The consensus-based CINEX guideline for reporting clinical information extraction studies
Abstract
Objective: Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies. Material and methods: We developed the Clinical Information Extraction Reporting Guideline (CINEX) through a multi-phase process. The initiative was prospectively registered on the EQUATOR Network as a reporting guideline under development, and a detailed Delphi study protocol was published in advance. A scoping review informed an initial set of items, which was refined through a 3-round electronic Delphi study with 20 international experts, followed by a final consensus meeting. Items were iteratively refined based on predefined inclusion criteria and expert feedback. Results: The final CINEX guideline comprises 29 checklist items grouped into 5 domains: information model, architecture, data, annotation, and outcomes. The 3 eDelphi rounds included 20, 15, and 12 experts, respectively. Two items were added after round one. Consensus for inclusion was reached for a 21 and additional 7 items after rounds 2 and 3, respectively. Discussion: CINEX provides a structured framework to improve transparency, reproducibility, and interpretability in clinical IE research. By standardizing reporting of key methodological components, such as data provenance, annotation processes, and evaluation strategies, it facilitates meaningful comparison across studies and supports safer clinical implementation. Conclusion: CINEX complements existing AI reporting standards by addressing domain-specific challenges in clinical IE.
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BibTeX
@article{reichenpfader2026consensusbased,
title = {{The consensus-based CINEX guideline for reporting clinical information extraction studies}},
author = {Daniel Reichenpfader and Jamil Zaghir and Elsa Cecilia-Joseph and Boris Delange and Anastassia Shaitarova and Emmanuelle Sylvestre and Fabio Dennstädt and Boya Zhang and Douglas Teodoro and Stéphane Meystre and Henning Müller and Kerstin Denecke},
journal = {Journal of the American Medical Informatics Association},
year = {2026},
month = sep,
doi = {10.1093/jamia/ocag136},
url = {https://hal.science/hal-05753276v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The consensus-based CINEX guideline for reporting clinical information extraction studies
AU - Daniel Reichenpfader
AU - Jamil Zaghir
AU - Elsa Cecilia-Joseph
AU - Boris Delange
AU - Anastassia Shaitarova
AU - Emmanuelle Sylvestre
AU - Fabio Dennstädt
AU - Boya Zhang
AU - Douglas Teodoro
AU - Stéphane Meystre
AU - Henning Müller
AU - Kerstin Denecke
PY - 2026
DA - 2026/09/17
JO - Journal of the American Medical Informatics Association
DO - 10.1093/jamia/ocag136
UR - https://hal.science/hal-05753276v1
AB - Objective: Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies. Material and methods: We developed the Clinical Information Extraction Reporting Guideline (CINEX) through a multi-phase process. The initiative was prospectively registered on the EQUATOR Network as a reporting guideline under development, and a detailed Delphi study protocol was published in advance. A scoping review informed an initial set of items, which was refined through a 3-round electronic Delphi study with 20 international experts, followed by a final consensus meeting. Items were iteratively refined based on predefined inclusion criteria and expert feedback. Results: The final CINEX guideline comprises 29 checklist items grouped into 5 domains: information model, architecture, data, annotation, and outcomes. The 3 eDelphi rounds included 20, 15, and 12 experts, respectively. Two items were added after round one. Consensus for inclusion was reached for a 21 and additional 7 items after rounds 2 and 3, respectively. Discussion: CINEX provides a structured framework to improve transparency, reproducibility, and interpretability in clinical IE research. By standardizing reporting of key methodological components, such as data provenance, annotation processes, and evaluation strategies, it facilitates meaningful comparison across studies and supports safer clinical implementation. Conclusion: CINEX complements existing AI reporting standards by addressing domain-specific challenges in clinical IE.
SN - 1067-5027
SN - 1527-974X
ER -