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DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP

Paper · 2026 · Hanlin Lv, Xiao Wang, Kesong Wu et al.

Summary

Converting free-text Chinese radiology/imaging reports into the OMOP CDM v5.4 is difficult. Rule-based tools break on abbreviations and phrasing variants, BERT-style extractors need costly expert annotation, and LLMs give inconsistent outputs or require cloud APIs that conflict with patient-privacy rules. The authors built DIMAS-OMOP, a locally deployed pipeline with three stages:

  • deterministic NLP for normalization, NER and number extraction;
  • on-demand calls to a local LLM with RAG, used only for ambiguous cases;
  • a multi-agent debate in which a proposer suggests SNOMED CT concept mappings and a skeptic agent challenges them.

An audit agent then reviews the full debate. A four-dimensional quality gate must be passed before records are written to OMOP tables. Concept lookup relies on multi-strategy search with adaptive thresholds rather than hard-coded rules. On 1,250 reports from three tertiary hospitals, the system reached 95.2% mapping accuracy at about 1,200 reports per hour, clearly beating rule-based and single-model baselines. This matters because it shows a privacy-preserving route for bringing non-English clinical narratives into the OHDSI research ecosystem.

Key points

  • Six agents are coordinated by a task orchestrator: Linguist, Structurer, Mapping Proposer (ReAct), Skeptical Critic, Synthesis Audit and OMOP Builder. Output fills the OBSERVATION, MEASUREMENT and FACT_RELATIONSHIP tables of OMOP CDM v5.4 using SNOMED CT.
  • Accuracy was 95.2%, which is +21.8 percentage points over rule-based methods and +8.1 over single-AI baselines. The deliberation stage alone gave an 8.9% relative improvement.
  • In a pilot deployment, the authors report a 160.6% ROI and 31.5% higher workflow efficiency.
  • Limitations noted by the authors:
    • a modest dataset from only three hospitals;
    • no inter-annotator agreement, held-out split details or error analysis reported;
    • usability feedback came mainly from informatics staff.
  • Code is released at github.com/raywong2121/Multi-Agent, and data is available only on request. Planned extensions cover pathology, lab and operative reports, plus Japanese and Korean.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓The University of QueenslandBrisbane · Australia14 works · 4,603 citations🎓Southeast UniversityNanjing · China28 works · 354 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksBiochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 worksMedicine · 241 worksHealth Informatics · 184 works

Cite

Cite as Hanlin Lv, Xiao Wang, Kesong Wu, Lei Li and Lei Wang. 2026. DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP. Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.mellm-1.10

@inproceedings{lv2026dimas,
  title = {{DIMAS-OMOP: A Deliberative Intelligence-Based Multi-Agent System for Chinese Medical Text Standardization toward OMOP}},
  author = {Hanlin Lv, Xiao Wang, Kesong Wu, Lei Li and Lei Wang},
  booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.mellm-1.10},
  url = {https://doi.org/10.18653/v1/2026.mellm-1.10}
}

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