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Early recognition of critical deterioration events (CDEs) in pediatric intensive care units (PICUs) is crucial to improving outcomes, but the real-time performance of machine-learning (ML)-based alert systems is rarely assessed. We prospectively evaluated the performance, operational utility, and deployment feasibility of the PICU Warning Index (P-WIN), a personalized ML alert system predicting CDE in the PICU.

Patient-directed SMART on FHIR lets registries acquire longitudinal electronic health record data, but the payload requires substantial engineering before use. We present Registry Forge, an open-source pipeline that converts it into curation-ready outputs.

Digital mental health interventions could improve population health. However, our understanding of how to improve their effectiveness through personalization is limited. We describe the outcomes of a micro-randomized trial (MRT) of a 60-day text messaging program, delivered during the final stages of the COVID-19 pandemic. We analyze the effects of 6 different types of cognitive behavioral therapy (CBT) and dialectical behavioral therapy (DBT) messages, and examine whether socio-demographic, health-related, and [...]

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.