Babita Yadav*
| Sachin Gupta
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Healthcare is one of the major technical challenges of the 21st century, with the rapid adoption of technologies like the Internet of Things (IoT) to support remote patient access to telemedicine. This domain is highly data-intensive and involves handling large amounts of sensitive personal information, making trust a crucial issue among all involved parties. Blockchain technology, with its decentralized trust management model, offers the potential to transform healthcare data management by removing the need for a trusted third party. However, applying blockchain to telemedicine introduces specific data management challenges. The objectives of this research are to determine the diverse storage needs of healthcare data within a blockchain-based system and to develop a decision framework for managing this data. The study begins with an extensive review of existing blockchain storage solutions across various domains, with a focus on healthcare. This background research underscores the importance of making balanced storage decisions that consider the high costs of on-chain methods and the lower security of off-chain methods to optimize blockchain storage expenses. The methodology involves creating a formal framework that guides storage management decisions for telemedicine data generated through IoT devices. This framework incorporates both on-chain and off-chain storage methods, taking into account factors such as data sensitivity, access frequency, and cost efficiency. Scenario-based validation of the framework is performed to evaluate its practicality and effectiveness in real-world settings. The anticipated results include a robust and adaptable decision framework that simplifies storage management for developers and practitioners in telemedicine and healthcare data management. This framework aims to enhance data security, reduce storage costs, and improve the overall efficiency of blockchain-based telemedicine systems.
healthcare big data, blockchain, Internet of Things (IoT), telemedicine systems
Healthcare challenges in the 21st century have earned a prominent position among the seventeen sustainable development goals (SDGs) adopted by the United Nations (UN). The vast amount of healthcare data, encompassing patient medical histories, diagnostic reports, prescription records, and hospital records, exemplifies the concept of big data with its volume, variety, and velocity. The COVID-19 pandemic exposed significant weaknesses in global healthcare infrastructure and underscored the urgent need for telemedicine solutions [1, 2]. This period also presented an opportunity for researchers to develop IoT-integrated telemedicine solutions for continuous, non-invasive patient monitoring with remote doctor access. Despite these advances, the integration of wearable sensors and improvements in communication networks and edge processing have introduced new concerns regarding data privacy and security. The potential misuse of personal and sensitive healthcare data and massive security breaches remain significant obstacles to the adoption of IoT-integrated telemedicine systems. Effective management and protection of electronic healthcare records (EHR) and understanding the sensitive nature of this data [3] are critical for developing any Internet of Medical Things (IoMT) [4] framework or solution.
Blockchain technology provides an immutable way to store transactions in a distributed ledger. This immutability is crucial for maintaining trust in many applications. However, the "Right to be Forgotten," as offered by various laws, presents a challenge. With rising awareness of individual data rights and stricter data privacy laws, such as the General Data Protection Regulation (GDPR) in the European Union, the universal use of blockchain becomes problematic. This necessitates customized implementations rather than standard ones.
While several blockchain storage strategies exist, there is no comprehensive framework for blockchain-based data management [5] in IoT-integrated telemedicine solutions [6]. This paper aims to propose a decision framework for blockchain storage tailored to healthcare's diverse data needs.
This paper offers the following contributions:
1. Extensive Literature Review:
Provides a thorough survey of current research to comprehend the range of blockchain solutions for healthcare and the methods for storing medical health records [7].
2. Diverse Storage Needs Identification:
Identifies the varying storage needs of different types of healthcare data in blockchain-based applications.
3. Framework Proposal:
Introduces a formal framework for managing healthcare big data on blockchain within IoT-integrated telemedicine systems, aiming to deliver efficient and cost-effective storage solutions [8].
The rapid advancement of IoT-integrated telemedicine systems has highlighted the significant challenge of managing healthcare big data. Researchers have extensively analyzed the application of big data characteristics—volume, variety, and velocity—to patient medical histories, diagnostic reports, and other healthcare records, underscoring the complexities and opportunities this data presents. The COVID-19 pandemic exposed critical weaknesses in global healthcare infrastructures, which in turn emphasized the urgent need for robust telemedicine solutions. In response, researchers have developed IoT-integrated telemedicine systems using wearable sensors and advanced communication networks to enable continuous, non-invasive patient monitoring. However, these innovations have also raised significant concerns about data privacy and security [9, 10].
