In-Hospital Mortality by Race and Ethnicity Among Hospitalized COVID-19 Patients Using Data From the US National COVID Cohort Collaborative.

Background: Studies examining racial and ethnic disparities in-hospital mortality for patients hospitalized with COVID-19 had mixed results. Findings from patients within academic medical centers (AMCs) are lacking, but important given the role of AMCs in improving health equity.

Objective: The purpose of this study is to assess whether minority patients hospitalized with COVID-19 in National COVID Cohort Collaborative (N3C) institutions, which consist predominantly of AMCs, have higher mortality rates relative to White patients.

WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging.

Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally associated with human intelligence. At present, AI has been widely used in a variety of ultrasound tasks, including in point-of-care ultrasound, echocardiography, and various diseases of different organs. However, the characteristics of ultrasound, compared to other imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses significant additional challenges to AI.

Childhood Educational Outcomes of Infants Born With Esophageal Atresia With or Without Tracheoesophageal Fistula.

Background: There are limited studies on educational outcomes of children born with esophageal atresia (EA) with or without tracheoesophageal fistula (TEF). We aimed to compare 3rd to 5th grade academic proficiency among children born with EA/TEF versus unaffected children.

Methods: Children born with EA/TEF (2000-2005) were identified from the Arkansas Reproductive Health Monitoring System.

Association of Allergy Specialty Care and Asthma Outcomes for Medicaid-Enrolled Children.

Objective: To evaluate the comparative effectiveness of allergy specialist care for children with asthma enrolled in the Arkansas Medicaid program.

Study Design: We used the Arkansas All-Payers Claims Database to identify Medicaid-enrolled children with asthma who had an allergy specialist visit in 2018. These children were propensity score matched to children without an allergy specialist visit to evaluate differences in asthma-related adverse events (AAEs), specifically emergency department visits and/or hospitalizations in 2019.

A Statewide Examination of Medical Cannabis Purchasing Patterns in Arkansas Over the Three Years Immediately Following Legalization.

The use of medical cannabis (MC) to treat a host of conditions has expanded considerably in the United States; however, precise quantitative assessments of purchasing characteristics are unknown. This study sought to characterize the trends in MC purchases, US dollars spent, and type and amount purchased by demographic and clinical characteristics. This descriptive exploratory association study examined statewide MC registry data in Arkansas linked at the person level with statewide transaction data documenting each MC purchase.

Health consumers' ethical concerns towards artificial intelligence in Australian emergency departments.

Objectives: To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs.

Methods: Qualitative semi-structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022.

Results: We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs.

Association Between Prepregnancy Body Mass Index and Newborn Breastfeeding Initiation.

The benefits of breastfeeding a newborn are well documented. Identification of mothers who do not initiate breastfeeding is essential for developing initiatives to improve breastfeeding initiation. The study used data from the National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS) birth certificate data (2014-2021) to identifying 15,599,930 in-hospital deliveries.

Ultrasound during Advanced Life Support-Help or Harm?

Ultrasound is used in cardiopulmonary resuscitation (CPR) and advanced life support (ALS). However, there is divergence between the recommendations of many emergency and critical care societies who support its use and the recommendations of many international resuscitation organizations who either recommend against its use or recommend it only in limited circumstances. Ultrasound offers potential benefits of detecting reversable causes of cardiac arrest, allowing specific interventions.

Student ultrasound education - current views and controversies.

As an extension of the clinical examination and as a diagnostic and problem-solving tool, ultrasound has become an established technique for clinicians. A prerequisite for high-quality clinical ultrasound practice is adequate student ultrasound training. In light of the considerable heterogeneity of ultrasound curricula in medical studies worldwide, this review presents basic principles of modern medical student ultrasound education and advocates for the establishment of an ultrasound core curriculum embedded both horizontally and vertically in medical studies.

Applications of natural language processing at emergency department triage: A narrative review.

Introduction: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.

Methods: All English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion.

Attitudes towards artificial intelligence in emergency medicine.

Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine.

Methods: We undertook a qualitative interview-based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks.

Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model.

Objective: Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes.

Methods: Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors.

Examining Impacts of Healthy Families America on Infant Health Care.

