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Health systems can be vulnerable to "shiny object syndrome," adopting unneeded emerging technologies (ETs), or may ignore "red flags" if they rely on linear health technology procurement processes that exclude the influence of information exchanges between the health system, vendors, and industry partners.

Expert clinician chart review is the most accurate method to identify patients with infection but is laborious and costly. We aimed to develop and externally validate machine-learning models using structured and unstructured data to identify infection in hospitalized patients compared to chart review.

This article summarizes the architecture, design principles, and implementation strategy of the ATRI Laboratory Information Management System (LIMS), a cloud‑native, open‑source platform supporting biomarker‑driven Alzheimer's disease (AD) clinical research. The system addresses challenges associated with managing large‑scale, multimodal biomarker, and biobanking data across multisite studies.

Polypharmacy rates continue to rise which can lead to increased risk of adverse drug events. Deprescribing is essential but time-consuming, requiring extensive clinical note and chart review. We developed RxReduce, a large language model (LLM)-powered framework as a proof of concept that can assist with medication deprescribing.

To simultaneously evaluate and compare LLM performance in clinical reasoning with a minimally necessary criteria for clinical alignment with patients.

This prospective single-arm trial assessed the use of an integrated patient platform (IPP) and effects on patient-reported outcomes survey (PROs) completion.

We aimed to develop and validate an automated, scalable framework to harmonize fragmented UK primary care prescription records into a research-ready dataset by mapping diverse medical ontologies to a unified reference standard from the UK Biobank.

Exploratory data analysis (EDA) is foundational to clinical research yet inaccessible to clinician-scientists without programming training. Existing large language model (LLM) tools improve accessibility but struggle with hallucinations. We developed and evaluated data reflector (DR), a hybrid agentic platform pairing natural language interaction with deterministic statistical computation.

Artificial intelligence (AI) can be used to measure sentiment and reading level, facilitating its use to improve nutrition communication, though AI has not yet been tested for such analysis of nutrition articles. The present study investigated this by looking at reading level and sentiment in magazines aimed at various demographics as determined with ChatGPT.

To produce the first structured longitudinal dataset of symptom severity trajectories in vagus nerve stimulation (VNS) patients using a 2-stage large language model pipeline for automated symptom severity extraction from clinical notes, as part of the NIH-funded research evaluating vagal excitations and anatomical linkages (U54AT012307).