🏥 Pilot Option B – Clalit Health Services

CAVIA (Biometric Data Acquisition Infrastructure)

1. System Overview: Beyond Reactive Healthcare

The Problem: Current monitoring models are often reactive—clinical teams respond only after a patient’s condition has already deteriorated. In the "Clinic of the Future," having access to continuous, high-quality data is the key to moving from reaction to prevention.

The Solution: CAVIA is a high-fidelity, biometric data acquisition infrastructure. It leverages the full suite of advanced smartphone-based sensors to capture a patient's functional and behavioral data in real-time, providing Clalit’s researchers with the continuous data stream necessary to identify subtle changes before they become clinical emergencies.

2. The Innovation: The Data Pipeline for Preventive Medicine

CAVIA represents a major technological leap in patient management, proven through two years of deployment at Soroka Medical Center:

High-Fidelity Acquisition: A proprietary data pipeline capable of capturing raw sensor data (movement, gait, and activity patterns) with extreme precision and industry-leading battery efficiency.
Managed Cloud Infrastructure: TendersLab provides and manages the entire back-end infrastructure on a secure AWS cloud environment, ensuring high availability and robust data security for massive biometric datasets.
Direct Data Access for Research: The system provides Clalit’s internal Data Science teams with direct access to a clean, structured repository of raw sensor data. This massive database serves as the essential foundation for researchers to develop their own proprietary algorithms and clinical insights.

3. Immediate Value: Precision in the Community

Data Democratization: Providing Clalit’s researchers with immediate access to raw, real-world patient data without the need to build complex collection tools or manage storage servers.
Clinical Research Acceleration: Shortening the time-to-insight for internal studies by providing a "research-ready" biometric stream.
Security & Compliance: Leveraging AWS’s advanced security protocols, managed entirely by TendersLab, to ensure the biometric repository meets the highest standards of data protection.

4. Pilot Implementation (4 Months)

Scope: Deployment in a selected Clalit clinical research program or high-risk patient cohort.
Technical Constraint: During the pilot phase, installation will be limited to CAVIA-supported devices (Android OS only, up to Android 14) to ensure stability and data integrity.
Phases:
1. Month 1: System integration and setup of the AWS-based data delivery pipeline.
2. Months 2-3: Active data acquisition phase for the designated cohort.
3. Month 4: Evaluation of data integrity, capture rates, and research usability.

5. Anticipated Impact & Users

Participants: Continuous data collection from 30–50 high-risk participants using supported Android devices.
Primary Users: Clalit’s Data Scientists and Researchers, who will gain direct access to the raw data repository to build their own analytical models according to organizational needs.

6. Required Resources

From Clalit Health Services:

Research Team: Allocation of Data Scientists to analyze the collected data.
Clinical Point of Contact: To manage patient recruitment and coordination.
Technical Support Representative: A dedicated staff member to assist with the on-site installation of the application on the participants' devices.

From TendersLab Ltd (Managed Service):

Cloud Infrastructure: Provision and management of the secure AWS storage environment and the biometric repository.
CAVIA Platform: Deployment of the mobile acquisition app and providing structured data access to the research team.
Technical Training: Providing the Clalit representative with the necessary training for smooth on-site installation.

7. Defined Success Criteria

Data Reliability: Achieving 99% success in the transmission and storage of raw sensor data to the AWS repository.
Research Readiness: Confirmation from Clalit’s data scientists that the captured data is high-quality and accessible for their analysis.
Operational Efficiency: Successful onboarding and installation for the participant cohort within the designated timeframe.