Journal of hospital medicine最新文献

Background: Delayed diagnosis of venous thromboembolism (VTE) is prevalent among hospitalized patients, yet case identification is challenging and feedback limited.

Objective: To develop a large language model (LLM)-based electronic-trigger to identify VTE diagnostic delays.

Methods: All admissions to internal medicine (IM) residents at NYU Langone Health between January 2022 and December 2023 (n = 20,843) were included. Using an open-source LLM, prompts were validated to detect (1) residents considering VTE in admission notes and (2) VTE confirmation in five types of imaging reports (n = 100 for each prompt validation set). The validated prompts were applied to determine discordance between admission note differential omitting VTE and imaging report confirming VTE. Two hospitalists reviewed discordant cases using a validated tool to identify diagnostic delays. Hospitalizations were labeled as diagnostic delays, in-hospital complication, or false-positive. Based on in-hospital complication and false-positive patterns, exclusion criteria were implemented. Positive predictive value (PPV) and negative predictive value (NPV) were calculated.

Results: The LLM prompts correctly classified admission notes and VTE imaging studies with high accuracy (range 98%-100%, n = 699 VTE cases identified). Of the 137 diagnostic delays the LLM-based electronic-trigger identified, 31 were true-positives, 60 in-hospital complications, and 46 false-positives. 4.4% of all VTE hospitalizations had a diagnostic delay. With the exclusion criteria, the PPV was 48% (95% confidence interval [CI], 35%-62%) and NPV was 95% (95% CI, 87%-98%).

Conclusions: We developed the first LLM-based electronic-trigger to identify VTE diagnostic delays, with higher performance than existing non-LLM electronic-triggers. LLM-based approaches can facilitate diagnostic performance feedback and are scalable to other conditions and institutions.

Logic models are an accessible tool to organize the planning, management, and evaluation of programs across healthcare disciplines. This methodological progress note describes the five standard components of a logic model (Inputs, Activities, Outputs, Outcomes, and Impact) and provides guidance for developing a logic model. Logic models offer a visual representation of the work and theory of a program and should be created with input from all interested parties. Logic models are living tools that need to be revised as programs evolve and new information becomes available.

Background: Hospital discharge summaries are critical for ensuring continuity of care, but their quality often varies. Large language models (LLMs) have the potential to standardize and enhance the efficiency of this documentation process.

Objectives: To evaluate the quality of hospital discharge summaries created by an LLM-based hospital course drafting tool created by Epic Systems compared with human-written summaries.

Methods: Retrospective study at a single tertiary-care institution in 2024. The cohort included 100 adult hospitalizations lasting >72 hours across medical and surgical dismissing services. No interventions were performed. Summaries (LLM-generated vs human-written) were independently reviewed using a standardized rubric covering nine domains (e.g., comprehensiveness, clarity, relevance). Scores were normalized and compared. Readability was assessed using Flesch Reading Ease.

Results: LLM-generated summaries outperformed human-written summaries across all criteria (p < .05), with the greatest difference observed in comprehensiveness (LLM median 0.62 vs. human -0.23). Human-written summaries from surgical services scored lower than those from medical services, but LLM performance was consistent across both. Human summaries had higher Flesch Reading Ease scores (33.11 vs. 26.2; p < .05), reflecting simpler language.

Conclusions: LLM-generated summaries demonstrated superior quality, consistency, and clinical utility compared with human-written summaries, highlighting their potential to improve documentation efficiency and standardization.

Hospitalizations can offer key opportunities to provide focused pediatric-to-adult healthcare transition planning for adolescents and young adults with childhood-onset chronic conditions. Here, we describe the components of our standardized, comprehensive inpatient transition consultation. We review pilot data and discuss formal implementation of the consultation in an academic hospital setting.

Despite a decline in hepatitis A virus (HAV) incidence following vaccine introduction, HAV remains a public health concern in the United States. Recent multi-state outbreaks have demonstrated that HAV can re-emerge and highlight inequities in vaccine coverage or access, outbreak response, and in those with social or health risk factors. Continued investment in prevention, particularly vaccination and surveillance, is essential to prevent resurgence. Hospitalization offers a critical opportunity to address these gaps and provide equitable protection for at-risk populations.