Our Work

Remote monitoring of patient vitals in the operating room

Mobile application gives anesthesiologists the capability to monitor vital signs of multiple operating room patients, simultaneously.

Industry & practice areas

  • Digital health
  • Mobile app development
  • AI & machine learning
  • UX design
Vigilant-medical-solutions-dr-justin-scott
CLIENT TESTIMONIAL

“By applying a robust machine learning model to our app, DS helped us create a scalable and accurate solution to improve the operating room experience for the anesthesia team and the patient. They are true collaborators.”

Dr. Justin Scott, MD, FASAchief executive officer & founder

The challenge

Anesthesiologists generally provide care for multiple patients at the same time. This makes it difficult to remain in a single operating room to continuously monitor a patient. Our client, Vigilant Medical Solutions, created a remote monitoring app designed to help anesthesiologists monitor multiple patients simultaneously, but the app needed refinement to display vital signs accurately from multiple monitors. Vigilant needed an advanced machine learning model that could extract and transcribe clinically meaningful data into accurate metrics from multiple operating rooms.



The solution

We used advanced machine learning tactics to capture, define, and structure monitor images and translate the patient vitals with high accuracy directly to the medical staff’s mobile devices. With custom alerts and real-time data, the medical staff are alerted the moment their patient’s vitals go beyond pre-set parameters or their individualized alerts.



remote monitoring hospitals

Remote monitoring in hospitals

Our stakeholder, Dr. Justin Scott, had a vision to create a mobile app that would give anesthesia teams a way to monitor multiple surgical patients simultaneously. As an anesthesiologist within a large hospital network, Dr. Scott understood the challenges associated with trying to oversee vital signs of multiple patients at once.

To improve operating room surveillance and accelerate clinician intervention, we needed to build and apply a machine learning model that could accurately transcribe data from various types of operating room monitors to a physician’s mobile device in real time.

Operating-room-monitors

Machine learning in healthcare

Analyzing over a million images on a variety of operating room monitors, we were able to create an intelligent and precise model that can quickly analyze and translate complex data on monitors into metrics on a mobile app, with 99% accuracy.

 

Operating-Room-Remote-Monitoring-ML-Model

Technologies

  • ruby on rails
  • node.js
  • express.js
  • golang
  • python
  • tesseract
  • terraform
  • redis
  • aws green grass



  • shell script
  • ruby script
  • twilio / sendgrid
  • ec2
  • cloudfront
  • s3
  • raspberrypi
  • postgresql

UI & branding refresh: before & after

To help promote the Guardian app, our team performed a UI and branding redesign for the Guardian website. In the redesign, we focused on SEO and inclusive design for diversity and accessibility.

UI-refresh-Guardian
operating room remote monitoring

Results

Through advanced machine learning models, we transformed the partially developed Guardian app into a fully developed remote monitoring app that can transmit patient vital signs in real-time with 99% accuracy.

The Guardian app is now available to hospitals and anesthesia teams throughout the nation. The app allows clinicians to continuously track vital signs of multiple patients simultaneously, accelerates clinician intervention, decreases liability exposure, and improves patient outcomes.