Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
- Providing a review of the various current usages of blockchain technology for healthcare applications.
- Discussing the key challenges for the healthcare applications in the blockchain technology.
- Describing and stressing the guidelines for future investigation and open issues about blockchain-based healthcare applications.
- Discussing the benefits and drawbacks of existing blockchain-based healthcare applications.
2. Blockchain-Based Healthcare Applications
3. Blockchain-Based Healthcare Management Applications
- Step-1: Primary data is generated by the interaction between a patient and their doctors and specialists. This data consists of medical history, current problem and other physiological information.
- Step-2: An EHR is created for each patient using the primary data collected in the first step. Other medical information such as those generated from nursing care, medical imaging, and drug history are also included in EHR.
- Step-3: Individual patient who has the ownership of sensitive EHR, and customized access control is given only to the owner of this property. Parties who want to access such valuable information must request permission which is forwarded to the EHR owner, and the owner will decide to whom access will be granted.
- Step-4, 5, and 6: These three steps are part of the core of the whole process including database, the blockchain, and cloud storage. Database and cloud storage store the records in a distributed manner and a blockchain provides extreme privacy to ensure customized authentic user access.
- Step-7: Healthcare providers such as ad hoc clinic, community care center, hospitals are the end user who wants to get access for a safe and sound care delivery which will be authorized by the owner. For example, no matter where you are treated in the globe, your health record will be available and accessible on your phone and validated through a distributed ledger such as blockchain, to which healthcare providers would continue to add to over time .
3.1. Global Scientific Data Sharing
3.2. Data Management
3.3. Data Storage (Cloud-Based Applications)
3.4. Electronic Health Record
3.5. Blockchain Use Cases in Healthcare Data Management
4. Supply Chain Management
- Step-1: A block is created upon the invention of a new medicine or medical care which includes patent protection and a long process of clinical trials. This information is recorded in the digital ledger as a form of transaction.
- Step-2: Once the clinical trial is successful, the patent is sent to the manufacturing plant for test prototype and mass production. Every product has its own unique identity that is integrated with another transaction or block in the blockchain including other relevant information.
- Step-3: Once the mass production along with packaging is finished, medicine is gathered in a warehouse for future distribution. Information such as, time, lot number, barcode, expiry date are included in the blockchain.
- Step-4: Transportation information is also included in the blockchain which may include time out from one warehouse (IN) to other, mode of transportation, authorized agent, and other information.
- Step-5: A third-party distribution network is normally responsible for distributing drugs and medical supplies to healthcare providers or retailers. A warehouse (OUT) for each third party is used for this purpose from where all distribution endpoints are linked. A separate transaction is also integrated into the blockchain.
- Step-6: Care providers such as hospitals, or clinics need to provide information, for example, batch number, lot number, product owner, expired date to authenticate, and prevent counterfeit. This is also included in the blockchain.
- Step-7: The actions taken by a retailer are similar to Step-6.
- Step-8: Patients are encouraged to determine authenticity throughout the whole process as blockchain supply chain offers transparent information for verification to potential buyers.
4.1. Clinical Trials
4.3. Blockchain Use Cases in Supply Chain Management
5. Internet of Medical Things
- Step-1: In the realm of IoMT, the patient is the source of all data.
- Step-2: Medical IoT devices are normally either attached closely or remotely monitoring patients’ body, consequently, generating large volume of data.
- Step-3: Data generated in step-2 are stored on blocks or on the cloud storage. AI will help blockchain to create intelligent virtual agents, which in turn can create new ledgers automatically. In case of sensitive medical data, where security is the first priority, decentralized AI system could help block chain to reach highest security .
- Step-4: Healthcare providers are the end users who seek access for a safe and sound care delivery which is authorized by the owner.
5.1. Healthcare IoT and Medical Devices
5.2. Healthcare IoT Infrastructure and Data Security
5.3. Artificial Intelligence
5.4. Blockchain Use Cases in the Internet of Medical Things
Conflicts of Interest
|EHR||Electronic health record|
|PHR||Personal health record|
|R&D||Research and development|
|SCM||Supply chain management|
|P2P||Peer to peer|
|QoE||Quality of experience|
|IoMT||Internet of medical things|
|EMR||Electronic medical record|
|WBAN||Wireless body-area network|
|HGD||Healthcare data dateway|
|RFID||Radio frequency identification|
|IoT||Internet of things|
|PHS||Parallel healthcare system|
|PSN||Pervasive social network|
|IDS||Intrusion detection system|
|API||Application program interface|
|ICT||Information and communication technology|
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|Article||Blockchain Technology||Type of Data||Merits||Limitations|
|||• The MultiChain platform do not rely on the Proof-of-work.|
• Private blockchain.
|EHR||• Sharing of health data and securely improving audit logging.||• Except for the EU, no other cross border country is discussed.|
|||• Private blockchain.||EHR and PHR||• Smart App based on blockchain to control and share healthcare data.||• No consideration for scalability and availability.|
• Data sharing is limited.
