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To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tuned with Low-Rank Adapters (LoRA) on intensive care units (ICU) physician notes, and to determine whether specialty-specific training enhances treatment recommendation accuracy compared to other ICU specialties or zero-shot baselines.

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

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 [...]

Missed diagnosis of genetic conditions is a persistent challenge in clinical care, particularly for familial hypercholesterolemia (FH), hereditary breast and ovarian cancer (HBOC), and Lynch syndrome-conditions designated by the U.S. Centers for Disease Control and Prevention (CDC) as Tier 1 genomic applications. This scoping review summarizes evidence on the use of electronic health record (EHR)-based algorithms to identify individuals with these conditions.