We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point-of-care ultrasound (POCUS) providers.
 
  We used publicly available long short term memory (LSTM) deep learning basic architecture that can track temporal changes and relationships in real-time video, to create an algorithm for ultrasound video analysis. The algorithm was trained on public domain IVC ultrasound videos to improve its ability to recognize changes in varied ultrasound video. A total of 220 IVC videos were used, 10% of the data was randomly used for cross correlation during training. Data were augmented through video rotation and manipulation to multiply effective training data quantity.  https://www.selleckchem.com/products/camostat-mesilate-foy-305.html  After training, the algorithm was tested on the 50 new IVC ultrasound video obtained from public domain sources and not part of the data set used in training or cross validation. Fleiss' κ was calculated to compare level of agreement between the 3 POCUpatients. Such an algorithm could be adopted to run in real-time on any ultrasound machine with a video output, easing the burden on novice POCUS users by limiting their task to obtaining and maintaining a sagittal proximal IVC view and allowing the artificial intelligence make real-time determinations.Emergency medicine has increasingly focused on addressing social determinants of health (SDoH) in emergency medicine. However, efforts to standardize and evaluate measurement tools and compare results across studies have been limited by the plethora of terms (eg, SDoH, health-related social needs, social risk) and a lack of consensus regarding definitions. Specifically, the social risks of an individual may not align with the social needs of an individual, and this has ramifications for policy, research, risk stratification, and payment and for the measurement of health care quality. With the rise of social emergency medicine (SEM) as a field, there is a need for a simplified and consistent set of definitions. These definitions are important for clinicians screening in the emergency department, for health systems to understand service needs, for epidemiological tracking, and for research data sharing and harmonization. In this article, we propose a conceptual model for considering SDoH measurement and provide clear, actionable, definitions of key terms to increase consistency among clinicians, researchers, and policy makers.
  Emergency departments (EDs) are called to implement public health and prevention initiatives, such as infectious disease screening. The perception that ED resources are insufficient is a primary barrier. Resource needs are generally conceptualized in terms of total number of ED encounters, without formal calculation of the number of encounters for which a service is required. We illustrate potential differences in the estimated volume of service need relative to ED census using the examples of HIV and hepatitis C (HCV) screening.
 
  This cross-sectional analysis adjusted the proportion of ED encounters in which patients are eligible for HIV and HCV screening according to a cascade of successively more restrictive patient selection criteria, presuming full implementation of each criterion. Parameter estimates for the proportion satisfying each selection criterion were derived from the electronic health records of an urban academic facility and its ED HIV and HCV screening program during 2 time periods. The primary outcome was the estimated reduction in proportion of ED visits eligible for screening after application of the entire cascade.
 
  There were 76,104 ED encounters during the study period. Applying all selection criteria reduced the number of required screens by 97.1% (95% confidence interval, 97.0-97.2) for HIV and 86.1% (95% confidence interval, 85.9-86.3) for HCV.
 
  Using the example of HIV and HCV screening, the application of eligibility metrics reduces the volume of service need to a smaller, more feasible number than estimates from ED census alone. This approach might be useful for clarifying perceived service need and guiding operational planning.
 Using the example of HIV and HCV screening, the application of eligibility metrics reduces the volume of service need to a smaller, more feasible number than estimates from ED census alone. This approach might be useful for clarifying perceived service need and guiding operational planning.
  Little academic investigation has been done to describe emergency department (ED) practice structure and quality improvement activities. Our objective was to describe staffing, payment mechanisms, and quality improvement activities among EDs in a nationwide quality improvement network and also stratify results to descriptively compare (1) single- versus multi-site EDs and (2) small-group versus large-group EDs.
 
  Observational study examining EDs that completed activities for the 2018 wave of the Emergency Quality Network (E-QUAL), a voluntary network of EDs nationwide that self-report quality improvement activities. EDs were defined as single-site or multi-site based on self-reported billing practices; additionally, EDs were defined as large-group if they and a majority of other sites with the same group name also identified as multi-site. All other sites were deemed small-group.
 
  Data from 377 EDs were included. For staffing, the median number of clinicians was 17 overall (16 single-site; 19 multi-site).etween single- and multi-site EDs. Group-level analysis suggests that practice structure may influence adoption of quality improvement strategies. Future work is needed to further evaluate practice structure and its influence on quality improvement activities and quality.
  The homeless patient population is known to have a high occurrence of inappropriate emergency department (ED) utilization. The study hospital initiated a dedicated homeless clinic targeting patients experiencing homelessness with a combination of special features. We aim to determine whether this mode of care can reduce inappropriate ED utilization among homeless patients.
 
  We conducted a retrospective observational study from July 1, 2017 to Dec 31, 2017. The study enrolled all homeless patients who visited any hospital regular clinic, dedicated homeless clinic, and ED at least once during the study period. ED homeless patients were divided into four groups (A no clinic visits; B those who only visited hospital regular clinic; C those who only visited dedicated homeless clinic; and D those who visited both hospital regular clinic and dedicated homeless clinic). The New York University algorithm was used to determine appropriate ED utilization. We compared inappropriate ED utilization among patients from these groups.