This study examined the relationship between hospital-based electronic health information exchange (HIE) and the likelihood of having a preventable emergency department (ED) visit during the COVID-19 pandemic for US patients with Alzheimer's Disease and Related Dementias (ADRD). : We used multi-level data from six states. The linked data sets included the 2020 State Emergency Department Databases (SEDD), the Area Health Resources File, the American Hospital Association (AHA) Annual Survey, and the AHA Information Technology Supplement to study 85,261 hospital discharges from patients with ADRD. Logistic regression models were produced to determine the odds of having a preventable ED visit among patients with ADRD. : Our final sample included 85,261 hospital discharges from patients with ADRD. Patients treated in hospitals that received more types of clinical information for treating patients with COVID-19 from outside providers ( = 0.961, < .05) and/or hospitals that received COVID-19 test results from more outside entities were significantly less likely to encounter preventable EDs ( = 0.964, < .05), especially among patients who also had multiple chronic conditions (MCC) ( = 0.89, = .001; = 0.856, < .001). : Our results suggest that electronic HIE may be useful for reducing preventable ED visits during the COVID-19 pandemic for people with ADRD and ADRD alongside MCC.
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http://dx.doi.org/10.1177/23337214241244984 | DOI Listing |
Ann Intern Med
January 2025
Center for Healthcare Delivery Sciences, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (R.J.D., N.K.C., N.H., J.C.L.).
Background: The evidence informing the harms of gabapentin use are at risk of bias from comparing users with nonusers.
Objective: To describe the risk for fall-related outcomes in older adults starting treatment with gabapentin versus duloxetine.
Design: New user, active comparator study using a target trial emulation framework.
Pediatr Emerg Care
January 2025
University of California Davis School of Medicine, Sacramento, CA.
Objective: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
January 2025
Department of Neurosurgery, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan.
Background: Cases of congenital disorders of glycosylation (CDGs) are rare, and the occurrence of hemorrhagic infarction is also rare. The etiology is unclear.
Observations: A 3-year-old Asian boy with CDG type 1A was hospitalized with pneumonia.
J Med Internet Res
January 2025
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
Crit Care Med
January 2025
Department of Intensive Care, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.
Objectives: Randomized clinical trials informing clinical practice (e.g., like large, pragmatic, and late-phase trials) should ideally mostly use harmonized outcomes that are important to patients, family members, clinicians, and researchers.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!