ArticleCureus2026
Development of an In-House Assistant Application to Reduce Preparation Time of Preoperative Informed Consent Forms for Anesthesia.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction Documentation-related tasks, including preparation of informed consent (IC) forms for anesthesia, increase workload and cognitive burden in anesthesiology. This study evaluated the time-saving effect and usability of a semi-automated institution-specific assistant application for anesthesia IC form preparation. Methods We developed a semi-automated Python-based (Python Software Foundation, Wilmington, DE, USA) assistant application using generative AI assistant programming. The application was tailored to our institutional electronic medical record (EMR) workflow and was designed to automatically select relevant checkboxes and insert required text into IC forms for anesthesia. Its time-saving effect and usability were evaluated using a randomized crossover design. Twenty anesthesiologists were randomized into two groups: Group A (n = 10, 50%) prepared IC forms for five mock patients first by manual entry and then using the application, whereas Group B (n = 10, 50%) followed the reverse order. Preparation time was defined as the interval from the start of preparing the first IC form to the completion of the fifth IC form. Usability was assessed using a 5-point Likert scale. Results The mean preparation time was 9.7 (SD 1.7) minutes with the application and 16.0 (SD 2.6) minutes without the application (mean difference, 6.2 minutes; 95% CI, 5.2 to 7.2 minutes; p < 0.001). Use of the application reduced preparation time by approximately 40%. Regarding usability, 11 participants (55%) reported that they "very much wanted to continue using" the application, whereas nine participants (45%) reported that they "wanted to continue using" it. Conclusion The tailored application significantly reduced preparation time compared with manual entry and was well accepted by participants. These findings provide proof-of-concept evidence that generative AI-assisted in-house development may enable clinicians to create institution-specific workflow-support tools for improving documentation efficiency in anesthesiology practice.
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