How AdaptX Uncovers Hidden Potential in Their Clinical Data

Matthew Goos, the CTO of AdaptX, explains how his company uses FalkorDB as a core component of its solution. AdaptX is an AI-driven clinical management solution that empowers clinical leaders to improve and manage care across patients by leveraging their own real-world EMR data. 
How AdaptX Uncovers Hidden Potential in Their Clinical Data

Highlights

AdaptX leveraged FalkorDB to store and analyze high-dimensional medical data, enabling rapid access to Statistical Process Control (SPC) charts. The solution enhanced data organization, improved recommendations, and unlocked hidden potential in clinical data through LLM interactivity.

Technologies & Integrations

The Challenge

AdaptX faced the complex task of managing and analyzing vast amounts of high-dimensional medical and clinical patient data. Key challenges included:

  1. Efficient storage and retrieval of complex data structures
  2. Providing fast access to Statistical Process Control (SPC) charts
  3. Enhancing data organization and grouping for improved analytics
  4. Uncovering hidden insights in clinical data

The Solution

AdaptX implemented FalkorDB as their core data storage and analysis platform. The solution encompassed:

  1. Utilizing FalkorDB for high-dimensional medical data storage
  2. Leveraging Vector Index support for enhanced data organization
  3. Implementing LLM interactivity for advanced data interpretation
  4. Developing an analytical toolset for rapid SPC chart generation

FalkorDB’s multi-graph-tenancy capability allowed AdaptX to efficiently manage data from multiple clinicians and healthcare facilities. The platform’s low latency and high accuracy enabled real-time analysis of complex clinical datasets.

“AdaptX uses FalkorDB to store high-dimensional data about medical and clinical patient data and results. We provide an analytical toolset that gives clinicians fast access to Statistical Process Control (SPC) charts to answer clinical questions across patients, treatments, and workflows. As we continue to develop and refine our use of FalkorDB, we are excited to take advantage of its new features, such as Vector Index support, to solve new challenges. By organizing and grouping our clinicians and their findings using Vector Index support, we can enhance the recommendations and interactivity of our analytical toolset, providing even greater value to our customers."

The Result

The implementation of FalkorDB yielded significant improvements:

  1. Accelerated access to SPC charts for clinicians
  2. Enhanced recommendations and interactivity in the analytical toolset
  3. Improved data organization and grouping using Vector Index support
  4. Uncovered previously overlooked signals of interest in clinical data

 

AdaptX is also leveraging FalkorDB’s LLM interactivity to unlock hidden potential in clinical data. The integration of LLM interactivity with FalkorDB’s existing capabilities is expected to:

  • Augment pattern recognition in complex medical datasets
  • Facilitate natural language querying of high-dimensional data
  • Enhance predictive modeling for patient outcomes

"By using LLM interactivity to improve how we analyze and interpret clinical data, we can help AdaptX users find signals of interest that may have previously been overlooked, empowering them to successfully address their most pressing problems, including quality, capacity, equity, burnout, and environmental impact.  Overall, we are confident that FalkorDB will continue to play an important role in helping us achieve our business goals and provide innovative solutions for the healthcare industry.”

Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.

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Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.

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Avi Tel-Or

CTO at Intel Ignite Tel-Aviv

I enjoy using FalkorDB in the GraphRAG solution I'm working on.

As a developer, using graphs also gives me better visibility into what the algorithm does, when it fails, and how it could be improved. Doing that with similarity scoring is much less intuitive.

Dec 2, 2024

Ultra-fast, multi-tenant graph database using sparse matrix representations and linear algebra, ideal for highly technical teams that handle complex data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.

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