Agentic artificial intelligence for code status extraction from clinical notes: a cross-model comparison of single agent, reflective agent, and multiagent extractor-reviewer system

Clicks: 20
ID: 333109
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #63 of 88 articles by views in Journal of the American Medical Informatics Association : JAMIA

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Objectives To evaluate whether increasing agentic architectural complexity via self-reflection or multiagent design improves clinical code status extraction (Do Not Resuscitate [DNR], Do Not Intubate [DNI], Comfort Measures Only [CMO]) across open-source large language models (LLMs) of varying capabilities. Materials and Methods We conducted a controlled cross-model comparison evaluating 3 architectures: (1) single extractor agent, (2) single agent with self-reflection, and (3) sequential multiagent extractor-reviewer system. Four open-source LLMs (DeepSeek-R1-Distill-LLaMA-8B, GPT-OSS-20B, MedGemma-27B-IT, Qwen3.5-27B) were evaluated on 1000 Medical Information Mart for Intensive Care IV-Note discharge summaries. Ground truth labels were established through a physician-AI dual review with adjudication (Cohen’s κ = 0.98-0.99). Models were evaluated on accuracy, precision, recall, F1-score, Cohen’s κ, token consumption, and processing latency. McNemar’s test was used for paired comparisons. Results For capable larger models (GPT-OSS-20B, MedGemma-27B-IT, Qwen3.5-27B), single-agent architectures achieved comparable or superior performance for explicit code status (DNR, DNI) relative to complex systems, whereas multiagent review doubled token consumption and latency without accuracy gains. Reflective self-review significantly impaired MedGemma-27B-IT on DNI extraction (P = .047). For the smaller DeepSeek-R1-Distill-LLaMA-8B, multiagent review provided statistically significant corrective benefits for DNR (P = .017) and CMO (P < .001), demonstrating a capability-threshold effect, though performance remained below clinically acceptable levels. Discussion Architectural complexity does not enhance extraction performance for capable LLMs, and reflective self-review may cause conservative overcorrection. Multiagent verification value is inversely related to model capability. Conclusion Single-agent LLMs provide optimal accuracy and efficiency for explicit clinical extraction in capable models. Agentic complexity must be matched to both task characteristics and model capability rather than adopted by default.
Reference Key
openalex_W7223068040 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yan Sun, Gary Ang, Chun Wei Yap, Palvannan Kannapiran, John Arputhan Abisheganaden
Are you one of these authors? Claim your profile
Journal Journal of the American Medical Informatics Association : JAMIA
Year 2026
DOI
10.1093/jamia/ocag179
URL
https://doi.org/10.1093/jamia/ocag179
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.