This project develops new technology to assist healthcare workers, thereby making healthcare safer and more cost-effective. Healthcare is 18% of the Gross Domestic Product, yet errors annually lead to as many as 250,000 deaths and more than $500 billion in costs. The project is creating virtual assistants in intensive care and surgical units, which guide nurses and other healthcare workers, using worker input and experimental results to guide technology development. These virtual assistants give step-by-step guidance and feedback during complex tasks and monitor routine activities to make sure no steps are missed. Benefits include assistance in learning new activities, reducing stress, detecting possible problems, and automating some of the work providers do now to document their ongoing activities.
The research team is developing prototypes, experimental results, and effectiveness measurements in healthcare settings for virtual assistants, called “ambient intelligent monitors†(AIMs). The team includes computer scientists, electrical engineers, clinicians, behavioral scientists, and healthcare economists at Stanford hospitals, Intermountain hospitals, and Stanford’s Schools of Engineering, Business, and Medicine. Research goals include developing sensors, computing, and software for AIMs, discovering how clinical workers prefer to communicate with AIMs, and experimenting with work environments to maximize their value to patients, families, and clinicians. The project includes three cycles of research, covering hand hygiene, Intensive Care Unit care bundles, and laparoscopic surgery. Each cycle includes analysis, design, development, implementation in normal hospital care, and evaluation. The research is proceeding along four parallel tracks. One track is designed to develop hardware for capturing privacy-protected data on edge devices such as cameras, depth sensors, and microphones. A second track is for developing algorithms that discern success and failure in enacting intended clinician bedside activity. A third track is for obtaining healthcare worker input to develop preferred ways for AIMs to interact with people, such as providing verbal prompts, confirming observations made by sensors, and automatically updating medical records. A fourth track is for running experiments to measure the clinical, behavioral, operational, and economic impacts of the AIMs. By coupling the engineering research with real-world deployment and clinical studies, this research will create new knowledge that is unattainable in a purely technology-driven or purely observational investigation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
