AI Insight
Researchers conducted a randomized equivalence trial with 130 patients comparing PEAR, a multilingual AI chatbot designed to explain anesthesia risks, against standard preoperative consultations. Patients who used the AI chatbot achieved understanding scores statistically equivalent to those who received traditional face-to-face consultations with clinicians. The AI system also reduced documentation and consultation time, with projected cost savings of approximately USD 0.78 million annually at a single hospital.
Why it matters
This study demonstrates that conversational AI could help address language barriers and improve efficiency in surgical settings while maintaining patient comprehension of critical medical information. The technology could free up clinician time while ensuring patients understand procedure risks before surgery, though human oversight remains necessary for personalized decision-making.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background Informed consent depends on patients’ understanding of anaesthesia risk, yet comprehension remains poor despite routine preoperative consultation. Conversational artificial intelligence (AI) could establish patient-reported understanding before clinician contact, but whether such systems can achieve patient-reported understanding comparable to clinician-delivered education remains unknown. Methods We conducted a randomised equivalence trial (n = 130) of PEAR (Preoperative Education of Anaesthesia Risks), a multilingual retrieval-augmented conversational AI grounded in institutional consent materials, versus standard preoperative consultation in adults undergoing elective surgery. Results A total of 130 adults (mean age 52.4 +/- 14.5 years) were enrolled. Post-consultation understanding scores in the PEAR group met the pre-specified equivalence criterion compared with standard consultation across all three primary measures. Patients who interacted with PEAR before clinician contact achieved understanding scores comparable to those receiving standard face-to-face consultation alone. PEAR reduced documentation and consultation time, corresponding to a projected annual net benefit of approximately SGD 0.99 million (USD 0.78 million) at a single tertiary centre. Conclusions A retrieval-augmented conversational AI achieved patient-reported understanding of anaesthesia risk equivalent to standard preoperative consultation while substantially improving workflow efficiency. These findings support supervised deployment of conversational AI within perioperative care pathways while preserving clinician oversight for verification and patient-specific decision-making.
Source: Randomised Trial of a Multilingual Conversational AI for Preoperative Education