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CocktailAI : Discharge Summary Generator Using Template Document and Retrieval-Augmented Generation, and Hybrid-Cloud
Tomohiro Kuroda1, Keina Sado2, Kenji Suda2
1Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Kyoto, Japan.
Studies in Health Technology and Informatics
|August 8, 2025
Summary
Kyoto University Hospital created CocktailAI, a hybrid-cloud system, to safely use artificial intelligence (AI) for health data research. This AI tool significantly reduced clinical documentation tasks by generating discharge summaries with 92% efficiency.
Area of Science:
- Health Data Science
- Artificial Intelligence in Healthcare
- Clinical Informatics
Background:
- Advancements in artificial intelligence (AI) necessitate secure access to health data for research.
- University hospital information systems must balance data safety with enabling AI tool integration.
- Clinical documentation burdens healthcare professionals, highlighting the need for efficient solutions.
Purpose of the Study:
- To develop a secure hybrid-cloud hospital information system for AI-driven health data research.
- To implement and evaluate a generative AI tool, CocktailAI, for automating clinical documentation, specifically discharge summaries.
- To mitigate potential errors in generative AI outputs, such as hallucination, using retrieval-augmented generation.
Main Methods:
- Development of a hybrid-cloud hospital information system at Kyoto University Hospital.
- Integration of retrieval-augmented generation to extract key information from inpatient records.
- Application of the CocktailAI system for generating discharge summaries for referral letters over one month.
Main Results:
- The CocktailAI system successfully initiated AI research, including a discharge summary generator.
- 56% of generated discharge summaries were published with minimal or no edits.
- An additional 36% of generated summaries required only minor additions, resulting in 92% overall task support.
Conclusions:
- The hybrid-cloud system facilitates secure AI research using clinical data.
- Retrieval-augmented generation effectively reduces generative AI errors like hallucination.
- CocktailAI significantly enhances efficiency in clinical documentation, supporting 92% of discharge summary generation tasks.
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