Computer vision can also analyze video content and augment patient monitoring. Healthcare professionals often need to monitor patients' activities, vital signs, and adherence to prescribed treatments. Computer vision can automate some of this process by tracking movement, analyzing facial expressions, and checking vital signs through visual cues. This reduces the burden on healthcare professionals, allowing them to focus on critical tasks and patients requiring immediate attention.

Key impacts on quality of care⁴

• Improved patient diagnosis, treatment, and monitoring
• Earlier/more accurate disease detection
• Safer care
• Reduced likelihood of patient falls
• Advanced/smart operating rooms
• Improved patient identification
• Faster medical research

Key impacts on workforce challenges

• Task automation
• Efficiency improvement
• Reduced burnout
• Better training and education

Computer vision is founded on pattern recognition. Humans can train a computer how to understand visual data by feeding it millions of images or video content, then subjecting them to various software techniques or algorithms that allow the computer to hunt down patterns in all the elements that relate to those labels.⁵

In many instances, a sensing device — often a camera — will capture an image or video and send it to an interpreting device. The device uses pattern recognition to compare against its library of known patterns. A user can then ask for information about the image or video from the device.

Most computer vision applications involve trying to recognize things in images or videos. There is a lot of variability in visual data, and it can often be difficult to interpret the semantic content of images or videos, such as object recognition, scene understanding, and context. The three main capabilities of computer vision include:

1. Object classification: Classifies objects according to a defined category.
2. Object identification: Identifies a particular object in a photo, video, or image.
3. Object tracking: Analyzes a video to process the location of a moving object.

For computer vision in healthcare to be successful and have a tangible impact, several key components and considerations must be in place beyond the technology itself:

• Reliable alert systems: Solutions should provide timely and accurate alerts to make sure staff immediately act on critical findings. Timeliness and accuracy of alerts will help reduce alert fatigue, which can occur if staff are overwhelmed by frequent or false alarms.
• Clear protocols and guidelines: There should be established protocols for how to respond to several types of alerts or findings from the computer vision system. Staff should know the steps to take, who to notify, and how to document actions taken in response to system outputs.
• Integration with clinical workflows: Computer vision tools must be seamlessly integrated into existing clinical workflows to ensure they support healthcare professionals without causing disruptions.
• User training and acceptance: Staff must be adequately trained not only on how to use the computer vision system but also on its capabilities and limitations.

Remember, each healthcare organization is unique, so it's essential to tailor this guidance to your specific goals. By taking a strategic and informed approach, healthcare provider executives can successfully leverage computer vision technology to enhance patient care, improve operational efficiency, and drive innovation in their organizations. Additionally, it's imperative to keep this tool under human supervision, reinforcing that computer vision complements but does not replace essential human judgment.