Discharge process for patients experiencing homelessness in the emergency department: A thematic qualitative study.

People experiencing homelessness are more likely to utilize emergency departments than their non-homeless counterparts. However, obtaining a bed in a homeless shelter for patients can be complex. To better understand the challenges of finding a safe discharge plan for homeless patients in the emergency department, our team conducted interviews with emergency department social workers and homeless shelter case managers in the Boston area. We identified and mapped the stages in the processes performed by both parties, identifying challenges with successful placement into a shelter. Furthermore, we assembled a data dictionary of key factors considered when assessing a patient's fit for a homeless shelter. By identifying bottlenecks and areas of opportunity, this study serves as a first step in enabling homeless individuals to receive the post-discharge assistance they require.

Critical Care in the Austere Environment.

Practice of critical care in austere settings involves navigating rapidly evolving environments, where physical resources, provider availability, and healthcare capacity are constrained. Austere Critical Care focuses on maintaining the highest standard of care possible for patients while also identifying resource limitations, responding to patient surges, and adhering to proper triage practices at the austere site. This includes transferring the patient when able and necessary. This article describes the current practice of critical care medicine in the austere environment, using recent natural disasters, pandemics, and conflicts as case studies.

Effects of Low-Level Light Therapy on Resting-State Connectivity Following Moderate Traumatic Brain Injury: Secondary Analyses of a Double-blinded Placebo-controlled Study.

Background Low-level light therapy (LLLT) has been shown to modulate recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on the functional connectivity of the brain when at rest has not been well studied. Purpose To use functional MRI to assess the effect of LLLT on whole-brain resting-state functional connectivity (RSFC) in patients with moderate TBI at acute (within 1 week), subacute (2-3 weeks), and late-subacute (3 months) recovery phases. Materials and Methods This is a secondary analysis of a prospective single-site double-blinded sham-controlled study conducted in patients presenting to the emergency department with moderate TBI from November 2015 to July 2019. Participants were randomized for LLLT and sham treatment. The primary outcome of the study was to assess structural connectivity, and RSFC was collected as the secondary outcome. MRI was used to measure RSFC in 82 brain regions in participants during the three recovery phases. Healthy individuals who did not receive treatment were imaged at a single time point to provide control values. The Pearson correlation coefficient was estimated to assess the connectivity strength for each brain region pair, and estimates of the differences in Fisher z-transformed correlation coefficients (hereafter, z differences) were compared between recovery phases and treatment groups using a linear mixed-effects regression model. These analyses were repeated for all brain region pairs. False discovery rate (FDR)-adjusted P values were computed to account for multiple comparisons. Quantile mixed-effects models were constructed to quantify the association between the Rivermead Postconcussion Symptoms Questionnaire (RPQ) score, recovery phase, and treatment group. Results RSFC was evaluated in 17 LLLT-treated participants (median age, 50 years [IQR, 25-67 years]; nine female), 21 sham-treated participants (median age, 50 years [IQR, 43-59 years]; 11 female), and 23 healthy control participants (median age, 42 years [IQR, 32-54 years]; 13 male). Seven brain region pairs exhibited a greater change in connectivity in LLLT-treated participants than in sham-treated participants between the acute and subacute phases (range of z differences, 0.37 [95% CI: 0.20, 0.53] to 0.45 [95% CI: 0.24, 0.67]; FDR-adjusted P value range, .010-.047). Thirteen different brain region pairs showed an increase in connectivity in sham-treated participants between the subacute and late-subacute phases (range of z differences, 0.17 [95% CI: 0.09, 0.25] to 0.26 [95% CI: 0.14, 0.39]; FDR-adjusted P value range, .020-.047). There was no evidence of a difference in clinical outcomes between LLLT-treated and sham-treated participants (range of differences in medians, -3.54 [95% CI: -12.65, 5.57] to -0.59 [95% CI: -7.31, 8.49]; P value range, .44-.99), as measured according to RPQ scores. Conclusion Despite the small sample size, the change in RSFC from the acute to subacute phases of recovery was greater in LLLT-treated than sham-treated participants, suggesting that acute-phase LLLT may have an impact on resting-state neuronal circuits in the early recovery phase of moderate TBI. ClinicalTrials.gov Identifier: NCT02233413 © RSNA, 2024 Supplemental material is available for this article.

Biomedical text readability after hypernym substitution with fine-tuned large language models.

