Blockchain Personal Health Records: Systematic Review

Introduction

Background

Personal health records (PHRs) are a form of electronic health records (EHRs). PHRs are unique in that patients themselves can access, manage, and share their health information [1]. The benefits of PHRs include patient empowerment, which leads to improved outcomes and reduced health care costs [2,3]. Although interest in PHRs has been increasing, their adoption remains low [4,5]. One of the oft-cited reasons is related to privacy and security concerns owing to an increasing trend of health information breaches [6,7]. Another reason is the lack of perceived usefulness to patients [7].

Blockchain technology was introduced through Bitcoin in 2008 [8]. It is considered a general-purpose technology and has since been successfully applied across several different industries [9,10]. In the health care industry, EHRs were found to be the most commonly used case for blockchain applications [11-14]. Compared with conventional data management methods that rely on on-premise data servers or third-party cloud services, blockchain’s distributed ledger technology offers a novel alternative. This could potentially address the privacy and security concerns surrounding EHRs [15]. Specifically for application to PHRs, blockchain also has the ability to decentralize control and incorporate incentive mechanisms through smart contracts, which can further entice its general use and increase adoption [16]. These advantages, among others, have motivated efforts to test the feasibility and implement blockchain PHRs [17-19].

The research space in which EHRs and blockchain intersect is still in its infancy, with the first blockchain EHR introduced in 2016 [20]. Systematic reviews covering this space so far have considered EHRs as a collective entity. Mayer et al [21] provided an overview of the ecosystem of blockchain EHRs while also proposing a taxonomy for the space. Shuaib et al [22] looked at the main areas of focus when implementing a blockchain EHR and the remaining issues to be addressed, whereas Vazirani et al [23] assessed the feasibility of blockchain as a method of managing health care records efficiently.

Given that one of the inherent properties of blockchain is its decentralized nature, in which data ownership is placed in the hands of individual users, some have proposed that blockchain may be more suitably applied to PHRs specifically rather than EHRs in general [19,24-26]. In this paper, we aim to systematically review the following: (1) the current landscape and trends of blockchain-based PHRs (blockchain PHRs), (2) the attributes of various blockchain PHRs that have been described, and (3) the current limitations and future directions for blockchain PHRs. To the best of our knowledge, this is the first systematic review examining blockchain with PHRs. We hope that this review will serve as a useful reference, especially for those intending to develop a blockchain PHR and for future reviews in this area.

To provide more context for subsequent sections of this paper, we will first explain pertinent blockchain concepts and take the opportunity to introduce some terminology specific to blockchain. This is by no means an exhaustive explanation of blockchain.

What Is a Blockchain?

A blockchain can be thought of as a shared (or distributed) database that is spread across multiple sites and participants. For new data to be added to a blockchain, they are first compiled into a block, which is simply a collection of records to be added to the database. The block is then combined with some data (a hash key) from the previous block through a cryptographic technique called hashing before it is added. As it combines the previous block’s hash key, each new block is tied to all its predecessors in the form of a chain—hence the term blockchain (Figure 1).

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Types of Blockchains and Their Properties

Before data can be added to a blockchain, its users need to agree or reach consensus. This is achieved through a consensus algorithm. A well-known consensus algorithm is the proof of work (PoW) algorithm. PoW is used in the Bitcoin and Ethereum blockchain network protocols [8,27]. In the PoW algorithm, users (also known as miners) compete in computational tasks to reach consensus. The winning miner of each block’s task is usually given a reward [28].

Blockchains can be classified into the following three types, depending on which participants are allowed in the consensus algorithm [28]:

1. Public: anyone can participate in the consensus algorithm. Examples include Bitcoin and Ethereum [8,27].
2. Consortium: a select (or permissioned) group of entities can participate in the consensus algorithm. Examples include Hyperledger Fabric (HF), Quorum, and Corda [29-31].
3. Private: only a single entity operates the consensus algorithm and controls the addition of new data.