Objective: Healthy Families America (HFA) is an evidence-based home visiting program that provides parenting education with the overall goal of preventing child maltreatment and optimizing child development. This study compares the health care utilization and vaccination of infants enrolled in HFA with similar infants not in the program.

Methods: From January 2014 to December 2020, 604 children served statewide by HFA in Arkansas were identified in vital statistics and administrative medical claims records.

Western Australian medical students' attitudes towards artificial intelligence in healthcare.

Introduction: Surveys conducted internationally have found widespread interest in artificial intelligence (AI) amongst medical students. No similar surveys have been conducted in Western Australia (WA) and it is not known how medical students in WA feel about the use of AI in healthcare or their understanding of AI. We aim to assess WA medical students' attitudes towards AI in general, AI in healthcare, and the inclusion of AI education in the medical curriculum.

Asthma Quality Measurement and Adverse Outcomes in Medicaid-Enrolled Children.

Objectives: To determine the association between the asthma medication ratio (AMR) quality measure and adverse outcomes among Medicaid-enrolled children with asthma in Arkansas, given concerns regarding the utility of the AMR in evaluating pediatric risk of asthma-related adverse events (AAEs).

Methods: We used the Arkansas All-Payer Claims Database to identify Medicaid-enrolled children with asthma using a nonrestrictive case definition and additionally using the standard Healthcare Effectiveness Data and Information Set (HEDIS) persistent asthma definition. We assessed the AMR using the traditional dichotomous HEDIS AMR categorization and across 4 expanded AMR categories.

Deep learning model to predict exercise stress test results: Optimizing the diagnostic test selection strategy and reduce wastage in suspected coronary artery disease patients.

Background: Cardiac exercise stress testing (EST) offers a non-invasive way in the management of patients with suspected coronary artery disease (CAD). However, up to 30% EST results are either inconclusive or non-diagnostic, which results in significant resource wastage. Our aim was to build machine learning (ML) based models, using patients demographic (age, sex) and pre-test clinical information (reason for performing test, medications, blood pressure, heart rate, and resting electrocardiogram), capable of predicting EST results beforehand including those with inconclusive or non-diagnostic results.

Survival From Birth Until Young Adulthood Among Individuals With Congenital Heart Defects: CH STRONG.

Background: Limited population-based information is available on long-term survival of US individuals with congenital heart defects (CHDs). Therefore, we assessed patterns in survival from birth until young adulthood (ie, 35 years of age) and associated factors among a population-based sample of US individuals with CHDs.

Methods: Individuals born between 1980 and 1997 with CHDs identified in 3 US birth defect surveillance systems were linked to death records through 2015 to identify those deceased and the year of their death.

Racial differences in COVID-19 vaccine acceptance in Arkansas.

Vaccines are one of the most successful tools for protecting the public's health. However, widespread vaccine hesitancy in the Southern United States is preventing effective mitigation of the current COVID-19 pandemic. The purpose of this study was to assess COVID-19 vaccine acceptance among adults living in a largely rural Southern state.

Cost-benefit analysis of home visiting to reduce infant mortality among preterm infants.

Purpose: The Following Baby Back Home (FBBH) visiting program, which is provided by nurse and social worker teams, supports families of low-birthweight preterm infants after discharge from a neonatal intensive care unit. Enrollment in the FBBH program has been documented to reduce the likelihood of infant death. In this study, we conducted a cost-benefit analysis of the FBBH program.

Right Iliac Fossa Pain other than Appendicitis: A Pictorial Review.

Right iliac fossa pain is a common presentation. Although appendicitis is the most common surgical emergency, many other pathologies can have similar presentations and should be considered. This review describes the findings and shows examples of conditions other than appendicitis that should be examined for in a patient who presents with right iliac fossa pain, particularly if the appendix is not seen or seen to be normal.

Cardiology Care and Loss to Follow-Up Among Adults With Congenital Heart Defects in CH STRONG.

Many of the estimated 1.4 million adults with congenital heart defects (CHDs) in the United States are lost to follow-up (LTF) despite recommendations for ongoing cardiology care. Using 2016 to 2019 CH STRONG (Congenital Heart Survey To Recognize Outcomes, Needs, and well-beinG) data, we describe cardiac care among community-based adults with CHD, born in 1980 to 1997, identified through state birth defects registries.