Apheris for Healthcare

Make healthcare data accessible and usable without any risk of privacy breach to help discover new treatments and cure disease

Talk to us

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.

Never 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.

decorative icon

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.

decorative icon

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.

decorative icon

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.

Maginifier icon

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:

  • 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

If organizations with complimentary data sets could join their databases together, it would become possible to train strong machine learning models that could outcompete current industry standards. However, data sets are protected due to their inherent value. They also often contain intellectual property, which cannot be disclosed or shared with other parties. Furthermore, medical data can contain sensitive and personal identifiable information, which is governed by regulatory frameworks that often prevent companies from easily sharing data within their professional networks.

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. Although machine learning in its various forms has been deployed for drug discovery and development for several decades, the era of Big Data has created a niche for Deep Learning. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

Interested in Collaborative Data Ecosystems?

Get in touch with one of our specialists to learn more

Get in touch