Search AMIA.org

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 [...]

Demonstrate the ability to encapsulate clinical-grade genomics data normalization algorithms within a FHIR Genomics reference implementation.

Overuse and misuse of antibiotics is an urgent health care problem and one of the key factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness in guiding providers to an appropriate diagnosis and identifying when antibiotics are the recommended choice for treatment.We aimed to study the relative ability of registered nurses using clinical prediction rules to guide the management of acute respiratory infections in a simulated environment compared [...]

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 [...]

To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children.

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

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.

Type 2 diabetes (T2D) is a growing public health burden with persistent racial and ethnic disparities. . This study assessed the completeness of social determinants of health (SdoH) data for patients with T2D in Epic Cosmos, a nationwide, cross-institutional electronic health recors (EHR) database.

Assess time impact and provider perception of AI-generated encounter summaries.

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 [...]