An anesthesiologist turns an idea into reality with the mobile app designed to remotely track multiple patients in the operating room. Custom insights and triggers supported by AI and machine learning gave tailored insights to each patient and maximized ROI by optimizing resources and tailoring treatment for each patient.

Challenge

• Create a remote patient monitoring app designed for anesthesiologists to stay updated on patient status without a physical presence in the operating room.
• This platform had to be a plug & play out of the box. The platform had to bypass extensive EMR integration timelines and costs and yet track all the key patient vitals. Additionally, it had to have deidentified patient data.
• Finally, the technology had to have the ability to set patient-specific configurations and alert the anesthesiologist when specific patients needed attention.

What we did

• Robust machine learning, image optimization algorithm that translated on-screen graphs to data that tracked key patient vitals. This eliminated the timeline and costs to integrate with the EMR.
• Smart configurations on a mobile device allowed anesthesiologists to set patient-specific parameters.
• Alerts based on patient vitals benchmarked against set configurations allowed anesthesiologists to significantly reduce OR traffic and yet actively manage the right patients at the right time.

Outcome

• Advanced machine learning to capture, define, and structure images from patient monitors in the OR, translating patient vitals – with 99.9% accuracy – directly to a caregiver’s handheld mobile device screen.
• Significant reduction in OR traffic that is often associated with higher rates of surgical site infections.
• Significant reduction in cost and reduced implementation timelines which led to market-leading pricing and accelerated speed to value.