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Private data stays private
Health data is highly sensitive, and subject to strict regulations and privacy rights. Making health data accessible for wider use can therefore be challenging, whether in primary healthcare, or in secondary use for research. Apheris allows you to build and participate in collaborative, open, federated, and secure data ecosystems that fulfil highest standards on data privacy and security.
Making data accessible
Better and reliable access to health research data allows scientists and researchers to uncover new findings about diseases and treatments.
No need to move data
Apheris is the architectural backbone for health data in research networks around the globe. The federated data platform gives organizations and researchers the possibility to share insights, without moving data and while respecting data ownership and privacy.
Why Apheris
Decentralized data requires more investment to achieve interoperability. We are experts in establishing harmonized standards for large scale health data networks, and work with many global pharma companies in building sustainable, and collaborative data ecosystems. Our platform is used in the health data ecosystems across research institutions, hospitals, labs, pharma companies and insurance companies.
Research-grade data
Apheris helps to curate and standardize available and future data sets to ensure interoperability and enables access to longitudinal data that is complete, standardized, and prospective.
Federated learning
Provide deep and secure access to granular data and insights, without having to move and centralize data, and while respecting full data ownership and privacy.
Privacy-enhancing technologies
Additional to a federated architecture, Apheris leverages different PETs with the highest level of precision to establish governance, privacy, and security, as well as to maintain data quality and usability.
Governance & auditability
Connect data while maintaining full control and privacy compliance. Continuously monitor and audit the data you provide to other organizations.
What customers are saying about Apheris
Pharma
"Our long-term strategy heavily focusses on collaboration with our partners along the health data value chain. We are an innovator across major disease areas, and are convinced that federated approaches will enable the most advanced data ecosystems of the next decade."
Scientific Director Analytics & Data Science Top 20 Pharma
Collaborative Data Ecosystems in Healthcare
A collaborative data ecosystem is an alignment of business goals, data and technology, among two or more participants, to collectively create more value than each can create individually.
Potential shared value propositions in healthcare:
Potential shared value propositions in healthcare:
- Expand data applications across therapeutic areas
- Accelerate drug discovery processes
- Enable precision medicine
- Build marketplaces based on federated health data
Resources
Article
What Are Collaborative Data Ecosystems?
Enterprises across industries are realizing that collaboration on data, machine learning and data science is the key to solving the biggest challenges of our time. This leads to a massive rise of collaborative data ecosystems around the globe. Read more if you want to know what collaborative data ecosystems are
White Paper
Federated Learning on Vertically Distributed Healthcare Data
Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.
White Paper
Privacy-preserving Data Ecosystems in Support of Drug Discovery
In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.