3 Use Cases for Blockchain in Healthcare – HealthITAnalytics.com

This website uses a variety of cookies, which you consent to if you continue to use this site. You can read our privacy policy for details about how these cookies are used, and to grant or withdraw your consent for certain types of cookies. Consent and dismiss this banner by clicking agree.
Source: Thinkstock

– Blockchain is a type of distribution ledger technology (DLT) that allows data to be stored and shared in a decentralized manner. In healthcare, data storage and sharing challenges are common because health information must be both protected to uphold privacy and interoperable to allow the exchange of data for authorized use, which can improve health outcomes.
Blockchains are defined by the National Institute of Standards and Technology (NIST) as “tamper evident and tamper resistant digital ledgers implemented in a distributed fashion (i.e., without a central repository) and usually without a central authority (i.e., a bank, company, or government). At their basic level, they enable a community of users to record transactions in a shared ledger within that community, such that under normal operation of the blockchain network no transaction can be changed once published.”
These characteristics suggest that blockchain could be a solution to some of healthcare’s data storage and sharing problems but understanding its potential and actual use cases is critical for successful implementation.
One of the most talked-about applications of blockchain in healthcare is centered on EHR interoperability and healthcare big data exchange.
In 2016, the Office of the National Coordinator announced its Blockchain Challenge, which offered thousands in cash prizes to authors of white papers that explored blockchain’s potential use for addressing privacy, security, and scalability challenges related to EHRs. Fifteen winners were chosen, and many of the submissions focused on creating a trusted environment for clinical decision-making.
Clinical decision-making relies heavily on the success of care coordination and the ability to connect data to patients across the care continuum. In one of the winning papers submitted by Beth Israel Deaconess Medical Center, the authors asserted that EHRs were not designed for multi-institutional, lifetime medical record management.
Instead, they proposed a blockchain-based system called MedRec, which allows patients to approve changes to their EHRs, authorize new providers to view their records, and govern sharing between providers. The methodology also has the potential to increase trust in the data at the point of care, which can be a major issue in clinical decision-making.
Blockchain also has the potential to significantly impact healthcare data security.
Health data security is a top priority for all health systems and organizations, but the increasing volume of data and questions about managing it are significant hurdles for providers. Here, blockchain could help as it uses immutable ledgers that are continually updated simultaneously on all participating network nodes. This means there isn’t a single gateway from which data can be tampered with, like in a central repository.
Though providing multiple gateways that are not secure could also present a problem, blockchain is designed to mitigate this risk. Within blockchains, the data “blocks” are connected to all the blocks that come before and after using unique signatures or “chains.” If data within a block needs to be updated, a new block is added, denoting the update, rather than the old block being altered. This creates a record, with timestamps, of all data that is added or updated.
Blockchains also operate using decentralized consensus, meaning that all parties involved in the consortium using the blockchain must agree on how data is verified and recorded. For a bad actor to attempt to exploit this and manipulate the data, they would need to gain control of a majority of the nodes in the network simultaneously and alter the entire blockchain concerned with the data they are targeting. This isn’t impossible, but it is extremely difficult because of the large number of nodes in a healthcare-related network.
The healthcare Internet of Things (IoT) plays a key role in collecting, analyzing, and utilizing patient-generated health data (PGHD). PGHD is created by IoT devices such as wearables, home scales, blood glucose monitors, telehealth tools, mHealth apps, and other technology. This data has enormous potential for enhancing clinical care, but it can be unstandardized and poorly defined. Combine this with the large amount of PGHD generated, and the problem becomes clear.
For PGHD to be useful, it must be clear, concise, and nearly instantaneous. These characteristics enable real-time analytics, which can be used in emergency situations to prevent significant patient harm or death. Many organizations have depended on cloud computing to provide real-time analytics, which allows data to be uploaded from the device to the cloud. From there, relevant information is identified and given to an analytics engine to process and present to clinicians.
Unfortunately, this method can take a few minutes, which may be too long in an emergency. Fog computing has been used to address this problem. With fog computing, IoT devices can conduct analytics on their own by adding a layer of computing between the device and the cloud. This speeds up the processing time, leading to faster clinical decision-making and leaving the cloud pipeline open for large-scale analytics.
Fog computing’s ability to turn IoT devices into miniature data processing centers could also be applied to sharing health data across organizations. Using a fog computing system with predefined user and authorization protocols, patient health data can be transmitted across devices via a shared interface. However, any data transformations or alterations only occur at the hospital or physician’s office from which the data originated.
This fog computing ecosystem would allow permissions-based access to pieces of the same EHR for hospitals, payers, pharmacies, clinicians, and other healthcare stakeholders, without needing to transmit an entirely new record each time one organization had to make a change. This is similar to how blockchains share and update ledgers on each network node. Thus, the fog computing ecosystem could benefit from the security and data integrity protocols of blockchain technology.
Using blockchain, users would be allowed to view and modify certain datasets, and all devices would be accessing up-to-date information simultaneously. Concerns about security and privacy common to some data-sharing models would be alleviated because blockchain prevents unauthorized alteration of data and is extremely difficult to hack because of the way it is designed. Blockchain-specific security and privacy protocols would need to be developed, but doing so has the potential for a significant payoff.
HIMSS has an extensive overview of blockchain in healthcare which explores all aspects of its implementation, including the use cases and challenges described here.

Assistant Editor
.(JavaScript must be enabled to view this email address)
Newsletter Signup
Sign up to receive our newsletter and access our resources

view our privacy policy
Recent Features
Popular Topics
Most Read Stories
Complete your profile below to access this resource.
Thanks for subscribing to our newsletter. Please fill out the form below to become a member and gain access to our resources.
Enter your email address to receive a link to reset your password

Machine-Learning Clinical Decision Support Tool Improves UTI Treatment
©2012-2022 TechTarget, Inc. Xtelligent Healthcare Media is a division of TechTarget. All rights reserved. HealthITAnalytics.com is published by Xtelligent Healthcare Media a division of TechTarget.