Public blockchains are sometimes referred to as permissionless blockchains, whereas consortium and private blockchains are collectively termed permissioned blockchains.

The three types of blockchains differ in the following properties:

1. Decentralization: unlike traditional databases that are owned by a specific entity, a decentralized blockchain can allow every user to own the data collectively. Using the illustration in Figure 1 as an example, a decentralized blockchain would contain all the records, but only one user owns records #1, #3, #4, #6, and #8, and another user separately owns records #2, #5, #7, and #9.
2. Immutability: because of the underlying chain structure, once data have been added to the blockchain, they cannot be tampered with. Changing a record would alter the hash key and effectively cause a break in the chain.
3. Transparency (with privacy): the entire blockchain can be made publicly viewable while preserving privacy by masking each individual record using cryptography. To unmask one’s own records, a private key is required.

Table 1 provides a summary of the different types of blockchains and their properties, with an example of each type in the health care setting.

Scalability and Smart Contracts

Finally, we will briefly explain the two concepts of scalability and smart contracts, which will be relevant to subsequent parts of this paper.

Scalability refers to the capacity of a blockchain to store and process transactions. It generally relates to the size and frequency of transactions a blockchain can handle. For example, Bitcoin’s block size is limited to 1 megabyte, and each block is added every 10 minutes. This translates to a rate of approximately 7 transactions per second. Various solutions have been proposed to improve scalability. One such solution is to store data off-chain (instead of on-chain), and another solution is to use side-chains (linked to the main chain) to enable larger transaction volumes to be processed in parallel. Given that health care data are estimated to reach as much as 2314 exabytes generated yearly by 2020, it is crucial for almost all blockchain-based health care applications to achieve a certain level of scalability [32].

Smart contracts are programmable computer rules. Blockchain is a digital database that allows for the implementation of smart contracts, which can be automatically triggered to execute when predefined conditions are satisfied. For example, a smart contract can be programmed to issue tokens on the blockchain each time a user records his or her blood pressure. These tokens can then be used to pay for health care services. Such smart contracts can thus potentially be used to enable incentive structures to encourage certain positive user behaviors.

In this systematic review, particularly focused on the blockchain component of blockchain PHRs, we will pay particular attention to the (1) type of blockchain, (2) scalability solutions, and (3) smart contract–based incentive structures.

Methods

Study Design

While conducting and reporting this systematic literature review, the guidelines described in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement were adopted [33]. This type of literature review was selected because the goal was to identify articles on blockchain PHRs and to summarize the current landscape, design choices, limitations, and future directions. Unlike a meta-analysis, this review did not require any data synthesis. Quality assessment was not performed because the intention was to achieve a collective understanding of the efforts and ideas rather than judging the quality of various blockchain PHRs.

The presented systematic review was carried out by defining the following activities:

1. Research questions
2. Search strategy
3. Article selection
4. Data abstraction

Research Questions

For this review, there were 3 research questions we aimed to address:

1. What are the current landscape and trends of blockchain PHRs in terms of interest groups, geography, and maturity level?
2. What were the key design decisions made for the blockchain PHRs described?
3. What are the current limitations faced by blockchain PHRs and future directions?

Search Strategy

The following search string was used: “blockchain” AND (“health record*” OR “medical record*” OR “*EHR*” OR “*EMR*” OR “*PHR*”). Articles in the following databases were searched: (1) ACM, (2) IEEE Xplore, (3) MEDLINE, (4) ScienceDirect, (5) Scopus, (6) SpringerLink, (7) Web of Science, and (8) Google Scholar. For databases whose search engines did not enable the use of wildcards, the search was widened to include abstracts and keywords, and Microsoft Excel was subsequently used to filter the returned list by applying the search string to the titles.

As the space is still in its infancy stage, Google Scholar was included as a search database to incorporate relevant gray literature in this review. This decision was supported by systematic reviews by Holbl et al [34] and Kuo et al [35] on blockchain in the health care domain, which had found valuable information residing in gray literature.

