Peizhu Qian

Papers

Total Citations

H-Index

About

Peizhu Qian is a pioneering researcher at the intersection of human-robot interaction (HRI) and healthcare education, with a focused expertise in developing robotic tutoring systems for clinical training. Her work addresses the critical nursing labor shortage by creating intelligent, interactive robots that train nurses in high-stakes, life-critical procedures. Qian’s major contributions include the design and evaluation of ASTRID, a robotic tutor specifically engineered to reduce healthcare-associated infections by guiding nurses through complex central line dressing changes—a procedure prone to preventable errors under heavy workloads. Her research demonstrates how HRI can mitigate systemic risks in hospital settings, offering scalable, on-demand training that enhances patient safety. With foundational papers like "Robotic Tutors for Nurse Training" (2023) and "ASTRID" (2025), Qian’s work is gaining traction as a transformative approach to medical education, directly addressing the gap between theoretical knowledge and practical, error-prone clinical tasks. Her achievements highlight a novel application of robotics in healthcare, positioning her as a key voice in leveraging automation to support overburdened healthcare systems and improve patient outcomes.