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The IMPACT framework for evaluating generative AI in critical care: development and multinational consensus
Yu-Chang Yeh1, Ming-Chieh Shih2, Daniel De Backer3
1Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan.
Annals of Intensive Care
|May 25, 2026
Summary
A new framework, IMPACT, has been developed to evaluate generative artificial intelligence (GenAI) clinical decision support in intensive care. This validated tool addresses critical gaps in assessing AI accuracy and urgency for better patient care.
Area of Science:
- Critical care medicine
- Artificial intelligence in healthcare
- Clinical decision support systems
Background:
- Generative artificial intelligence (GenAI) is increasingly utilized for clinical decision support in critical care settings.
- Current evaluation methods for GenAI in intensive care lack standardization and fail to assess clinical accuracy, reasoning, or urgency.
- There is a need for a robust framework to evaluate the quality and safety of GenAI tools in critical care.
Purpose of the Study:
- To develop and validate a standardized framework for evaluating generative artificial intelligence (GenAI) clinical decision support in intensive care.
- To address the limitations of existing metrics that focus on textual similarity rather than clinical utility.
- To ensure the accuracy, reasoning quality, and urgency assessment of GenAI in critical care settings.
Main Methods:
- The IMPACT framework was developed and validated through a five-phase multinational panel consensus process.
- Reporting adhered to the ACCORD guideline, with clinical and methodological oversight from a steering committee.
- Content validity was assessed using the Content Validity Ratio (CVR) and Item-level Content Validity Index (I-CVI), with specific retention thresholds.
Main Results:
- Fifty-eight panelists from 12 countries participated, including intensivists, AI researchers, and IT specialists.
- All six IMPACT domains exceeded validity thresholds, demonstrating high consensus (mean agreement 89.3%, CVR = 0.79, I-CVI = 0.92).
- Of 24 candidate subitems, 21 met retention criteria (mean agreement 85.7%, CVR = 0.71, I-CVI = 0.90), resulting in a validated framework with six domains and 21 subitems.
Conclusions:
- The IMPACT framework offers a consensus-validated approach for evaluating GenAI clinical decision support in intensive care.
- This framework addresses the critical need for standardized evaluation methods beyond simple textual similarity.
- IMPACT aims to improve the safety and efficacy of GenAI tools in critical care by ensuring clinical accuracy and appropriate urgency assessment.
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