We are designing an intelligent computer vision system for remote monitoring, assessment, and support of hand hygiene in hospital environments. By improving hand-wishing quality and compliance, we hope to reduce the rate of hospital-acquired infections due to contaminated equipment, bed linens, or improper patient handling.
We are investigating the use of multiple sensors for the detection, measurement, and evaluation of hand hygiene in controlled laboratory environments, hospital corridors, and patient bedroom units. Our goal is to automatically detect missed hand hygiene events and intervene in real-time to prevent potentially contaminating events. This can include physical contact with patients, handling of biologically hazardous materials, or insufficient hand washing quality.
Our sensors are deployed at two major healthcare partners: Intermountain Healthcare and Lucile Packard Children's Hospital (LPCH) at Stanford. There is continuous active research exploring computer vision technologies and clinical outcome improvement. Our findings have been published in both medical and machine learning venues.
If your hospital organization would like to join this groundbreaking collaboration, we welcome any questions and are happy to facilitate discussion. Contact information is below.
We have partnered with Intermountain's Healthcare Transformation Lab where we have deployed 3D depth sensors in several patient rooms. With the help of Intermountain, we are using live data streams to teach our computer vision algorithms to discern events of clinical relevance such as hand hygiene events and patient interaction.
In collaboration with Lucile Packard Children's Hospital, we have installed state-of-the-art sensors in over 10 patient rooms and multiple corridors for hand hygiene activities. Our machine learning algorithms learn routine movement patterns by staff and individual hand hygiene behaviors by guests.
Vision-Based Hand Hygiene Monitoring in Hospitals
American Medical Informatics Association (AMIA) Annual Symposium