ArticleNEJM AI2024
Large Language Models for More Efficient Reporting of Hospital Quality Measures.
Article in NEJM AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07581340 (Impact of Automated Sepsis Metric Evaluation on Provider Performance), which is not on this map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Impact of Automated Sepsis Metric Evaluation on Provider Performance
Who cites it
26 citing papers in PubMed.
- Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence: A Cluster Randomized Trial.JAMA network open · 2026Trial
- An eyecare foundation model for clinical assistance: a randomized controlled trial.Nature medicine · 2025Trial
- A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives.Scientific reports · 2026Article
- Comparison of large language models and expert multidisciplinary team decisions in colorectal cancer.BMJ health & care informatics · 2026Article
- Scaling medical device regulatory science using large language models.NPJ digital medicine · 2026Article
- SHREC: A framework for advancing next-generation computational phenotyping with large language models.PLOS digital health · 2026Article
- LLM and CQL computable phenotypes for myocarditis adverse event feature and outcome determinations using patient FHIR data: a feasibility study.Frontiers in digital health · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- The Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era.Open forum infectious diseases · 2026Article
- Large language models in healthcare quality management: a European perspective on process automation and compliance.Frontiers in digital health · 2026Review
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- Shifts in emergency physicians' attitudes toward large language model-based documentation: a pre- and post-implementation study.Scientific reports · 2025Article
- Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Syndromic Analysis of Sepsis Cohorts Using Large Language Models.JAMA network open · 2025Article
- Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model.medRxiv : the preprint server for health sciences · 2025Article
- SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.ArXiv · 2025Article
- Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges.JMIR medical informatics · 2025Review
- Benchmark evaluation of DeepSeek large language models in clinical decision-making.Nature medicine · 2025Article
- Can we predict the future of respiratory failure prediction?Critical care (London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
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. We tested the system on a sample of 100 manual SEP-1 abstractions that University of California San Diego Health reported to the Centers for Medicare & Medicaid Services in 2022. The LLM system achieved agreement with manual abstractors on the measure category assignment in 90 of the abstractions (90%; κ=0.82; 95% confidence interval, 0.71 to 0.92). Expert review of the 10 discordant cases identified four that were mistakes introduced by manual abstraction. This pilot study suggests that LLMs using interoperable electronic health record data may perform accurate abstractions for complex quality measures. (Funded by the National Institute of Allergy and Infectious Diseases [1R42AI177108-1] and others.).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.