• Private blockchain.
|Medical image records||• Securely sharing medical images.||• No consideration of data searching .|
|||• Hybrid consensus mechanism based on practical byzantine fault tolerance.|
|EMR||• Securely sharing of healthcare data.||• Medblock failed to provide enough privacy for the patient’s identity and energy efficiency .|
|||• Proof-of-work.||Location||• Multi-layer location sharing schema.||• No discussion provided about under which critical condition a patient’s location data will be retrieved.|
|||Undefined.||EHR||• Securely sharing of healthcare data.||• High storage overhead and the breadcrumbs mechanism is looking up a single record.|
|||• Ethereum platform.|
|Medical records||• EHR management and sharing of healthcare data.||• No consideration of key replacement capability.|
|||• Ethereum platform.||EHR||• Data management in the cloud environment.|
• High scalability.
|• Practically not feasible.|
|||• Hyperledger platform.||Medical records||• Managing personal data in the e-health.||• There is no access control and exhaustive authorization consideration [90,91].|
|||• Ethereum platform.||Healthcare data||• Cost effective smart contracts.||• No consideration of interoperability between different parties.|
|||• Undefined.||EMR||• Store and manage EMR in a cloud environment.||• The exact cost of the system is not known.|
|||• Undefined||PHR||• Patients control their personal medical data.||• Interoperability is not tested across several healthcare parties.|
|||• Undefined.||EHR||• Facilitate the privacy of patients and maintain the immutability of EHRs by attribute-based signature scheme.||• This system is not cost-effective for large number of users.|
|||• Consortium blockchain.||Medical records||• Coupling encryption, and signature for robust security.||• The system is not fully automated.|
|||• Undefined.||PHR and EHR||• High control access enhanced mobility.||• The input data to OmniPHR framework must be in the format of OmniPHR standard otherwise rejected .|
|||• Undefined.||Medical records||• Securing and managing medical records by using a genetic algorithm.||• It is very difficult to verify the level of security offered by this system.|
|Article||Blockchain Technology||Type of Data||Merits||Limitations|
|||• Ethereum platform.||Clinical trial records||• Smart contract based on improving the transparency of clinical trials.||• Scalability can be a serious concern.|
|||• Undefined.||Medical Records||• Better support precision medicine.||• Lack of coherence .|
|||• Undefined.||Clinical trial records||• Monitoring and managing records in clinical trials.||• Cost of implementation is high.|
|||• Proof-of-concept.||Clinical trial records||• Improve the transparency and processing of data enhancement and traceability of the consent protocol.||• The relation between the digital and the physical identities of patients is vague.|
|||• Ethereum and Hyperledger Fabric platform.|
• Delegated proof-of-stake and practical byzantine fault tolerance.
• Public blockchain.
|Transaction records||• Enhancing the traceability of falsified drugs.||• The system is developed using simulated network.|
• There is no assurance of not tracking falsified drugs outside of official distribution chains.
|||• Gcoin platform (Consortium).|
• Private blockchain.
|Transaction records||• Creating transparent drug transaction data and shifting from regulating (government audits) to surveillance net.||• Operational cost is not discussed.|
|||• Ethereum platform.|
|Transaction records||• Supply chain system for anti-counterfeits by using RFID technologies.||• Exposed to tracking attacks that monitor the movement of the RFID because electronic product codes are sent as a fixed value during the whole process and it would be possible for counterfeiters to copy the genuine product’s tag [124,125].|
|Article||Blockchain Technology||Type of Data||Merits||Limitations|
|||• Ethereum platform.|
• Public blockchain.
|Sensor data||• Integration of WBAN using smart contracts for securely automated patient monitoring.||• Inefficient data ingestion.|
|||• Ethereum and Hyperledger platform.|
• Private blockchain.
|Multimedia IoT data||• Dyslexia diagnosis data can be shared securely with mobile medical practitioners.||• High upload time.|
|||• Ethereum platform. Proof-of-work.|
• Private blockchain.
|Sensor data||• Ensuring transparency, data security, and data storage by using a PoW consensus mechanism.||• Security risks for real-time monitoring because of faster block-time.|
|||• Hyperledger Fabric platform.||EHR and sensor data||• Robust against network fault such as distributed node down.||• Vulnerable to attack.|
|||• Public blockchain.||Sensor data||• Ensuring anonymity and immutability.|
• Log activity of entity and object.
|• Computational overhead is high.|
|||• Public blockchain.||Sensor data||• Robust localization security of sensor devices in a wireless network.||• Implementation of blockchain in such a large and complex network will eventually be vulnerable to malicious attack.|
|||• Undefined.||EMR, EHR, and PHR||• Storage and mining overhead are reduced.||• Vulnerable to security and privacy.|
|||• Public blockchain.||Sensor data||• Reduced validation time by using Cluster Head.||• Lack of transparency and trustworthiness.|
• Private blockchain.
|Medical records||• Enhanced privacy in medical health prediction model.||• Vulnerable to attack .|
|||• Proof of stake.|
• Private blockchain.
|Medical records||• Artificial healthcare systems.||• Limited treatment scenarios are included.|
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Khezr, S.; Moniruzzaman, M.; Yassine, A.; Benlamri, R. Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research. Appl. Sci. 2019, 9, 1736. https://doi.org/10.3390/app9091736
Khezr S, Moniruzzaman M, Yassine A, Benlamri R. Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research. Applied Sciences. 2019; 9(9):1736. https://doi.org/10.3390/app9091736Chicago/Turabian Style
Khezr, Seyednima, Md Moniruzzaman, Abdulsalam Yassine, and Rachid Benlamri. 2019. "Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research" Applied Sciences 9, no. 9: 1736. https://doi.org/10.3390/app9091736