Scaling Medical Data Infrastructure for AI-Driven Patient Monitoring
FluidAI, a leader in medical device technology, partnered with Ample Insight to develop scalable internal workflows for improving biosensor data accuracy, enabling more effective AI-driven patient monitoring. This collaboration empowered FluidAI to provide earlier detection of postoperative complications and improve patient outcomes.
Postoperative complications typically occur in about 30% of patients, with about half of these arising after the patient leaves the hospital.
(American College of Surgeons, 2023)
The Opportunity
Building a Scalable Data System for AI-Driven Patient Monitoring
FluidAI aimed to detect postoperative complications faster by improving data accuracy and workflows to support an AI-powered monitoring system. However, several key challenges needed to be addressed:
- Managing Large Volumes of Inconsistent Data: The Internet of Medical Things (IoMT) generates vast amounts of biosensor data, which can at times be incomplete, inconsistent, or lost, making analysis challenging.
- Combining Data from Multiple Biosensors: Effective patient monitoring requires integrating multiple biosensors to construct a comprehensive view of patient health.
- Developing Internal Tools for Data Processing: To ensure high-quality data inputs for its AI system, FluidAI needed robust internal workflows for cleaning, analyzing, and visualizing complex biosensor data.
To overcome these challenges, FluidAI sought a data partner with deep expertise in AI, data engineering, and healthcare analytics. Ample Insight was chosen to design and implement a robust data infrastructure to support FluidAI’s mission.
The Solution
Implemented a Scalable, Reliable Data Infrastructure
Ample Insight worked closely with FluidAI to address these challenges by developing internal workflows and tools to improve data accuracy and reliability:
- Ensured Data Quality and Reliability: Custom data pipelines were implemented to clean and process biosensor data, applying quality guardrails to maintain accuracy and consistency.
- Developed Advanced Data Processing and Visualization Tools: Tailored analytics and visualization tools were built to help medical professionals interpret complex biosensor data, enabling faster and more informed decision-making.
- Optimized Internal Data Workflows: Structured data workflows and best practices in data engineering were implemented to enhance processing, ensuring more accurate inputs for AI models.
The Impact
Improved Patient Outcomes with AI-Driven Patient Monitoring
The successful deployment of this scalable data infrastructure equipped FluidAI with:
- Enhanced Data Accuracy: Robust data cleaning processes improved the reliability of biosensor data, ensuring high-quality inputs for AI models.
- Enabled Faster Response to Health Risks: With improved data organization and visualization tools, hospitals and healthcare providers gained faster access to critical patient health insights, facilitating timely interventions.
- Strengthened AI Capabilities: Improved data accuracy and quality enabled FluidAI to refine and scale its AI-driven patient monitoring system.
The Ample Insight team rapidly launched our project and got onboarded faster than any previous engagement I've had in the past. It was remarkable. With that, we were able to accelerate our development and path to market by adding resources to our most critical path, and better understand our long-term talent needs. The team automated data reporting and found more effective methods of processing to improve data integrity.
The Ample Insight team rapidly launched our project and got onboarded faster than any previous engagement I've had in the past. It was remarkable. With that, we were able to accelerate our development and path to market by adding resources to our most critical path, and better understand our long-term talent needs. The team automated data reporting and found more effective methods of processing to improve data integrity.