In the United States, there are an estimated 98,000 people per year killed and $17.1 billion dollars lost due to medical errors. One way to prevent these errors is to have clinical students engage in simulation-based medical education, to help move the learning curve away from the patient. This training often takes place on human-sized android robots, called high-fidelity patient simulators (HFPS), which are capable of conveying human-like physiological cues (e.g., respiration, heart rate). Training with them can include anything from diagnostic skills (e.g., recognizing sepsis, a failure that recently killed 12-year-old Rory Staunton) to procedural skills (e.g., IV insertion) to communication skills (e.g., breaking bad news). HFPS systems allow students a chance to safely make mistakes within a simulation context without harming real patients, with the goal that these skills will ultimately be transferable to real patients.
While simulator use is a step in the right direction toward safer healthcare, one major challenge and critical technology gap is that none of the commercially available HFPS systems exhibit facial expressions, gaze, or realistic mouth movements, despite the vital importance of these cues in helping providers assess and treat patients. This is a critical omission, because almost all areas of health care involve face-to-face interaction, and there is overwhelming evidence that providers who are skilled at decoding communication cues are better healthcare providers – they have improved outcomes, higher compliance, greater safety, higher satisfaction, and they experience fewer malpractice lawsuits. In fact, communication errors are the leading cause of avoidable patient harm in the US: they are the root cause of 70% of sentinel events, 75% of which lead to a patient dying.
In the Robotics, Health, and Communication (RHC) Lab at the University of Notre Dame, we are addressing this problem by leveraging our expertise in android robotics and social signal processing to design and build a new, facially expressive, interactive HFPS system. In this talk, I will discuss our efforts to date, including: in situ observational studies exploring how individuals, teams, and operators interact with existing HFPS technology; design-focused interviews with simulation center directors and educators which future HFPS systems are envisioned; and initial software prototyping efforts incorporating novel facial expression synthesis techniques.
Dr. Laurel Riek is the Clare Boothe Luce Assistant Professor of Computer Science and Engineering at the University of Notre Dame. She directs the RHC Lab, and leads research on human-robot interaction, social signal processing, facial expression synthesis, and clinical communication. She received her PhD at the University of Cambridge Computer Laboratory, and prior to that worked for eight years as a Senior Artificial Intelligence Engineer and Roboticist at MITRE.