Blockchain technology has emerged as a promising solution for managing and securing healthcare data due to its decentralized nature and ability to establish trust in multi-party environments. Studies have demonstrated its potential in managing electronic healthcare records (EHR) and other sensitive data within healthcare systems. Beyond healthcare, blockchain applications have been explored in various sectors such as supply chain management, food management, digital marketing, reputation management, and smart cities, each revealing unique challenges and solutions [11, 12].
Despite these advancements, integrating blockchain with healthcare big data in IoT-integrated telemedicine systems presents unresolved challenges. These include ensuring data privacy, preventing security breaches, and developing a versatile storage decision framework that can handle the diverse nature of healthcare data, from streaming information to large diagnostic reports [13, 14]. Addressing these issues is crucial for the effective and secure deployment of IoT-integrated telemedicine solutions.
In response to these challenges, this research proposes a novel decision framework that leverages blockchain technology to manage and secure healthcare big data within IoT-integrated telemedicine systems. This framework aims to provide tailored solutions for different types of healthcare data, enhancing data privacy and security [15-17] while accommodating the specific needs of telemedicine [18-20].
Sonnis et al. [21] discuss creating a secure and interoperable healthcare system using blockchain e-healthcare solutions with wireless body area networks to enhance interoperability. They compare power consumption and usage in their study.
A decentralized storage solution is proposed in the study [22] to use unused personal hard disk space globally via blockchain. The system issues a data integrity certificate to users, allowing storage only after verification through lightning network technology. All related proofs and payment information are stored on the blockchain, ensuring security and credibility.
Data masking technology is explored, and insights are provided into using the Inter Planetary File System (IPFS) to build a secure and cost-effective data-sharing model [23]. The cost of storing IoT data on the blockchain using smart contracts is discussed in the study [24], and it also examines storing numeric data from temperature sensors on the blockchain using a single variable.
The study compares storing data in an array versus storing data from all sensors in one variable. The authors conclude that while storing data on the blockchain is expensive, it provides reliable data integrity and transparency. Two critical challenges in health data sharing are addressed: deploying and installing blockchain software across different hospitals, and protecting sensitive health information. A blockchain-based solution using a distributed microservice architecture is proposed. This approach encapsulates core functions into isolated services that can be independently scaled to meet the needs of different hospitals [25]. The workflow process of blockchain-based healthcare on a global scale is also explored. Using blockchain to prevent healthcare data manipulation while maintaining data transparency is advocated [26].
Blockchain scalability issues are discussed, and off-chaining is presented as a solution. Various off-chaining models are categorized, and it is concluded that off-chain computations are more powerful and scalable than other approaches [27]. A detailed study of IPFS-based secure healthcare storage solutions is also provided. Traditional local storage methods and cloud-based storage are compared, highlighting their respective issues. Various existing solutions are discussed, and improvements for medical record storage are suggested [28]. Additionally, the explosion of public and social sector data is reviewed, emphasizing the big data challenges in healthcare data storage [29].
An integrated project focusing on telemetric health using IoT sensors to monitor bedridden patients is discussed in the studies [30, 31]. The authors propose a virtual nurse that observes patient vitals and generates alerts for any anomalies to the attending doctor. They emphasize that IoMT-based observations, such as ECG and glucose levels, can be monitored and reported in real time.
Additionally, a study of various blockchain solutions available in the literature was conducted to inform the framework's choice of blockchain solutions specifically for healthcare, based on the data characteristics and the commissioning organization. Table 1 presents the key distinguishing features of the most commonly used blockchain platforms.
The table can be referred to by the blockchain application developers to choose a platform based on the priority ranking of blockchain features of the specific use case. For instance, it is apparent from the above table that healthcare applications are better adapted to be developed on Ethereum and Hyperledger [32] platforms.
The choice of consensus algorithm is also significant when making the selection of blockchain platforms for application development. There are over a hundred consensus algorithms available in the literature, of which around twenty are exceptionally significant. Table 2 is a filtered list to summarize the most commonly used consensus algorithms from among all consensus algorithms.
