Clinical and economic impact of a large language model in perioperative medicine: a randomized crossover trial

Abstract

Preoperative assessment is a critical but time-consuming component of perioperative care, often hindered by poor guideline adherence and high documentation burdens. This study evaluates the impact of PEACH (PErioperative AI CHatbot), an LLM-based clinical decision support system, on documentation efficiency, quality, user acceptance, and cost-effectiveness in preoperative consultations. PEACH did not significantly reduce overall documentation time in this randomized crossover trial involving resident physicians at Singapore General Hospital. However, subgroup analyses showed time savings for moderate-complexity patients (5.77 min, p = 0.010) and experienced physicians (4.6 min, p = 0.040). Evaluators preferred PEACH-assisted documentation in 57.1% of cases, with improved inclusion of issue lists (p = 0.05). Economic modeling projected annual institutional savings of SGD197,501 (USD146,297), with sensitivity analyses ranging from SGD 48,979 to 197,499 (USD36,280 to 146,295). These findings suggest that LLM-based tools like PEACH may enhance preoperative documentation efficiency and offer economic value.

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