Search AMIA.org

Large language models (LLMs) offer potential in natural language processing tasks in healthcare. Due to the need for high accuracy, understanding their limitations is essential. The purpose of this study was to evaluate the performance of LLMs in classifying radiology reports for the presence of pulmonary embolism (PE) under various conditions, including different prompt designs and data perturbations.

Clinical informatics (CI) is a dynamic field at the intersection of health care, technology, and information science, aimed at enhancing health care delivery and patient outcomes. CI fellowship programs prepare physicians to lead and apply health information technologies that drive improvements in clinical care, system efficiency, and innovation. Achieving Accreditation Council for Graduate Medical Education (ACGME) accreditation for such programs is crucial for standardizing training and ensuring the development of [...]

Emergency department (ED) handoff to inpatient teams is a potential source of error. Generative artificial intelligence (AI) has shown promise in succinctly summarizing large quantities of clinical data and may help improve ED handoff.Our objectives were to: (1) evaluate the accuracy, clinical utility, and safety of AI-generated ED-to-inpatient handoff summaries; (2) identify patient and visit characteristics influencing summary effectiveness; and (3) characterize potential error patterns to inform implementation strategies.This exploratory [...]

Patient mobility during hospitalization is essential for high-quality healthcare as mobility is linked to physical function and quality of life. The Johns Hopkins Highest Level of Mobility (JH-HLM) scale is a validated method to assess mobility in hospitalized patients. Although the JH-HLM is widely utilized, it has limitations including ceiling effects, unobserved mobility events going unrecorded, and the staff time needed to observe and document.We explored the feasibility of using [...]

Copy-paste (CP) and copy-forward (CF) are common electronic health record (EHR) documentation tools that purportedly improve provider efficiency, but they can also contribute to documentation burden while increasing note bloat and errors. Our understanding of provider perceptions of these tools remains limited.This study aimed to increase understanding of provider perceptions and self-reported usage patterns of CP and CF across different clinical environments and provider roles, including the impact of these [...]

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, with up to 70% of cases remaining undiagnosed. This paper proposes a COPD screening tool based on time-frequency representation features of self-recorded respiratory sounds.

This case series aims to evaluate several applications of inline disappearing text (DT) clinical decision support (CDS) tools within clinician documentation.DT blocks were created to prompt documentation for perioperative anticoagulation planning (scenario 1), predischarge intravenous antibiotic planning (scenario 2), and advanced care planning (ACP; scenario 3). In scenario 1, DT was the only intervention. In scenario 2, DT was paired with a documentation SmartList. In scenario 3, DT was paired [...]

Clinicians' occupational fatigue can lead to suboptimal decisions that do not align with evidence-based guidelines or patient needs.Examining the effects of occupational fatigue on (1) clinical decision making and (2) the use of clinical decision support (CDS) in a pediatric emergency department (ED).We interviewed 30 pediatric ED clinicians from a single site. Clinicians included physicians and advanced practice practitioners. The interviews were semi-structured and guided by the dual-processing model. Data [...]

School violence risk prevention in the United States relies on manual assessments that are time-consuming and subjective. We developed a machine learning algorithm named Automated RIsk Assessment (ARIA), using natural language processing (NLP) to find linguistic patterns in standardized interview questions that can predict risk of aggression. Our goal was to evaluate the incremental change in performance with the addition of each question to simulate situations where interviews cannot be [...]

Measurement-based care (MBC) improves clinical decision-making but remains underutilized in the Veterans Health Administration due to barriers such as provider awareness, time constraints, and user-experience limitations. This study describes the development of the War Related Illness and Injury Study Center Veteran Dashboard (VetDash), a patient-level clinical dashboard designed to integrate the VA's Collect, Share, Act model into the provider workflow.