Article Selection

Once the articles were obtained, we applied the following inclusion and exclusion criteria to select articles for the final review. The inclusion criteria were as follows: a health record system that had (1) a patient-facing component and (2) used blockchain in its health record system. The exclusion criteria were as follows: (1) duplicate articles, (2) review articles, (3) articles that did not have full text available, and (4) articles whose full text was not in English.

The selection was performed in a stepwise manner. First, duplicate articles returned from multiple databases were excluded. Second, the titles of the articles were reviewed and those that were not relevant to the topic were discarded. Third, the abstracts of the articles were reviewed and those whose main focus was not on blockchains and EHR or PHR and those that were review articles were also discarded. Those that looked at EHRs at this stage were retained because some EHRs would have a patient-facing component but might not have been explicitly mentioned in the title or abstract. Finally, the full text was reviewed and those that did not have a PHR element in the EHR were discarded. At this stage, those that did not have full text available or whose full text was not in English were also excluded.

Data Abstraction

For data abstraction, a standardized data collection form was developed using Microsoft Excel. A full-text review of each selected study was performed independently by 3 reviewers who are knowledgeable about blockchain and health records. For discrepancies in the abstracted data, the reviewers performed a repeat review of the articles together to reach a consensus.

For the interest groups, author affiliations, publishers, and publications were used as a proxy. As this space is situated at the intersection of computer science (CS), engineering, and medicine, we classified the publications into either (1) CS or engineering, (2) medical, or (3) general. For maturity level, the classification used by Chukwu et al [12] was modified, and the projects were classified as concept/model/framework, prototypes, and pilots or implementations. A prototype was considered to have both a working front-end and back-end system, and a pilot or implementation had to be a product that was released for use in the real world. If an article described systems at multiple levels of maturity (eg, a framework and a prototype), only the more mature level described was abstracted.

Many design choices must be made when developing a blockchain PHR. To keep this review manageable, the review focused on high-level design decisions [36]. To ensure a comprehensive list of possible design parameters, the PHR taxonomy proposed by Roehrs et al [37] and EHR in a Blockchain taxonomy proposed by Mayer et al [21] were used as starting points. Next, through a consensus-driven process of elimination, 10 design parameters were selected for abstraction. These were (1) blockchain type, (2) data storage, (3) scaling solution, (4) incentive smart contract, (5) PHR type, (6) data owner, (7) read and write ability, (8) semantic standards, (9) privacy standards, and (10) user interface (UI).

For limitations and future directions, the issues and areas for improvement brought up across the articles reviewed were identified, consolidated, and presented as a list of unique issues. We did not delve into a more in-depth analysis such as ranking the unique issues because the frequency of mention was not necessarily associated with importance or criticality. Moreover, the articles may not have fully listed all their limitations, as it was not their primary aim.

In total, 23 data elements were extracted from each article. Table 2 provides a complete list of the extracted data elements and a description of each element.

Results

Overview of Articles

The search performed on July 6, 2020, yielded 325 articles, of which 158 were unique articles. From the article selection process, 51 articles were selected for review. An additional 7 articles were added via snowballing (review of the references from the included articles) of the full texts screened (Figure 2).

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A total of 58 studies were included in the final review [17,19,37-92]. The complete list of articles, with identifiers used in this study, are presented in Table 3. The completed data collection form for these articles can be found in Multimedia Appendix 1. An overview of the articles with the publication year, publisher, article type, country, and interest group is presented in Multimedia Appendix 2 [17,19,37-92].

Current Landscape and Trends of Blockchain PHRs

Interest Group

The level of academic interest in the space has been rising, supported by an increasing trend in the number of published articles since 2016. In terms of interest groups, 45 articles were CS-or engineering-related publications or from CS- or engineering-related authors. Seven were published in medical journals, all of which were related to medical informatics. Of the 6 remaining articles that were classified as General, 5 were whitepapers. The articles from the CS or engineering interest group showed a sharp rise from 2017 to 2019 and may have started to plateau, whereas those from medical journals have been following a gradual, steady increase since 2016 (Figure 3).

