Patient Deterioration Monitoring App

This Predictive AI Healthcare app uses real-time patient data to predict deterioration, enabling providers to make faster, data-driven decisions to improve outcomes.

Comment 1

Predictive AI Healthcare App: Project Story

Inspiration

As a Biomedical Engineering Support Specialist with the Veterans Affairs (VA) for the last three years, I've had a firsthand view of how crucial it is for healthcare providers to have timely insights into patient conditions. I often saw instances where early detection of patient deterioration could have drastically improved outcomes. However, many times, it was the delays in identifying subtle changes that led to adverse outcomes. This experience inspired me to develop a solution that would empower healthcare professionals to make faster, more informed decisions, using the power of predictive AI to monitor patient health in real time.

What I Learned

During the development of this project, I gained a deeper understanding of:

  • FHIR Standards: Learning to work with SMART on FHIR for seamless integration with EHR systems was vital. It allowed me to access and use real-time patient data to power predictions.
  • - Machine Learning: I applied machine learning techniques to process healthcare data, training models to predict patient deterioration, a skill I now see as invaluable for improving clinical workflows.
  • API Integration: I gained hands-on experience integrating external healthcare APIs like FHIR to fetch patient data, which is crucial for building predictive applications in the medical field.

How I Built It

  1. Frontend: The user interface was built using React to display real-time patient data and show predictions about patient conditions.
  2. Backend: Using AWS Lambda, I created serverless functions to process data and make predictions. These functions call the SMART on FHIR API to fetch patient data and pass it through the predictive AI model.
  3. Machine Learning Model: I trained a machine learning model to predict patient deterioration based on key health metrics such as heart rate, blood pressure, and oxygen levels. This model runs as a Lambda function and generates real-time predictions.

Challenges Faced

  • FHIR Integration: Integrating with the FHIR API was challenging, especially with the need to convert and process patient data into the appropriate format for real-time predictions.
  • Training the Predictive Model: The complexity of healthcare data made it difficult to train an accurate predictive model. I had to experiment with various algorithms and refine the model iteratively.
  • Real-Time Processing: Ensuring that predictions were made in real-time without delays was a challenge, requiring me to optimize Lambda functions and algorithms for speed and accuracy.
  • Security and Compliance: Given the sensitive nature of healthcare data, ensuring the app complied with HIPAA and other healthcare regulations added complexity to the design and development process.

Conclusion

Working as a Biomedical Engineering Support Specialist at the VA has provided me with valuable insights into the everyday challenges faced by healthcare providers. Building this Predictive AI Healthcare App allowed me to apply my technical expertise to address these challenges, ultimately creating a solution that could transform patient monitoring and improve outcomes. This project has strengthened my skills in AI, AWS, and FHIR, and I am excited about the potential impact of predictive healthcare solutions.

Share this project:

Updates