Search AMIA.org

To determine whether numerical and large language model embedding-based representations of the same structured electronic health record data fail on the same patients or on distinct patient groups.

Obesity is a public health crisis with few effective preventative treatments limited by challenges in identifying who is at high risk of weight gain. To address this gap, our group has developed machine learning weight gain prediction models with modest predictive power. In this study, we aimed to build upon our prior work by incorporating a multi-ancestry polygenic risk score (MAPRS) into a machine learning model and hypothesized that its [...]

To characterize patients' perspectives on privacy on the disclosure, documentation, and visibility of social risk information collected during screening in healthcare settings, and to identify ways to design patient-centered, privacy-protecting screening and documentation systems.

To evaluate performance and efficiency of a lightweight open-source reasoning large language model (LLM) against an expert-derived reference standard for screening scientific publication databases.

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 [...]

OBJECTIVES: This case report describes the implementation and outcomes of robotic process automation (RPA) interventions used as an electronic health record (EHR)-based reliability layer for two inpatient quality initiatives: First Contact Provider (FCP) assignment and Admission Medication Reconciliation (AMR) within 24 hours.

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.