Gauging the stress of long-term care nursing assistants using ecological momentary assessment, wearable sensors and end of day reconstruction.

BACKGROUND: Nursing assistants working in long-term care (LTC) often report that their job is stressful. To reduce their work stress, a better understanding of their stress profile is needed. OBJECTIVE: We aimed to pilot test methods to identify and understand stressors that LTC nursing assistants experience. METHODS: We asked each participant to provide wearable sensor/watch data, ecological momentary assessment (EMA) surveys and end of day review data over two eight-hour working shifts. RESULTS: Eight nursing assistants participated. All participants worked in a common continuing care retirement community in Maryland, United States of America. Our stress assessment method revealed 83 stressful events that were classified under 10 categories. Most of the reported events were rated as having a mild to low-moderate intensity. The three most common causes of stress were work demands and pressure (28.35%), heavy workload and staffing (19.69%), and safety issues and COVID-19 concerns (17.32%). We also explored the difference between stress events and intensity among different shifts. Disrespect from residents (22.73%) was the most commonly reported stressor during day shifts. Feeling rushed was the most commonly reported stressor during the evening (22.47%) and the night (38.46%) shifts. CONCLUSIONS: We found stress was commonly reported. Stress intensity conflicted with prior literature, and we explored possible explanations. IMPLICATIONS FOR PRACTICE: We discuss potential implications for these findings, modification of our methods to increase feasibility, the utility of these data collection methods for future work and suggest next steps.

Overlapping Physiologic Signs of Sepsis and Paroxysmal Sympathetic Hyperactivity After Traumatic Brain Injury: Exploring A Clinical Conundrum.

BACKGROUND: Paroxysmal sympathetic hyperactivity (PSH) occurs in a subset of patients with traumatic brain injury (TBI) and is associated with worse outcomes. Sepsis is also associated with worse outcomes after TBI and shares several physiologic features with PSH, potentially creating diagnostic confusion and suboptimal management of each. This is the first study to directly investigate the interaction between PSH and infection using robust diagnostic criteria. METHODS: We performed a retrospective cohort study of patients with TBI admitted to a level I trauma center intensive care unit with hospital length of stay of at least 2 weeks. From January 2016 to July 2018, 77 patients diagnosed with PSH were 1:1 matched by age and Glasgow Coma Scale to 77 patients without PSH. Trauma infectious diseases subspecialists prospectively documented assessments corroborating diagnoses of infection. Extracted data including incidence, timing, classification, and anatomical source of infections were compared according to PSH diagnosis. We also evaluated daily PSH clinical feature severity scores and systemic inflammatory response syndrome (SIRS) criteria and compared values for patients with and without confirmed infection, stratified by PSH diagnosis. RESULTS: During the first 2 weeks of hospitalization, there were no differences in rates of suspected (62%) nor confirmed (48%) infection between patients with PSH and controls. Specific treatments for PSH were initiated on median hospital day 7 and for confirmed infections on median hospital day 8. SIRS criteria could identify infection only in patients who were not diagnosed with PSH. CONCLUSIONS: In the presence of brain injury-induced autonomic nervous system dysregulation, the initiation and continuation of antimicrobial therapy is a challenging clinical decision, as standard physiologic markers of sepsis do not distinguish infected from noninfected patients with PSH, and these entities often present around the same time. Clinicians should be aware that PSH is a potential driver of SIRS, and familiarity with its diagnostic criteria as proposed by the PSH assessment measure is important. Management by a multidisciplinary team attentive to these issues may reduce rates of inappropriate antibiotic usage and misdiagnoses.

A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement.

Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded global healthcare system, which receives approximately 60 million primary care visits and 6 million emergency room visits annually. These systems, developed by clinical psychologists, psychiatrists, and AI researchers, are designed to aid in Cognitive Behavioral Therapy (CBT). The main focus of VMHAs is to provide relevant information to mental health professionals (MHPs) and engage in meaningful conversations to support individuals with mental health conditions. However, certain gaps prevent VMHAs from fully delivering on their promise during active communications. One of the gaps is their inability to explain their decisions to patients and MHPs, making conversations less trustworthy. Additionally, VMHAs can be vulnerable in providing unsafe responses to patient queries, further undermining their reliability. In this review, we assess the current state of VMHAs on the grounds of user-level explainability and safety, a set of desired properties for the broader adoption of VMHAs. This includes the examination of ChatGPT, a conversation agent developed on AI-driven models: GPT3.5 and GPT-4, that has been proposed for use in providing mental health services. By harnessing the collaborative and impactful contributions of AI, natural language processing, and the mental health professionals (MHPs) community, the review identifies opportunities for technological progress in VMHAs to ensure their capabilities include explainable and safe behaviors. It also emphasizes the importance of measures to guarantee that these advancements align with the promise of fostering trustworthy conversations.