Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.

There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to identify and provide individuals at risk for developing PTSD with timely interventions. The present study seeks to identify a minimal set of trauma-related symptoms, assessed during the weeks following traumatic exposure, that can accurately predict PTSD. Participants were 2185 adults (Mean age=36.4 years; 64% women; 50% Black) presenting for emergency care following traumatic exposure. Participants received a 'flash survey' with 6-8 varying symptoms (from a pool of 26 trauma symptoms) several times per week for eight weeks following the trauma exposure (each symptom assessed ∼6 times). Features (mean, sd, last, worst, peak-end scores) from the repeatedly assessed symptoms were included as candidate variables in a CART machine learning analysis to develop a pragmatic predictive algorithm. PTSD (PCL-5 ≥38) was present for 669 (31%) participants at the 8-week follow-up. A classification tree with three splits, based on mean scores of nervousness, rehashing, and fatigue, predicted PTSD with an Area Under the Curve of 0.836. Findings suggest feasibility for a 3-item assessment protocol, delivered once per week, following traumatic exposure to assess and potentially facilitate follow-up care for those at risk.

Development of a health equity journal club to address health care disparities and improve cultural competence among emergency medicine practitioners.

Health care disparities have been magnified by the COVID-19 pandemic. Only recently has the medical community acknowledged implicit bias and systemic racism as a public health emergency. Graduate medical education has been slow to adopt curricula beyond lecture-based formats that specifically address social determinants of health (SDOH) and its impact on communities. Curricula addressing unconscious (implicit) biases and their influence on patient care has not been widely adopted. The emergency department (ED) has a unique role in addressing health care disparities. Approximately 69% of emergency medicine residency programs incorporate cultural competency training in their curricula. Most are primarily lecture-based without a longitudinal component, and gaps exist in content, quality, and expertise of the presenters. Lecture-based formats may not be best suited to manage the nuanced conversations necessary to dismantle biases and socialized beliefs that result in disparities for marginalized communities. Reporting little or no education in medical school related to SDOH, residents acknowledge that barriers to care exist, but have limited or no knowledge of what those barriers are or how mitigate them. To improve health equity, understanding and competence in caring for culturally and ethnically diverse populations, we developed a monthly, longitudinal, SDOH- and cultural competency-based "health equity journal club" (HEJC) for all levels of ED staff. Four educational domains were developed, and specific content within each domain was selected based on predetermined criteria. Content for each session was mapped to the ACGME program and core competency milestone requirements, ACGME Clinical Learning Environment (CLER) mandates, and The Joint Commission's institutional recommendations for culturally competent care. The HEJC series has been successful in reducing barriers to identifying biases in health care; translating literature to clinical care; generating initiatives and interdisciplinary research; and cultivating interest in community health, health advocacy, and public policy.

Time Well Spent: Patient Industry and Occupation Data Collection in Emergency Departments.

OBJECTIVE: No comprehensive national system tracking work-related diseases and injuries exists in the United States. Industry and occupation (I/O) are the missing data elements that would make existing healthcare data useful for occupational health. The authors previously petitioned the National Uniform Billing Committee (NUBC) to adopt I/O standards for states to consider during their healthcare data rulemaking processes. METHODS: The NUBC asked for a pilot study to ascertain the potential burden. The time and cost to ask I/O questions in two hospital emergency departments was evaluated. RESULTS: Asking four I/O questions required 48 seconds on average and cost between $520 and $623 per Registrar per year. The annual cost for the two hospitals to gather I/O on every patient was $4160 and $15,000. CONCLUSIONS: We conclude no undue burden compared with the estimated $250 billion cost of occupational illnesses and injuries.

Assessment of Sleep Disruption and Sleep Quality in Naval Special Warfare Operators.

Little is known about sleep in elite military populations who are exposed to higher operational demands, unpredictable training, deployment, and mission cycles. Twenty-nine Naval Special Warfare (NSW) Operators wore an actiwatch for an 8-day/7-night period for objective sleep assessment and completed a nightly sleep log. A total of 170 nights of actigraphically recorded sleep were collected. When comparing objectively versus subjectively recorded sleep parameter data, statistically significant differences were found. Compared with sleep log data, actigraphy data indicate NSW Operators took longer to fall asleep (an average of 25.82 minutes), spent more time awake after sleep onset (an average of 39.55 minutes), and demonstrated poorer sleep efficiency (83.88%) (ps < 0.05). Self-reported sleep quality during the study period was 6.47 (maximum score = 10). No relationships existed between the objectively derived sleep indices and the self-reported measure of sleep quality (rs = -0.29 to 0.09, all ps > 0.05). Strong inter-relationships existed among the subjectively derived sleep indices (e.g., between self-reported sleep quality and sleep efficiency; r = 0.61, p < 0.001). To our knowledge, this is the first study to objectively and subjectively quantify sleep among NSW Operators. These findings suggest sleep maintenance and sleep efficiency are impaired when compared to normative population data.