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Geographic Distribution

The articles originated from 23 different countries. The majority were from India (n=13), United States (n=9), China (n=8), and South Korea (n=5), with Canada, Switzerland, Taiwan, and Thailand having 2 articles each and the remaining countries having 1 article each (Figure 4). Although the research interest in blockchain PHR is multinational, there clearly are a few countries that are leading the pack. Among these leading countries, there has been an increasing number of publications from India over the years, whereas China, South Korea, and the United States have shown a slowing trend. Apart from these countries, the aggregated output from the rest of the countries is also increasing (Figure 5).

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Maturity Level

The blockchain PHR space is maturing, with the proportion of articles describing prototypes showing an upward trend (Figure 6). In addition, the first paper to describe an implementation was also published in the first half of 2020 by Lee et al [19]. Their blockchain PHR implementation was deployed across Southeast Asia via an information network and became the first PHR management platform for cross-regional medical data exchange.

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Key Design Choices for Blockchain PHRs

Blockchain Attributes

Most blockchain PHRs are described using a private (n=24) or consortium (n=22) blockchain, whereas 4 others used a public-permissioned hybrid design. Only 4 cases of using a public blockchain were described. In the remaining 4 cases, the blockchain type was not clearly stated. An Ethereum-based blockchain was the most commonly used (n=26), with HF being the next most common (n=20). Among these, 3 articles used both Ethereum and HF.

For data storage, the majority used off-chain data storage (n=40), 14 stored EHR data on-chain, and 4 described hybrid data storage. For off-chain storage, 10 articles, all from 2019 onward, used the Interplanetary File System (IPFS). In terms of other scaling solutions, 9 articles considered new consensus algorithms such as Proof-of-Authority, 4 used a tiered-chain architecture, and 1 used both side-chain and algorithmic methods to improve the blockchain scaling capacity.

Among the articles, 5 described an incentive structure in the blockchain PHR using smart contracts. Four of these were whitepapers, which proposed incentivizing stakeholders through the issuing of tokens (digital currency of value) from smart contracts. In these cases, once an action warranting compensation had taken place, the smart contract automatically triggered the issuance of tokens. Table 4 provides additional details of the tokens and how they can be earned and used as part of the incentive structure. Unlike the others, Daraghmi et al [56] proposed a novel, nonmonetary incentive. Their system kept score using degrees based on the effort in maintaining the quality of records and creating new blocks. Those with higher degrees would have a lower probability of performing the computation task of creating new blocks. In this way, it is meant to achieve fairness and sustainability of the system.

PHR Attributes

A total of 18 articles described a tethered blockchain PHR that interfaced with an existing electronic medical record (EMR) system. All of these were of the Concept/Framework/Model maturity level. Those that were prototypes, pilots, or implementations were all standalone PHR systems.

In the majority (n=45) of the articles, the patient was the data owner. Of the remaining articles, providers were data owners in 9 of them, whereas 2 had both patients and providers as owners. It was unclear who the data owner was in the last 2 articles.

In most articles, both patients and providers had read and write abilities. Most blockchain PHRs granted providers with both read and write abilities (n=40), and only 4 blockchain PHRs did not grant providers any read or write abilities. Table 5 is a matrix representing the distribution of read and write capabilities for patients and doctors among the various articles and article codes refer to article identifier in Table 3.

Most articles did not mention the adoption of any semantic standard. For those that did, the 2 standards mentioned were Fast Healthcare Interoperability Resource (FHIR) and health level 7 (HL-7) in 5 and 2 articles, respectively. Similarly, most did not mention adopting any privacy standards. For those that did, 4 mentioned compliance with the Health Insurance Portability and Accountability Act (HIPAA), 1 with the General Data Protection Regulation (GDPR), and 1 with both the HIPAA and GDPR.

Among the blockchain PHRs that were either prototypes or implementations, 9 developed a web UI, whereas 2 had both a mobile phone application UI and a desktop UI.