The advent of patient access to complex medical information online has highlighted the need for simplification of biomedical text to improve patient understanding and engagement in taking ownership of their health. However, comprehension of biomedical text remains a difficult task due to the need for domain-specific expertise. We aimed to study the simplification of biomedical text via large language models (LLMs) commonly used for general natural language processing tasks involve text comprehension, summarization, generation, and prediction of new text from prompts. Specifically, we finetuned three variants of large language models to perform substitutions of complex words and word phrases in biomedical text with a related hypernym. The output of the text substitution process using LLMs was evaluated by comparing the pre- and post-substitution texts using four readability metrics and two measures of sentence complexity. A sample of 1,000 biomedical definitions in the National Library of Medicine's Unified Medical Language System (UMLS) was processed with three LLM approaches, and each showed an improvement in readability and sentence complexity after hypernym substitution. Readability scores were translated from a pre-processed collegiate reading level to a post-processed US high-school level. Comparison between the three LLMs showed that the GPT-J-6b approach had the best improvement in measures of sentence complexity. This study demonstrates the merit of hypernym substitution to improve readability of complex biomedical text for the public and highlights the use case for fine-tuning open-access large language models for biomedical natural language processing.

Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis.

Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.

Entropy removal of medical diagnostics.

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

Impact of medical technologies may be predicted using constructed graph bibliometrics.

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research. Random forest classification models were trained using 28 bibliometric features calculated from a dataset of 1,485,958 publications in medicine to retrospectively predict whether a publication would become high-impact. For each random forest model, the balanced accuracy score was above 0.95 and the area under the receiver operating characteristic curve was above 0.99. The high performance of high impact research prediction using our proposed models show that machine learning technologies are promising algorithms that can support funding decision-making for medical research.

Predictors of Mortality in Extracorporeal Membrane Oxygenation Support Patients Following Major Trauma.

INTRODUCTION: The usage of extracorporeal membrane oxygenation (ECMO) in trauma patients has increased significantly within the past decade. Despite increased research on ECMO application in trauma patients, there remains limited data on factors predicting morbidity and mortality outcome. Therefore, the primary objective of this study is to describe patient characteristics that are independently associated with mortality in ECMO therapy in trauma patients, to further guide future research. METHODS: This retrospective study was conducted using the Trauma Quality Improvement Program database from 2010 to 2019. All adult (age ≥ 16 y) trauma patients that utilized ECMO were included. A Significant differences (P < 0.05) in demographic and clinical characteristics between groups were calculated using an independent t-test for normal distributed continuous values, a Mann-Whitney U test for non-normal distributed values, and a Pearson chi-square test for categorical values. A multivariable regression model was used to identify independent predictors for mortality. A survival flow chart was constructed by using the strongest predictive value for mortality and using the optimal cut-off point calculated by the Youden index. RESULTS: Five hundred forty-two patients were included of whom 205 died. Multivariable analysis demonstrated that the female gender, ECMO within 4 h after presentation, a decreased Glasgow Coma Scale, increased age, units of blood in the first 4 h, and abbreviated injury score for external injuries were independently associated with mortality in ECMO trauma patients. It was found that an external abbreviated injury score of ≥3 had the strongest predictive value for mortality, as patients with this criterion had an overall 29.5% increased risk of death. CONCLUSIONS: There is an ongoing increasing trend in the usage of ECMO in trauma patients. This study has identified multiple factors that are individually associated with mortality. However, more research must be done on the association between mortality and noninjury characteristics like Pao2/Fio2 ratio, acute respiratory distress syndrome classification, etc. that reflect the internal state of the patient.

The Certification Matters: A Comparative Performance Analysis of Combat Application Tourniquets versus Non-Certified CAT Look-Alike Tourniquets.

INTRODUCTION: The Stop the Bleed campaign provided civilians with tourniquet application training and increased the demand for tourniquets among the general population, which led to the development of new commercially available devices. However, most widely available tourniquets have not undergone testing by regulatory bodies and their efficacy remains unknown. STUDY OBJECTIVE: This study aimed to compare the efficacy and performance of Combat Application Tourniquets (CAT) versus uncertified tourniquets. METHODS: This study compared 25 CAT with 50 commercially available "look-alike" tourniquets (LA-TQ) resembling the CAT. The CAT and the LA-TQ were compared for cost, size, and tested during one-hour and six-hour applications on a manikin's leg. The outcomes were force applied, force variation during the application, and tourniquet rupture rate. RESULTS: The LA-TQ were cheaper (US$6.07 versus US$27.19), shorter, and had higher inter-device variability than the CAT (90.1 [SE = 0.5] cm versus 94.5 [SE = 0.1] cm; P <.001). The CAT applied a significantly greater force during the initial application when compared to the LA-TQ (65 [SE = 3] N versus 14 [SE = 1] N; P <.001). While the initial application force was maintained for up to six hours in both groups, the CAT group applied an increased force during one-hour applications (group effect: F [1,73] = 105.65; P <.001) and during six-hour applications (group effect: F [1,12] = 9.79; P = .009). The rupture rate differed between the CAT and the LA-TQ (0% versus 4%). CONCLUSION: The LA-TQ applied a significantly lower force and had a higher rupture rate compared to the CAT, potentially affecting tourniquet performance in the context of public bleeding control. These findings warrant increased layperson education within the framework of the Stop the Bleed campaign and further investigations on the effectiveness of uncertified devices in real-world applications.

