DoctorVigilance

Elevate patient safety with our app: fatigue detection and surgical error prevention for doctors and nurses. #HealthTech

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DoctorVigilance: Elevate patient safety with our app

Inspiration

Our journey with DoctorVigilance began with a poignant presentation with Patient Safety Workshop. Experienced nurses and doctors shared heartbreaking stories of medical errors that led to tragic outcomes. These stories highlighted the pressing need for innovation in healthcare to prevent such devastating incidents.

Inspired by these stories, we embarked on a mission to harness technology for patient safety. We gathered feedback from healthcare professionals, identifying key challenges they face daily. Additionally, one of our team members has a family member who is a surgeon, and his insights into the grueling demands of surgical procedures further fueled our determination to make a difference.

We recognized that overworked doctors and nurses can unintentionally make mistakes, and we believed that technology could play a pivotal role in preventing these errors. With recent advancements in computer vision technology, we saw an opportunity to develop DoctorVigilance to improve healthcare outcomes.

What it does

DoctorVigilance is a revolutionary health tech application that offers real-time monitoring and assistance to healthcare professionals during surgical procedures. It features:

  1. Real-time Surgeon Hand Movement Monitoring: DoctorVigilance uses computer vision technology to detect unintentional hand movements or shakings during surgical procedures. It provides instant alerts to surgeons, helping them maintain steady hands and reduce the risk of errors.

  2. Real-time Eye Movement Monitoring for Doctors and Nurses: The app continuously monitors the eye movements of doctors and nurses, detecting signs of fatigue or prolonged concentration. When it identifies signs of fatigue, it recommends breaks to ensure healthcare providers remain alert and focused.

  3. Real-time Detection of Mask for Doctors and Nurses: The app uses machine learning to identify the doctors face and consequently tells us if the person is wearing a mask or not. This can act as a helpful pre surgery check.

How we built it

Building DoctorVigilance was an exciting and challenging journey. Here's how we did it:

  • In-Depth Research: We conducted extensive research on eye and hand movement identification and explored various algorithms to achieve high-accuracy detection.

  • Utilized Cutting-Edge Libraries: We leveraged the power of computer vision and machine learning with libraries like Mediapipe and OpenCV and skit-learn to develop our monitoring models.

  • Deployment: We used Streamlit, Twilio for communication, and Google Cloud for deploying our application into production, ensuring it could be easily accessed and utilized by healthcare teams.

Challenges we ran into

Our journey was not without its fair share of challenges:

  1. Accuracy Challenges: Achieving high accuracy in detecting hand and eye movements while minimizing false alarms was a significant challenge. We invested substantial effort into fine-tuning our algorithms.

  2. Resource Constraints: We faced limitations in terms of advanced computer resources for both model training and real-time application usage, requiring us to optimize our code and models.

Accomplishments that we're proud of

Our hard work and dedication yielded several achievements:

  1. High Accuracy: We successfully achieved high accuracy in movement detection, ensuring that alerts and recommendations are reliable.

  2. Rapid MVP Development: Despite the challenges, we managed to create a Minimum Viable Product (MVP) within a mere 36 hours, showcasing our commitment and agility.

  3. Scalability and Integration Potential: DoctorVigilance boasts high scalability and the potential for seamless integration into existing healthcare systems, making it a versatile tool for healthcare providers.

What we learned

Our DoctorVigilance journey was a learning experience in many ways:

  1. Advanced Technology: We delved deep into computer vision models and reinforcement learning, gaining expertise in cutting-edge technologies.

  2. Healthcare Challenges: We developed a profound understanding of the delicate balance between quality and safety in healthcare.

  3. Human Dynamics: Our exploration of human eye and hand movement dynamics enriched our knowledge in human behavior analysis.

  4. Social Responsibility: As application developers, we realized the social obligation that comes with wielding coding power and recognized the importance of ethical development.

What's next for DoctorVigilance

Our vision for DoctorVigilance extends beyond its current state:

  1. Integration into Healthcare Systems: We aim to seamlessly integrate DoctorVigilance into existing medical care systems, making it an indispensable part of healthcare procedures.

  2. Customizable Models: We plan to further train and customize our computer vision models using surgical data, enhancing their accuracy and relevance.

  3. Comprehensive Solution: DoctorVigilance will evolve to include additional features such as surgical equipment monitoring and sanitization detection, making it a comprehensive solution for the healthcare industry.

DoctorVigilance is more than just an app; it's a commitment to patient safety and healthcare excellence. Together, we can elevate the standards of care and reduce the risk of medical errors, ensuring brighter and healthier futures for patients worldwide.

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