Current Limitations of and Future Directions for Blockchain PHRs

Current Limitations

Most of the current limitations can be grouped into 1 of the following 3 main categories: (1) scalability, (2) privacy, and (3) usability. Scalability issues pertained to the inability of blockchain PHR to store large file sizes such as medical images [44,53,54] or to the slowness in confirming transactions, especially with the incorporation of streaming data from internet of things devices [45,76].

The inability of blockchain PHRs to ensure full privacy has been highlighted in a few articles. Although records on the blockchain are encrypted, there are possible means to infer the information, such as through blockchain analysis [17,43,54]. Another privacy issue raised was the inability to erase one’s records, as blockchains are inherently immutable [17,70]. This limitation would make it difficult for blockchain PHRs to comply with privacy regulations such as the GDPR, which stipulates data subjects’ right to erasure (Article 17 of the GDPR).

One of the usability limitations was the affordability of the blockchain PHR, as each transaction typically required users to pay a transaction fee [45,71]. Another practical usability issue described by Charanya et al [84] was that, unlike conventional PHRs that had password recovery mechanisms, patients would not be able to access their records if they lost their private keys on blockchain PHRs. Incapacitated or unconscious patients also present a similar problem with blockchain PHRs that do not have built-in access control when emergency health care providers would need permission to access records.

Apart from these 3 main categories, there were other limitations inherent to certain types of popular blockchains such as Ethereum. For example, Gebremedhin [45] highlighted that Solidity (Ethereum’s programming language) was unable to implement nested string data types, whereas Kung et al [86] mentioned the need to batch upload data in a certain file format as a limitation of their Ethereum-based PHR.

Future Directions

The current limitations provide direction to some future work areas for blockchain PHRs. Scalability solutions have already been studied and experimented on, such as Proof-of-Authority and the novel Byzanthine fault tolerance (BFT) consensus mechanisms [44,56,67]. Other methods include enhancing the blockchain architecture through tiered-chain [40,64] or side-chain structures [57]. Although privacy solutions were more limited in our review, we came across one by Reen et al [70] who proposed storing InterPlantary Naming System records instead of the conventional hash of the medical records directly on the blockchain. In this way, users may retain the ability to revoke access to the record if desired.

Many suggestions have been made to improve the usability of the system. These suggestions could be grouped into (1) user experience, (2) integration with existing systems, and (3) compliance with regulations and development of governance processes. Table 6 summarizes the suggestions proposed in the articles reviewed.

Apart from improving usability, another aspect of future work is the validation of blockchain PHRs. Among the areas for validation, several articles suggested data validation when data were transferred to off-chain storage [77], security validation [48,58,79], and real-world validation in terms of cost-effectiveness [52,53,71]. Validating these components would be relevant to obtain stakeholder and user confidence in deciding where to implement and adopt blockchain PHRs.

Discussion

Principal Findings

In this first ever systematic review on blockchain PHRs, we adopted a broad search strategy across medical and CS and engineering databases and included gray literature. We focused on the scope of blockchain PHRs to allow for more targeted data abstraction. Through our study, we found that there was a growing interest in blockchain PHRs and that the space has been steadily maturing over the past few years, albeit still much in the conceptual stage. As the space is still fairly new, a lion’s share of the research and innovation has been happening at the technical level to discover new ways to solve problems. This is evidenced by the overwhelming proportion of articles that have come from the CS and engineering domain.

One of the major areas regarding blockchain PHRs that is still undergoing much research is scalability. We came across a few ideas such as Proof-of-Authority, novel BFT consensus mechanisms, and other modified blockchain architectures such as tiered-chains and side-chains [40,44,56,57,64,67]. Apart from blockchain PHR teams working on this, the space may also benefit from parallel innovations from the larger blockchain ecosystem. As Ethereum is looking forward to a new version release (version 2), it is considering various scaling solutions, of which rollups is a strong contender [93]. Rollups solution essentially involves keeping transaction data on-chain while pushing the computational load off-chain. If adopted into Ethereum 2.0, this could automatically benefit many Ethereum-based PHRs.

