ArticleNPJ digital medicine2025
Clinical and economic impact of a large language model in perioperative medicine: a randomized crossover trial.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- The VALUE of AI-Guided Communication: Enhancing Shared Decision-Making in Metabolic Bariatric Surgery Consultations Through a Metacognitive Framework.Obesity surgery · 2026Trial
- Applying Large Language Models in Perioperative Medicine With an Equity-Informed Perspective.Anesthesia and analgesia · 2026Article
- Perioperative Risk Stratification with AI-Powered Chatbots: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- AI-induced never-skilling in medical education.Nature medicine · 2026Review
- Conversational artificial intelligence in the operating room: an exploratory cross-sectional single-center survey among anesthesiology residents.BMC anesthesiology · 2026Observational
- Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020-2025).Journal of clinical monitoring and computing · 2026Article
- Personalised health plan development using agentic AI in Singapore's national preventive care programme: a pilot study.NPJ digital medicine · 2026Article
- Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.