Search AMIA.org

To assess what practice-, provider-, and patient population-level predictors predict adoption of an ADHD ehealth technology in community pediatric settings, pediatric providers nationwide were recruited and offered free use of an evidence-based mental-health-focused ehealth quality improvement intervention (mehealth for ADHD). Practice-, provider-, and patient population-level factors predicting provider's adoption of the intervention were studied. We hypothesized that providers who were younger, nearing re-credentialing, having more patients with ADHD, working at [...]

Electronic health record (EHR) systems are essential for modern healthcare but contribute to significant documentation burden, affecting physician workflow and well-being. While previous studies have identified differences in EHR usage across demographics, systematic methods for identifying high-burden physician groups remain limited. This study applies cluster analysis to uncover distinct EHR usage profiles and provide a framework to inform the development of targeted interventions.

Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs.

This pilot study aimed to evaluate the impact of an ambient listening AI tool, DAX CoPilot (DAX), on clinical documentation efficiency among primary care providers in a community-based setting.

To examine user actions within a clinical decision support (CDS) alert addressing hypertension (HTN) in chronic kidney disease (CKD).

Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

To validate a phenotyping algorithm for gradations of diverticular disease severity and investigate relationships between unmet social needs and disease severity.

Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories.

While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

Although biomedical informatics has multiple roles to play in addressing the climate crisis, collaborative action and research agendas have yet to be developed. As a first step, AMIA's new Climate, Health, and Informatics Working Group held a mini-summit entitled Climate and health: How can informatics help? during the AMIA 2023 Fall Symposium to define an initial set of areas of interest and begin mobilizing informaticians to confront the urgent challenges [...]