SagaChain™ enables the development of secure, reliable and accessible healthcare applications – improving on and seemlessly integrating with all areas from patient to professional
Decentralized data means that there is no single point of failure, as found where applications access a single, standard database system.
Medical data is stored securely in every node on the network – providing the most robust, failsafe mechanism for accessing important data at speed, anywhere around the world.
Applications are developed with the full power of a mature object-oriented scripting language (Python) using a modified called SagaPython™
Having a wealth of resources means that there is a huge amount of developers and code available to expedite the development and testing processes.
The hybrid PoW/PoS nature of the SagaChain™ consensus, makes it highly resistant to attack.
Additionally, sensitive data can be stored in special enclaves, and is fully encrypted when stored (on chain) and is only decrypted by specifically allocated keys.
Patient health data
Electronic health records make patient information available to authorized users, instantly and securely.
SagaChain™ is perfectly positioned to power this data – thanks to the mechanics of its security, limitless scalability and speed of transaction.
In addition, it couldn’t be easier to develop new, or integrate SagaChain™ into existing applications using SagaPython™.
Pharma supply chain
When powered by SagaChain™, medical supply chains become immediately intuitive and understandable.
Pharmaceuticals supply can be fully audited – tracked and traced, from the patient, up the chain, to the manufacturer who produced …and at every point in between.
The procurement process is similarly empowered – by factoring for certificated and automated supply.
Research and development
The data created developing new devices and treatments, along with associated clinical trials, becomes a more secure and streamlined process.
Huge amounts of data can be transacted and integrated at speed – making research at ever larger scales possible.
And at any point, additional influencing datasets (also stored on chain) can be included – allowing for many advanced cross-referencing operations.