Search AMIA.org

To compare lightweight open-source large language models (LLMs) with cTAKES, a state-of-the-art natural language processing (NLP) system, in an information extraction task from hospitalization discharge summaries.

Despite low-level evidence, acutely ill patients are often continuously monitored. This creates high false alarm rates and alarm fatigue with unclear clinical effectiveness. We compare metrics, including alarm burden, area under the receiver operator characteristic curve (auROC), sensitivity, and specificity for threshold, score (i.e., National Early Warning Score [NEWS]), and machine learning (ML) alarms.We retrospectively annotated continuous biometric data for acutely ill patients receiving hospital care at home for clinical [...]

Deviation from evidence-based guidelines is common and associated with worse patient outcomes, especially in hectic emergency departments (EDs). Clinical decision support (CDS) systems can improve outcomes by promoting guideline adherence while allowing patient-specific adaptation. Implementation of a CDS system for pneumonia ("ePneumonia") in the ED has been associated with improved guideline adherence and reduced 30-day mortality. However, adoption of ePneumonia has been hindered by a suboptimal user interface (UI).This study [...]

Ambient listening tools utilize generative artificial intelligence (AI) to create clinical notes from real-time conversations between clinicians and patients during an encounter. One of the potential benefits of ambient listening tools is an improvement in reported patient experience.This study aimed to compare the patient experience of an outpatient visit during which an ambient listening tool is used with a standard visit and to quantify any perceived improvements in care.Patients completed [...]

Learning about critically ill children's outcomes after transfer to the pediatric intensive care unit (PICU) can help emergency department (ED) physicians improve future performance. However, there are no standard processes in place to systematically provide this information; thus, most ED physicians obtain inconsistent feedback.We aimed to determine the effect of delivering patient outcome feedback through the electronic health record (EHR) on the frequency of ED physicians' re-access of patients' EHRs [...]

This study aimed to compare two ambient AI documentation tools, Dragon Ambient eXperience (DAX) and Abridge in the emergency department (ED), assessing perceived effects on work burden, usability, documentation quality, satisfaction, and overall preference.We conducted a single-site, prospective crossover study in an ED over 6 weeks, from April to June 2025. Out of 20 faculty enrolled, 18 completed both phases. Participants used both ambient AI scribe tools in alternating 3-week [...]

Real-world data from vendor-aggregated health information exchanges represent a powerful resource for retrospective clinical research. Epic's Cosmos platform, a large-scale centralized data warehouse, enables querying deidentified electronic health record data.We describe the iterative development of a generalizable clinical informatics workflow for retrospective studies using Cosmos.We applied the Plan-Do-Study-Act cycle to iteratively refine a collaborative research process using Cosmo's SlicerDicer interface, focusing on setting, team, technical preparation, and workflow.We identified multiple [...]

Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available.