Circulating cellular clusters are associated with thrombotic complications and clinical outcomes in COVID-19.

We sought to study the role of circulating cellular clusters (CCC) -such as circulating leukocyte clusters (CLCs), platelet-leukocyte aggregates (PLA), and platelet-erythrocyte aggregates (PEA)- in the immunothrombotic state induced by COVID-19. Forty-six blood samples from 37 COVID-19 patients and 12 samples from healthy controls were analyzed with imaging flow cytometry. Patients with COVID-19 had significantly higher levels of PEAs (p value<0.001) and PLAs (p value = 0.015) compared to healthy controls. Among COVID-19 patients, CLCs were correlated with thrombotic complications (p value = 0.016), vasopressor need (p value = 0.033), acute kidney injury (p value = 0.027), and pneumonia (p value = 0.036), whereas PEAs were associated with positive bacterial cultures (p value = 0.033). In predictive in silico simulations, CLCs were more likely to result in microcirculatory obstruction at low flow velocities (≤1 mm/s) and at higher branching angles. Further studies on the cellular component of hyperinflammatory prothrombotic states may lead to the identification of novel biomarkers and drug targets for inflammation-related thrombosis.

Network Analysis of Academic Medical Center Websites in the United States.

Healthcare resources are published annually in repositories such as the AHA Annual Survey DatabaseTM. However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

Influence of ICU Surge and Capacity on COVID Mortality Across U.S. States and Regions During the COVID-19 Pandemic.

BACKGROUND: To describe the influence of COVID-19 caseload surges and overall capacity in the intensive care unit (ICU) on mortality among US population and census divisions. METHODS: A retrospective analysis of the national COVID ActNow database between January 1, 2021 until March 1, 2022. The main outcome used was COVID-19 weekly mortality rates, which were calculated and incorporated into several generalized estimation of effects models with predictor variables that included ICU bed capacity, as well as ICU capacity used by COVID cases while adjusting for ratios of vaccinations in populations, case density, and percentage of the population over the age of 65. RESULTS: Each 1% increase in general ICU capacity is correlated with approximately 5 more weekly deaths from COVID-19 per 100,000 population and each percentage increase in the number of patients with COVID-19 admitted to the ICU resulted in approximately 10 more COVID-19 deaths per week per 100,000 population. Significant differences in ability to handle caseload surges were observed across US census divisions. CONCLUSIONS: A strong association was observed between COVID-19 ICU surges, overall ICU surge, and increased mortality. Further research is needed to reveal best practices and public health measures to prevent ICU overcrowding amidst future pandemics and disaster responses.

Emoji Education: How Students Can Help Increase Health Awareness by Making Emojis.

Emojis can improve health communication, especially when incorporating emojis into traditionally word-only texts. Beyond improving communication, emojis also offer greater access to health care, especially for vulnerable and marginalized populations with limited health literacy. A recent study found that 94% of patients with limited health literacy preferred health reports with emojis. Moreover, health officials are considering adding emojis to cardiopulmonary resuscitation guidelines and public health guidelines for handwashing. As the world evolves with new technology and new methods of communication, we must also evolve the language and method we use to communicate health information to patients. In this viewpoint, we aim to discuss the methods health care professionals can use to develop novel communication methods using emojis and the benefits of their incorporation into health care communication.

End-user evaluation of an interface for clinical decision support using predictive algorithms.

There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology. Clinical Relevance- This end-user evaluation of a novel CDSS highlights the importance of end-user experience in the workflow integration of advanced computational algorithms for bedside decision support during critical care.

Looking back on forward-looking COVID models.

Covid Act Now (CAN) developed an epidemiological model that takes various non-pharmaceutical interventions (NPIs) into account and predicts viral spread and subsequent health outcomes. In this study, the projections of the model developed by CAN were back-tested against real-world data, and it was found that the model consistently overestimated hospitalizations and deaths by 25%-100% and 70%-170%, respectively, due in part to an underestimation of the efficacy of NPIs. Other COVID models were also back-tested against historical data, and it was found that all models generally captured the potential magnitude and directionality of the pandemic in the short term. There are limitations to epidemiological models, but understanding these limitations enables these models to be utilized as tools for data-driven decision-making in viral outbreaks. Further, it can be valuable to have multiple, independently developed models to mitigate the inaccuracies of or to correct for the incorrect assumptions made by a particular model.