In early 2020, before COVID-19 vaccines and effective treatments were widely available, universal mask wearing was a key strategy to prevent the transmission of COVID-19. But hospitals and other environments with mask mandates have faced a challenge. Reminding patients, visitors and staff to wear masks had to be done manually, which was time consuming and labor intensive. Researchers from Brigham and Women’s Hospital (BWH), a founding member of the Mass General Brigham Health Care System, and the Massachusetts Institute of Technology (MIT) set out to test a tool to automate medication adherence monitoring and reminders. mask using a computer vision algorithm. The team conducted a pilot study among hospital staff who volunteered to participate and found that the technology worked effectively, with most participants reporting a positive experience interacting with the system at the hospital entrance. The results of the study are published in BMJ open.
“Changing a behavior, such as wearing a mask, requires a lot of effort, even among healthcare professionals,” said lead author Peter Chai, MD, MMS, of the Department of Emergency Medicine. “Our study suggests that a computer visualization system like this could be useful the next time there is a respiratory viral pandemic, for which masking is an important strategy in a hospital environment to control the spread of infection.”
“We recognize the challenges of ensuring appropriate mask use and the potential barriers associated with staff reporting misuse of the mask by colleagues and describe here an alternative based on computer vision and computer-based assessment. our colleagues of the initial acceptance of the platform,” the manager said the author. C. Giovanni Traverso, MB, BChir, PhD, from the Department of Medicine at BWH and the Department of Mechanical Engineering at MIT.
For the study, the team used a computer vision program developed using low-resolution CCTV still images to detect mask use. Between April 26, 2020 and April 30, 2020, researchers invited employees who entered one of the hospital’s main entrances to participate in an observational study testing the computer vision model. The team recruited 111 participants who interacted with the system and were asked about their experience.
The computer visualization system accurately detected the presence of mesh adhesion 100% of the time. Most participants – 87% – reported a positive experience interacting with the system at the hospital.
The pilot project was limited to staff at a single hospital and may not be generalizable to other settings. In addition, behaviors and attitudes toward masking have changed throughout the pandemic and may vary across the United States. Future research is needed to identify barriers to implementing computer visualization systems in healthcare facilities compared to other public institutions.
“Our data suggest that people in hospital settings are receptive to using computer visualization systems to help detect and provide reminders about effective mask wearing, particularly during a pandemic as a way to protect themselves while serving on the front lines. of a medical emergency,” Chai said. “Continued development of detection systems may provide us with a useful tool in the context of the COVID-19 pandemic or to prevent the spread of future airborne pathogens.