Virtual Coaching and the Reduction of Radial Head Subluxation: A Case Series.

OBJECTIVES: After the establishment of the virtual pediatric emergency medicine clinic at our institution, we noted that several physicians independently began to instruct caregivers virtually on reducing a radial head subluxation. We thus conducted a case series to investigate the number, success, and follow-ups for the virtual reduction of radial head subluxation. METHODS: The electronic medical records at our institution were searched from the inception of the virtual clinic in May 2020 until August 2022 (inclusive), for visits and discharge diagnosis containing the word "elbow" or "arm." RESULTS: Fourteen charts were retrieved; however, 2 were excluded because they were not a suspected radial head subluxation. A virtual reduction was attempted for eight (66.7%) of the 12 patients. In 6 of 8 patients (75.0%), the reduction was deemed successful, and for 2 patients (25.0%), it was deemed unsuccessful. Of the latter, one was found to have a nondisplaced radial neck fracture. All 4 patients (33.3%) for whom a virtual reduction was not attempted were referred to the emergency department. CONCLUSIONS: Virtual video coaching of pulled elbow reduction was completed at our institution with overall good success rate. All the physicians involved noted the essential need and benefits of video conferencing for successfully reducing radial head subluxation. We note that a pediatric population may be more amenable to video-based appointments than other populations due to their caregivers' familiarity with digital technology. Finally, as nonphysician models of healthcare delivery for virtual urgent care visits expand, we propose a checklist based on our experience to ensure patient safety.

The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.

BACKGROUND: The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. OBJECTIVE: We aimed to conduct a scoping review of the literature to address the question, "What are the current and potential impacts of AI technologies on health equity in oncology?" METHODS: Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. RESULTS: Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. CONCLUSIONS: Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

Scale for retrospective assessment of immediate concussion symptoms.

The retrospective diagnosis of concussion is often missed by clinicians. We present a brief scale for retrospective assessment of the immediate concussion symptoms (ICS) to facilitate the diagnosis of patients without visible head injury or full loss of consciousness. We administered the scale to 90 survivors of car accidents (mean age 42.0, SD=13.6; 33 males, 57 females) at 2 to 33 months after their accident. Our scale consists of 6 items and these were endorsed by the following % of our respondents: feeling dazed (64.4% of our 90 respondents), stunned (73.3%), confused (70.0%), disoriented (62.2%), dizzy (57.8%), and loss of consciousness (22.2%). The statistical properties of the scale are satisfactory (Cronbach alpha = 0.74). The scale correlates with post-accident insomnia (r=0.28), depression (r=0.29), and also with Rivermead measure of the chronic post-concussion syndrome (r=0.34). The ICS scale could be used as a starting point in longitudinal research with brain imaging procedures to evaluate the stages of recovery from the initial concussion. Attached are the English, Spanish, French, German, Italian, Russian, and Czech versions of our scale.