Principle Investigator · Project Manager
Oliver Diekmeier
M.Sc.
DICE-CD
Contact
Oliver Diekmeier (M.Sc.)
Researcher and Doctoral Candidate
Project Status
planned
Kick-off Date
January 1, 2026
Website
About the Project
Description
DICE-CD (Data Innovations in Collaborative Ecosystems for Clinical Domains) is a nationally funded project (BMBF DigiNutzenDat) that develops a modular, scalable data ecosystem for clinical domains. The project connects hospitals, research institutions, and industry partners to integrate electronic health records, imaging, lab, and genomic data into a unified architecture.
Using autosomal dominant polycystic kidney disease (ADPKD) as a reference case, DICE-CD builds a blueprint for data-driven, interoperable clinical workflows that improve decision-making, enable AI applications, and streamline care. The project also creates a Clinical Data Usage Maturity Model to help hospitals measure and advance their data capabilities.
Our goal: turning fragmented data into actionable insights – empowering clinicians, researchers, and patients alike, and creating a foundation for the future of digital healthcare.
Collaborators
Collaboration Partner
| Name | Contact | Quicklink |
|---|---|---|
|
Learn More | |
|
Massimo Adorno | Learn More |
|
Volker Otto (CTO) | Learn More |
|
Dr. Johannes Schmidt | Learn More |
|
Learn More |
Funding
This project is supported by
Outcome
Achievements & Results
DICE-CD delivers concrete, validated outcomes that advance clinical research and healthcare delivery:
- Reference Architecture: A scalable, modular data ecosystem for ADPKD that can be adapted to other clinical domains.
- Clinical Data Usage Maturity Model: A dynamic framework for hospitals to assess and improve data integration and utilization.
- Interoperable Interfaces & APIs: Secure, standardized data flows across EHR, lab, imaging, and research systems.
- Validated Pilot Implementation: A fully functional prototype tested in two leading ADPKD centers, with real-world clinician feedback.
- Innovation Catalog: Prioritized use cases and guidelines for embedding AI and analytics into clinical workflows.
- Knowledge Transfer: Open-access publications, conference presentations, and open-source components to support adoption and scalability.
Together, these results create a blueprint for digital transformation in healthcare – improving decision-making, efficiency, and patient outcomes.