Technical Level: Intermidiate

EyA Envision Confidential Computing Within the Medical Sector

How EyA Envision can change the entire medical world, even within a completely trustless ecosystem

The Challenge With the Health Sector

The Challenge With the Health Sector

  • Pharmaceutical and biotech organisations need to identify patient populations to participate in their clinical trials and observational studies.  This continues to be an effort-intensive and lengthy process.

  • Health data is typically dispersed across a myriad of organisations and digital systems.  In many instances these digital systems are legacy-technology that are unconnected and in some instances health records remain paper-based.

  • Extracting, formatting and collating data is usually expensive and requires on-site assistance by in-house and external technology and healthcare personnel.  Achieving this and being compliant with GDPR and any local data privacy regulations presents challenges.

Extending EyA to Make Data Analysis, Federated Machine Learning

EyA Envision has been developed on top of both EyA stack of technologies and Conclave from R3 and Intel SGX. Wrapping the solution up, we have presented a full federated machine learning solution for the trustless environments, which map perfectly to global health and trust related issues between governments.

This along with cross-pollunated data from other related and even non-related industries means that fully enriched and classified data analytics can be executed without the need for transit of data across insecure gateways, opening holes in security barriers, or having to build vast and often incomprehensible data lakes.

Solution for Health Sector

Solution for Health Sector

  • Hospital digital systems and individuals are able to contribute data to EyA Envision from electronic medical records.  This can be further enriched with data held by medical registries, public health bodies and contract research organisations CROs.

  • Data is analysed in secure enclaves against clinical trial and observational study protocols that have been submitted either directly by biotech or pharmaceutical organisations  — or, on their behalf, by CROs. The PharmCo gets better quality data and access to data sources — and patient cohorts — previously undiscovered that better informs medical research and the development of treatments and drugs.  

  • The real value of such cross-pollination is that discoveries are also made outside of the parameters that science and medical research sets.  Hospitals and individuals are better incentivised to participate and receive equitable compensation for their data contributions.  Data privacy is preserved throughout.  Humanity wins.

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