Healthcare data
Collecting healthcare data generated across a variety of sources encourages efficient communication between doctors and patients, and increases the overall quality of patient care providing deeper insights into specific conditions. The whole future of healthcare is going to focus on the ability to securely share data. To empower providers and patients to take control of their healthcare journey, we need to build a system of trust that allows the efficient flow of personal healthcare information from stakeholder to stakeholder.
Key Challenges
Data Quality
Poordata quality characterized by incompleteness, invalidity, inaccuracy and duplication can lead to medical mistakes that threatens patients well-being and safety through faulty treatments.
Collaboration
When healthcare entities work together, they can develop muchmore effective treatment programs with better results. To work together means securely sharing data, that is currently sitting in a siloed structure
Security
As healthcare evolves with new technology & legislation, data is continuously under treat from hacking, privacy to atedhealthcare infrastructures or improper secured networks
Security
As healthcare evolves with new technology & legislation, data is continuously under treat from hacking, privacy to atedhealthcare infrastructures or improper secured networks
Privacy
Ethical health research and privacy protections both provide valuable benefits to society. Health research is vital to improving human health and health care. Protecting patients involved in research from harm and preserving their rights is essential to ethical research
Compliance
As patient privacy and compliance are of paramount concern, healthcare organizations need comprehensive data auditing and tracking features to guarantee complianc
Solution overview
Healthcare data digitization on the blockchain via decentralized IPFS secure storage combining federated learning for industry collaboration
Clinical data & Health records
A
Clinical data & Health records
B
Clinical data & Health records
C
Model
Model
Model
Terracuda.Ai Platform
IPFS decentralized storage
Blockchain
Federated Learning
Final model
Healthcare organization provide physicians with blockchain based permissioned access to immutable, privacy encrypted, traceable models
The data models can be used for risk scoring, readmission prediction and prevention, predicting infection and deterioration and so much more at the individual patient level
Models could be licensed via smart contracts on the blockchain in ollaboration with pharmaceuticals for research / clinical trials, and clinical safety analysis
All the consortium participants in the network have access to to the Terracuda.Ai platform, that will enable them to interact with the rest of the network. Here, blockchain on this platform digitizes health records, track each transactions and encrypt data with maximum security that ensures data integrity.
Our solution exploits the federated learning framework by providing algorithms for computation training on the platform. Model owners can then upload his model to a secure distributed storage infrastructure (IPFS) that is available to all member of the consortium, which is orchestrated and tracked on the blockchain, for further continued model updates.
This audit trail on the blockchain gathers events related to the learning process, will preserve privacy by guaranteeing a complete end-2-end federated learning process. This generates values for pharmaceuticals, where the quality of data obtained from this platform can accurately trace major disease in a community and accelerate the process for new drug discovery.
This federated learning technique enables healthcare leaders to drive revenue and innovation within their organization while preserving privacy & security