OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns.
The Journal of antimicrobial chemotherapy
May 2, 2025
INTRODUCTION: Large language models (LLMs) are becoming ubiquitous and widely implemented. LLMs could also be used for diagnosis and treatment. National antibiotic prescribing guidelines are customized and informed by local laboratory data on antimic...
Studies in health technology and informatics
Apr 8, 2025
This study explores the readiness of Dutch general practitioners (GPs) to adopt artificial intelligence (AI) technologies in primary healthcare (PHC) using an expanded Technology Acceptance Model (TAM). The model assesses perceived usefulness (PU), p...
OBJECTIVE: The objective of this study was to leverage machine learning techniques to analyze administrative claims and socioeconomic data, with the aim of identifying and interpreting the risk factors associated with high-dose opioid prescribing.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2025
BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescrip...
INTRODUCTION: Women and healthcare providers lack adequate information on medication safety during pregnancy. While resources describing fetal risk are available, information is provided in multiple locations, often with subjective assessments of ava...
OBJECTIVE: This study evaluated the characteristics of new users of sodium glucose co-transporter 2 inhibitors (SGLT2i) in clinical practice to assess the applicability of the findings from clinical trials (Empagliflozin, Cardiovascular Outcomes and ...
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2019
OBJECTIVE: Emergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We s...
OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
Journal of the American College of Radiology : JACR
Sep 1, 2019
Rapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informat...
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