Development and Evaluation of a Custom GPT-Based Artificial Intelligence Clinical Decision Support System for...

This study aimed to develop a custom GPT-based, knowledge-guided documentation support system that drafts emergency department (ED) interdepartmental consultation requests from minimal free-text input and evaluate its feasibility, usability, and workflow integration.

The system generates a patient summary, recommended consulting department, structured consultation draft, and clinical reference notes from brief natural language input, including age, chief concern, and key findings. Two public AI Hub datasets, the Essential Medical Knowledge Dataset (Dataset No. 71875) and the Specialized Medical Knowledge Dataset (Dataset No. 71874), were used to guide prompt construction and constrain output structure rather than for model training. Department recommendation concordance was assessed using 22 de-identified ED consultation scenarios purposively selected across multiple specialties. The documented department was removed from the input and compared with the system's recommendation. A post-use survey of 10 emergency physicians and residents assessed workflow utility, clinical appropriateness, usability, reflection of relevant information, perceived patient safety, adoption intention, and editing requirements using 5-point Likert scales.

In the 22-case pilot set, the system's recommended department aligned with the historically documented department in all cases under controlled conditions. Given the small sample size and study design, this finding represents preliminary evidence of workflow alignment rather than definitive validation of clinical performance.

The proposed system demonstrates the feasibility of large language modelenabled documentation support for generating standardized ED consultation requests, potentially reducing documentation burden. The system is intended to support, not replace, clinical decision-making, and clinician review remains essential. With further validation, this approach may be adaptable to other clinical referral contexts.