Although some areas are actively evolving, others are beginning to consolidate. As found in other systematic reviews, most blockchain PHR project teams have gravitated toward Ethereum and HF as their blockchains of choice [22]. In addition, in terms of data storage, we see more projects opting for IPFS as a complementary off-chain data store for their blockchain PHRs [68,72,83,92]. Outside of this review, we are also aware that there are efforts happening in other public blockchains. An example is NEO, whose core developers are developing a similarly distributed, decentralized object storage network known as NEO file storage system (NeoFS), which will seamlessly integrate with its native blockchain [94,95]. We did not come across any NEO-based PHRs in this review. NeoFS could potentially be a game changer, so it would be interesting to track its development in this area.

In this review, we also identified some current limitations that blockchain PHRs need to address. We broadly classified them into scalability, privacy, and usability limitations. In addition to identifying the current limitations, this review also revealed some possible solutions. For example, to address the privacy issue of inferring information from chain analysis, Ray Chawdhuri [54] introduced zero-knowledge provable mixing, whereas Park et al [17] proposed the zero-knowledge succinct non-interactive argument of knowledge technique. Another example is the solution of using biometric authentication mentioned by Tian [75] to address the issues of verifiable user authentication and patients losing their private keys. Medicalchain has also described an emergency bracelet that can be scanned, giving access to essential health information in unconscious patients who are unable to access their private keys [47].

The first blockchain PHR has already been piloted, and this will undoubtedly augur a move of the space toward deployment [19]. With this in mind, blockchain PHRs will need to comply with the privacy standards within the jurisdictions they intend to become operational. In addition, to enable integration with existing health care EMR systems, it is necessary to design blockchain PHRs that follow established semantic standards such as HL-7 and FHIR. Looking further ahead, to realize true decentralization, it may be necessary to consider building a PHR atop public blockchains.

Finally, in terms of geographic interest, we found that although interest in blockchain PHR was multinational, there were obvious leaders in this space. Looking deeper among the leading countries, we noticed that since 2018 there has been an increase in publications from India, whereas those from China, South Korea, and the United States started to level off or decrease. A possible reason for this could be that in 2017, in the midst of an initial coin offering (ICO) fever that drove unusually high interest in blockchain, the latter 3 countries’ relevant authorities had issued bans or indicated legal restrictions on ICO activities with stiff penalties [96-98]. This may suggest further research into the different factors, including sociopolitical, economic, and cultural factors, which could significantly impact the development of this space. In terms of interest groups, our findings should also provide a sense of where most of the developments are occurring, and this may guide government and private sector funders in their allocation of resources.

Limitations

We acknowledge that this review is not exhaustive and that there are many other areas that were excluded. These areas include other smart contract uses, performance evaluation, and the type of vocabulary standard such as Systematized Nomenclature of Medicine Clinical Terms, 10th revision of the International Classification of Diseases, and Logical Observation Identifiers Names and Codes. We also recognize that greater detail about the read and write models could be studied, such as their validity periods and whether other stakeholders (eg, researchers and insurance companies) were given access. Future reviews should consider delving deeper into these areas.

Furthermore, despite our best efforts to capture as much material available as possible, we are aware that the exclusion of articles whose full text was not in English would have limited the scope of this review. In addition, there may also be other developments in this space that have not been made publicly available for commercial or other reasons.

Conclusions

This cross-disciplinary systematic review on the blockchain PHR space has revealed that as of now, much of the development is still in the conceptual stage. However, there is a trend of growth and maturation. We believe that this provides consolidated evidence for researchers to continue following this space and, more optimistically, to spur them to contribute ideas and efforts to accelerate its development. Those in the medical informatics community will undoubtedly play an increasingly larger role in the development and implementation of blockchain PHRs, especially when the need to integrate with EMR systems and adopt health care data standards becomes more prominent. In addition, as the first systematic review covering blockchain PHRs, we expect this to be an important basis for subsequent reviews to track how the space has progressed in the future.