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Federated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have [...]

This study aimed to leverage a large language model (LLM) to improve the efficiency and thoroughness of medical record documentation. This study focused on aiding clinical staff in creating structured summaries with the help of an LLM and assessing the quality of these artificial intelligence (AI)-proposed records in comparison to those produced by doctors.This strategy involved assembling a team of specialists, including data engineers, physicians, and medical information experts, to [...]

Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive, domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis of social media data.

Time spent in the electronic health record (EHR) is an important measure of clinical activity. Vendor-derived EHR use metrics may not correspond to actual EHR experience. Raw EHR audit logs enable customized EHR use metrics, but translating discrete timestamps to time intervals is challenging. There are insufficient data available to quantify inactivity between audit log timestamps.This study aimed to develop and validate a computer vision-based model that (1) classifies EHR [...]

Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.

Negative descriptors in electronic health records (EHR) contribute to worse health outcomes; studies show they are also more prevalent in EHRs of women and racial minorities and affect downstream research biases. Similar and unique patterns of negative descriptors may also exist in the records of blind patients, including those with diabetic retinopathy. Diabetic retinopathy is a preventable but leading cause of blindness in the US that is disproportionally high among [...]

Patient portals are increasingly used to support digital health engagement, but little is known about how caregivers used patient portals before, during, and after the coronavirus disease 2019 (COVID-19) pandemic.This study aimed to examine longitudinal changes in caregiver engagement with pediatric patient portals, focusing on logins, session duration, messaging behaviors, and provider response times across prepandemic, pandemic, and postpandemic periods.We conducted a retrospective cohort study using deidentified MyChart data from [...]

To develop a data harmonization framework for neonatal hypoxic-ischemic encephalopathy (HIE) studies and demonstrate its suitability for prognostic biomarker development.

The timely administration of postoperative antibiotics is crucial for preventing surgical site infections. Despite surgical ordering workflows designed to facilitate care across settings, delays in antibiotic administration posttransfer to the pediatric intensive care unit (PICU) were identified. We aimed to develop a clinical decision support (CDS) system to enhance timely order activation in a large pediatric health system. We hypothesized that the time to release signed and held orders by [...]

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