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support.