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Development of an In-House Assistant Application to Reduce Preparation Time of Preoperative Informed Consent Forms
Masaki Takekoshi1, Kunihiro Mitsuzawa1, Takashi Ishida1
1Department of Anesthesiology and Resuscitology, Shinshu University School of Medicine, Matsumoto, JPN.
Cureus
|July 23, 2026
Summary
A new AI tool significantly cut anesthesia informed consent form preparation time by 40%. This application streamlines documentation, reducing workload and improving efficiency for anesthesiologists.
Area of Science:
- Anesthesiology
- Medical Informatics
- Artificial Intelligence
Background:
- Documentation tasks, such as anesthesia informed consent (IC) form preparation, contribute to anesthesiologists' workload and cognitive burden.
- Streamlining these administrative tasks is crucial for improving clinical efficiency and reducing burnout.
Purpose of the Study:
- To evaluate the time-saving effect and usability of a semi-automated, institution-specific assistant application for anesthesia IC form preparation.
- To assess the potential of generative AI in developing tailored clinical workflow support tools.
Main Methods:
- A semi-automated Python-based assistant application utilizing generative AI was developed and integrated with institutional electronic medical records (EMR).
- A randomized crossover study involving 20 anesthesiologists compared preparation times and usability between manual IC form entry and the AI application.
- Preparation time was measured, and usability was assessed using a 5-point Likert scale.
Main Results:
- The AI application reduced mean preparation time from 16.0 minutes to 9.7 minutes, a saving of approximately 40% (p < 0.001).
- The application was well-accepted, with 55% of participants strongly desiring continued use and 45% wanting to continue use.
Conclusions:
- The developed AI-assisted application significantly decreases the time required for anesthesia IC form preparation.
- This study demonstrates the feasibility and effectiveness of in-house generative AI development for creating institution-specific tools to enhance documentation efficiency in anesthesiology.
