Search AMIA.org

Pediatric hospital readmissions increase healthcare costs and highlight gaps in care. Social determinants of health (SDOH), such as housing and transportation insecurity, significantly impact outcomes but are underexplored in pediatric populations.

Background Clinical Informatics (CI) is a dynamic field at the intersection of healthcare, technology, and information science, aimed at enhancing healthcare delivery and patient outcomes. CI fellowship programs prepare physicians to lead and apply health information technologies that drive improvements in clinical care, system efficiency, and innovation. Achieving Accreditation Council for Graduate Medical Education (ACGME) accreditation for such programs is crucial for standardizing training and ensuring the development of a [...]

Background Emergency Department (ED) handoff to inpatient teams is a potential source of error. Generative Artificial Intelligence (AI) has shown promise in succinctly summarizing large quantities of clinical data and may help improve Emergency Department (ED) handoff. Objectives Our objectives were to: 1) evaluate the accuracy, clinical utility, and safety of AI-generated ED-to-inpatient handoff summaries; 2) identify patient and visit characteristics influencing summary effectiveness; and 3) characterize potential error patterns [...]

Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.

To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.

This case series aims to evaluate several applications of inline disappearing text (DT) clinical decision support (CDS) tools within clinician documentation.

To optimize in-context learning in biomedical natural language processing by improving example selection.

Clinicians' occupational fatigue can lead to suboptimal decisions that do not align with evidence-based guidelines or patient needs.

Electronic health records (EHRs) provide substantial resources for observational studies, yet present significant challenges in safeguarding patient privacy while maintaining research quality. Differential privacy (DP) offers a quantifiable privacy guarantee; however, its impact on observational studies remains underexplored. We empirically evaluated the effects of DP across varying values of its privacy parameter, epsilon, on case-control analysis outcomes using EHR data. This study aims to inform DP parameter selection and examines [...]

This study develops and validates the confidence-linked and uncertainty-based staged (CLUES) framework by integrating large language models (LLMs) with uncertainty quantification to assist manual chart review while ensuring reliability through a selective human review.