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DS4H Diabetes AI Clinical
Decision Support Use Case

Dataspace4Health

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Summary: DS4H Diabetes AI Clinical Decision Support Use Case

1. Purpose and Scope

  • The document outlines the integration of an AI-driven diabetes clinical decision support system within the Dataspace4Health (DS4H) framework.

  • It aims to demonstrate how DS4H enables secure, efficient data flows among healthcare providers and researchers, while ensuring patient privacy and compliance with regulations like GDPR.

2. Diabetes AI Decision Support

Current Process ("As-Is")

  • Diabetes care is currently manual, relying on clinician expertise and fragmented data sources.

  • Weaknesses include limited personalization, poor data integration, and suboptimal prediction of complications.

  • The patient journey involves initial consultation, diagnosis, therapeutic decision-making, education, self-monitoring, and regular follow-ups.

Target Process ("To-Be") with AI

  • The vision is to use an AI-powered "Digital Twin"—a virtual replica of a patient’s health profile—to predict risks, simulate treatments, and provide conversational decision support for clinicians.

  • The Digital Twin leverages a Patient Health Knowledge Graph and AI models for real-time risk prediction, treatment simulation, and clinician-facing chatbots.

  • Data privacy is maintained by anonymizing patient data before external transfer.

Functional Requirements

  • The system must generate digital twins, display risk levels, allow intervention simulations, and track outcomes to refine models.

User Stories & Use Cases

  • Diabetologist: Reviews risk scores and runs treatment simulations.

  • Diabetes Specialist: Explores evidence and compares scenarios.

  • Diabetes Nurse: Requests tailored patient education points.

  • Clinical Nutrition Specialist: Simulates dietary changes and gets meal plans.

  • Podiatrist: Receives forecasts and alerts for foot-care risks.

3. Dataspace4Health (DS4H) Framework

Overview

  • DS4H addresses secure data exchange among hospitals, labs, and research institutions.

  • It introduces unified data discovery, standardized Data Sharing Agreements (DSAs), approval workflows, and compliance checks.

To-Be Dataspace Process

  • Steps include data discovery, DSA management, access requests, data preparation (anonymization, encryption), secure transfer, and compliance logging.

User Stories for DS4H Proof of Concept

  • Focuses on federated catalogues for data discovery, streamlined contracting, and efficient data access, leveraging the GAIA-X framework for compliance and automation.

Key Functionalities

  • Onboarding participants and datasets using verifiable credentials.

  • Publishing and managing data offerings in a federated catalogue.

  • Signing and registering DSAs digitally.

  • Requesting and granting data access based on signed agreements.

KPIs & Validation

  • Metrics include time-to-access, compliance rates, and data integration coverage.

High-Level Architecture

  • Components: Federated catalog, access control, data preparation, compliance, and logging modules.

4. Non-Functional Requirements

  • Security and compliance with GDPR.

  • Performance (real-time data discovery, AI compute times).

  • Scalability, interoperability (standard interfaces), and maintainability.

In summary: The document presents a comprehensive blueprint for integrating AI-driven clinical decision support for diabetes within a secure, federated health data ecosystem (DS4H). It details both the clinical and technical processes, user roles, data governance, and compliance mechanisms necessary to enable innovative, privacy-preserving healthcare solutions.


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