Search AMIA.org

Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for [...]

The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.

Determine if an electronic documentation tool can reduce documentation queries for malnutrition without impacting diagnostic coding.

This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research.

Telemedicine for specialty medical care is evolving from a COVID-19 pandemic-era requirement to an option for patients and clinicians alike, requiring evidence to guide optimal use of virtual specialty care. Heterogeneity across medical specialties complicates this evidence generation. To address this gap in the literature, we present an approach to evaluation of telehealth across specialties with the potential to generate findings generalizable across specialties and health systems.

Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately identifies SVP patients using electronic health record (EHR) data.

We survey clinical document corpora, with a focus on German textual data. Due to rigid data privacy legislation in Germany, these resources, with only few exceptions, are stored in protected clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing, where easy accessibility and reuse of (textual) data collections are common practice. Hence, alternative corpus designs [...]

This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.

The objective of the study was to determine, after medication review, the patient risk score threshold that would distinguish between stays with prescriptions triggering pharmacist intervention (PI) and stays with prescriptions not triggering PI.

Electronic patient portals (PP) allow for targeted and efficient research recruitment. We assessed pre- and postnatal women's recruitment methods preferences, focusing on PP.