Governance, policy required for blockchain success in healthcare

Blockchain

Blockchain technology holds promise for improving healthcare security, efficiency and trust, but must be implemented within appropriate policy and governance structures to be successful, healthcare attorneys with firm Crowell & Moring say.

In a report published by Bloomberg BNA, authors Jodi Daniel, former policy director in the Health and Human Services Office of the National Coordinator for Health IT, and Ashley Southerland note that the virtual currency Bitcoin is the best-known example of blockchain technology. Blockchain leaves a very visible trail of transactions, but does not require an intermediary for data exchange. A blockchain on a distributed set of computers--Bitcoin only uses one--increases the value many times over, they say.

In a permissioned system, only certain computers have access to the information and each user must have specific authorization and is only allowed to see specific information.

Rather than serving as a central repository, the chain only points to the location of records, making it difficult to hack, according to Peter Nichol, former IT chief of the Connecticut state health information exchange. He predicts it will be “commonplace” by 2020.

The chain maintains a real-time record that’s identical on each computer. The implications are huge for interoperability, Daniel and Southerland say, creating one immutable record, furthering research with both provider and patient input and more effective consent/permissions management.

Still, they say, governance issues to be determined include:

  • Who should have access and for what purposes?
  • When should patient permissions be required?
  • Which local, state and federal laws on privacy and security apply?
  • Who will enforce the rules?

More than 70 entries were submitted to ONC’s recent blockchain challenge. The ideas for using the technology included providing improved real-time claims processing, reducing fraud and using wearables and other technologies to develop patient-related outcome measures from personalized data streams.

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