Search AMIA.org

This study evaluated the usability of EzList, a Brazilian digital platform developed to optimize elective surgical waitlist management.

Many institutions have reported poor mapping of laboratory results to Logical Observation Identifiers Names and Codes (LOINC). A human in the loop machine learning approach for clinical terminology management may help.

Hospital supply rooms are dense, vertically complex environments where clinicians may lose time locating items. Existing text-based inventory logs require mental translation from alphanumeric codes to physical space, adding cognitive load during time-sensitive tasks. Despite growing use of digital twins in health care, this technology has been underreported in supply room navigation.

To support the artificial intelligence (AI) lifecycle in an integrated academic health system, we implemented a modular monitoring system to oversee electronic health record (EHR) vendor-provided clinical predictive models. This case study describes the lessons learned using this modular system to support sustainable oversight of the deployed predictive models across their lifecycle.

Admission history and physical (H&P) notes influence inpatient documentation, risk adjustment, and reimbursement. In high-acuity settings, time constraints and fragmented chart data contribute to under-documentation of comorbidities. Generative AI systems that draft admission notes from existing electronic health record data may improve documentation completeness, but real-world inpatient performance remains insufficiently characterized.

Implementing biomedical informatics (BMI) tools and applications into clinical and other settings remains a significant challenge. Unintended consequences remain common post-implementation. This paper addresses the above issue by integrating systems thinking and implementation science approaches to support health information technology (HIT) implementation.

Ohio has been severely impacted by the opioid crisis, with opioid overdose (OD) death rates exceeding national averages. Accurate OD death prediction supports proactive prevention and treatment allocation. Existing methods often focus on ZIP Code Tabulation Area (ZCTA)-level prediction for small-area resource allocation; however, performance at this resolution is poor due to substantial fluctuations in OD death counts, which introduce noise. This raises a critical methodological question: what is the [...]

This study compares multiple LLMs, including ChatGPT, DeepSeek, and Llama, to generate meaningful, audience-adapted labels for the existing latent classes among patients with chronic low back pain (cLBP).

The pandemic dramatically increased in the frequency of audiovisual medical visits and the rate of audiovisual visits remains higher than before the pandemic. These visits have the potential to be an important clinical modality. Researchers have assessed several aspects of audiovisual visits but they have not investigated their clinical note quality. The goal of this study is to determine the quality of audiovisual and in-person clinical notes and to compare [...]

This study aims to understand how inpatient nurses determine and prioritize necessary documentation within the context of the Excessive Documentation Burden (ExDocBurden) in Electronic Health Records (EHRs).