A Layered View of Blockchain in Healthcare

To understand the value that blockchain technology can provide in healthcare, it is helpful to take a layered view of the current state of healthcare, the potential value of blockchain, and the foundation that blockchain can provide for higher level functionality. This is depicted in the following diagram:

Today, the vast majority of healthcare data is locked up in silos within healthcare organizations. While there is data sharing occurring, many common types of data are stored and maintained redundantly across silos. This redundant storage and maintenance of common data results in significant waste of healthcare resources. Furthermore, when these common records stored redundantly across silos are inconsistent, due to not being kept up to date or inaccuracies, it can result in significant friction in the healthcare system, such as patient records being misidentified, medical claims being rejected, and so forth.

Blockchain enables the targeted, secure sharing of common types of data across consortia of healthcare organizations. This enables common data records to be maintained in one blockchain rather than redundantly across silos in healthcare organizations. The vast majority of healthcare blockchains are private blockchains where every healthcare organization connecting to the blockchain is well known and highly trusted, and access to these blockchains is restricted and access controlled. Data sharing is targeted in the sense that only minimal but sufficient data is shared for the defined healthcare use case, in order to realize established healthcare values, ranging from reducing healthcare costs, to improving patient outcomes, experiences, and engagement. In this layer, all of the executable logic of the healthcare consortium resides in the healthcare enterprise systems at the periphery of the blockchain. These systems include EHR systems in healthcare providers, membership, eligibility, and claims adjudication systems in healthcare payers, and so forth.

In this layer, some executable logic can be added to healthcare blockchains. This can improve efficiency, performance, and resilience since such logic can execute directly on the blockchain and does not depend on any enterprise systems at the periphery of the blockchain, which can be single points of failure. Executable logic on the blockchain is also known as smart contract. As new transactions are added to the blockchain they can trigger associated smart contracts, which then execute and result in outputs that are in turn stored on the blockchain.

Some healthcare blockchain use cases may benefit from cryptocurrencies or tokens. For example, a use case that requires patient engagement may encourage and reward such engagement with cryptocurrency. In another example, a clinical trials use case may reward patients with cryptocurrency for opting into participation, and sharing their data for the associated research. Patients may then redeem such cryptocurrency rewards to reduce costs of healthcare services or other meaningful purposes. Cryptocurrencies may also be useful to incentivize sharing and collaboration of healthcare organizations in blockchains. This can be important where healthcare organizations participating in a blockchain have asymmetric sizes and value contributions, bringing into question fairness of participation. In such a case, to maintain fairness and participation, the contributions of each healthcare organization can be rewarded with cryptocurrency amounts proportional to the size of their contributions.


Blockchain is a foundational technology supporting a vast range of healthcare use cases. As discussed in lower layers, it can enable and incentivize the targeted, secure sharing of healthcare data, and even automate some of the processing of this data with smart contracts. Artificial intelligence (AI) and machine learning (ML) show major potential in healthcare. However, they are extremely data hungry, and many AI and ML initiatives are constrained by data available from a single organization. This in turn limits the quality of models that can be trained and inference that can be done, and often results in error rates that are too high, rendering AI impractical for many use cases and applications. Blockchain enables the sharing of data from across consortia of healthcare organizations, greatly increasing the availability of data to power AI and ML. This has the potential to greatly increase the quality of models and inference that can be done, reduce error rates, and enable new insights that reduce healthcare costs, and improve patient outcomes, experiences, and engagement.

blockchain; AI