Large Language Models for More Efficient Reporting of Hospital Quality Measures.

Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction.

A Novel System to Improve Case Capture for Surgery Morbidity and Mortality Conference.

Introduction: Complications are often under-reported at surgical morbidity and mortality (M&M) conferences due to the sole reliance on voluntary case submission. While most institutions have databases used for targeted initiatives in quality improvement, these are not routinely used for M&M. We aimed to increase case capture for M&M conferences by developing a novel system that augments the existing case submission system with cases representing complications from quality improvement databases and the electronic health record (EHR).

On the Prognosis of Multifocal Glioblastoma: An Evaluation Incorporating Volumetric MRI.

Primary glioblastoma (GBM), IDH-wildtype, especially with multifocal appearance/growth (mGBM), is associated with very poor prognosis. Several clinical parameters have been identified to provide prognostic value in both unifocal GBM (uGBM) and mGBM, but information about the influence of radiological parameters on survival for mGBM cohorts is scarce. This study evaluated the prognostic value of several volumetric parameters derived from magnetic resonance imaging (MRI).

The ACS National Surgical Quality Improvement Program-Inflammatory Bowel Disease Collaborative: Design, Implementation, and Validation of a Disease-specific Module.

Background: Surgery for inflammatory bowel disease (IBD) involves a complex interplay between disease, surgery, and medications, exposing patients to increased risk of postoperative complications. Surgical best practices have been largely based on single-institution results and meta-analyses, with multicenter clinical data lacking. The American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) has revolutionized the way in which large-volume surgical outcomes data have been collected.