Table 2.
Comparison between traditional healthcare systems over proposed healthcare system based on various technical aspects and their benefits.
| Aspects | Standard Healthcare System | Proposed Healthcare Platform |
|---|---|---|
| Source Data Storage | The COVID-19 data are stored in a centralized cloud-based storage system, like PACS. | The COVID-19 data are stored in decentralized storage systems, such as IPFS. |
| Database Sharing Mechanism and Integrity | Depends on a cloud-based mechanism and EHR databases managed by a third-party clearinghouse. Thus, there are possibilities of data tampering. | Depends on a blockchain-based sharing mechanism and EHR databases managed by the participants of the healthcare ecosystem. Thus, databases are immutable. |
| Administration Performance and Scalability | More transactions are processed per second and enable great scalability. | Process minimal transactions per second, and there are scalability issues since the framework is at its developing stage. |
| Implementation Cost | Easy to implement and maintain due to its large-scale adoption. | Uncertainty in the operating costs. |
| Incentive Mechanism for Sharing Data | Not available. | The patient can receive an incentive for sharing their medical data for research purposes. |
| Data Accessibility | Depend on healthcare entities. | Patients have complete access to and control over their data. |
| Anonymity | High risk of privacy leakage and identity theft. | The identity of the patients and the transactions between healthcare participants remain anonymous since blockchain public addresses do not link to anyone’s identity. |
| Data Auditability | Always depends on administrators to audit the data. | The moment the blockchain reaches a predetermined state, any node in the blockchain network can track and trace the data right from its origin based on cryptography technology. |
| Computational Performance of AI | Computationally expensive for training large datasets acquired from different sources in a centralized server. | The federated learning approach reduces the computational power by enabling collaborations between several healthcare organizations to train the distributed global AI models without relying on any centralized server. |
| Decision Making | Human involvement. | Human involvement, AI, and a smart contract. |
| Fault Tolerance | Risk of a single point of failure. | A distributed blockchain ledger is highly fault-tolerant because of the consensus mechanism. |