CHS: Medium: Physical-Virtual Patient Bed for Healthcare Training and Assessment

The University Of Central Florida Board Of Trustees, Orlando FL

Investigators

Abstract

Flight simulators offer pilots a chance to safely practice flying a wide range of aircraft in a variety of scenarios. Similarly, human patient simulators offer nurses and physicians safe opportunities to practice healthcare on a wide range of patients and scenarios. Virtual patient simulators use computer graphics to render humans with a range of visual characteristics including medical symptoms, personality, race, and gender. However, they are inherently virtual--practitioners cannot manipulate them with their hands. Manikin-based patient simulators on the other hand are inherently physical, comprising human-sized bodies with realistic skin and electro-mechanical simulation of physiological symptoms. They afford a "hands on" experience but are very limited in their ability to present visual characteristics. Furthermore, medical educators are increasingly focusing on interpersonal skills and cultural competency, as these impact provider-patient relationships, diagnoses, and treatments. Manikins do not afford the associated humanistic traits. The researchers on this project are developing a Physical-Virtual Patient Bed (PVPB) that combines the flexibility of virtual patients with the physicality of manikins. The PVPB will employ dynamic computer graphics rear-projected onto a body-shaped shell mounted in a real hospital bed, along with various sensors and actuators, to create a patient simulator that talks; appears to sweat, breathe, and squirm; exhibits a pulse; feels warm/cold on various body parts; and responds to touch by humans or medical instruments. It will be able to change race, gender, and visually-apparent symptoms on the fly, and will exhibit real human emotional complexity via real human agency. The researchers will assess the effectiveness of the PVPB in simulating certain conditions, and use it to develop new knowledge about the relative importance of various patient cues, and provider biases arising from patient demographics.