It is worthwhile to mention here that there are several off-chain storage methods available for different blockchains. Each method has its own cost, pros and cons which the framework does not address. Without the loss of generality, we have used the Interplanetary File System (IPFS) for the off-chain storage cost calculations in the framework validation. The developer may choose any off-chain storage method based on total present and projected storage requirements of the application data, where the storage cost will adjust according to the choice of platform [33]. To make the choice easier for the developer, a comparative analysis of some common off-chain storage [34] methods is presented in Table 3 for reference.
Table 1. The key distinguishing features of the most commonly used blockchain platforms
|
Aspect |
Off-Chain Storage |
On-Chain Storage |
|
Cost Efficiency |
Significantly lower costs per GB and transaction fees |
Higher costs can be prohibitive for extensive data |
|
Scalability |
Facilitates scalability; easier to scale operations |
Costs can limit scalability; need to manage data volume |
|
Performance |
Better performance for large-scale data storage |
Potential impact on performance due to higher costs |
|
Security and Integrity |
Generally secure; may require additional measures |
Higher security and data integrity due to blockchain |
|
Use Cases |
Ideal for large volumes of less sensitive data |
Best for critical data needing security and immutability |
|
Hybrid Solutions |
Common approach to balance cost, performance, security |
Often used for critical and less critical data |
Table 2. Study of various algorithms from the literature
|
Type |
Lottery Based Consensus Algorithm [35, 36] |
Voting Based Algorithm [35] |
|||||||
|
Algorithm |
Proof of Stake (PoS) [37-39] |
Delegated Proof of Stake (DPoS) [40-43] |
Leased Proof of Stake (LPoS) [43] |
Proof of Work (PoW) [43] |
Proof of Authority (PoA)[44, 45] |
Proof of Importance (PoI) [35] |
Practical Byzantine Fault Tolerance (PBFT) |
Paxos [43] |
Raft [35] |
|
Blockchain type |
Permissionless and permissioned |
Permissionless and permissioned |
Permissionless and permissioned |
Permissionless |
Permissionless and permissioned |
Consortium |
Permissioned |
Permissioned |
Permissioned |
|
Miners Selection |
Based on stake |
Based on stake |
Based on stake |
Hash puzzle |
Hash puzzle |
High priority |
Mathematical operation |
Number will be proposed |
Random timings |
|
Decentralization followed [45] |
Strong |
Strong |
Strong |
Strong [24] |
Strong |
Strong |
Weak |
Weak |
Weak |
|
Transaction fees |
For all miners |
For all miners |
NA |
For all miners |
For miners and stakeholders |
For all transaction partners |
No |
No |
No |
|
Reward [48] |
Yes |
Yes |
Yes |
Yes |
Yes |
Yes |
No |
No |
No |
|
Trust model |
Un-Trusted |
NA |
NA |
Un-Trusted |
NA |
NA |
Semi-Trusted |
Semi-Trusted |
Semi-Trusted |
|
Speed of block creation |
High |
High |
Not Found |
Low |
High |
High |
High |
High |
NA |
|
51% attack [45] |
No |
No |
No |
Yes |
No |
No |
No |
No |
No |
|
Double spending [44] |
No |
No |
No |
yes |
No |
No |
No |
No |
No |
|
Pros |
Higher speed, less energy consumption [45] |
More decentralized and better distribution of rewards [45] |
Earn with fewer tokens also, less energy consumption [46, 47] |
Highly scalable so used in a variety of applications [48] |
Highly scalable, guaranteed higher throughput |
Reduces hoarding of coins |
Less time as multiple confirmations by each node do not require [45] |
Optimized for “ease of implementation” |
Easy to understand and implement as compared to Paxos [45] |
|
Cons |
Less decentralized, less scalability |
Cartel formation [49], 51% attack |
Possible cartel formation |
Energy intensive, notorious 51% attack [50] |
The identities of validators are public |
Rich get richer syndrome |
Sybil attacks [48] |
Overhead of request rejection, live lock |
Real-life applicability low as no byzantine fault assumption |
Table 3. Comparative analysis of off-chain storage methods
|
Criteria |
Filecoin [47] |
Sia [48] |
Swarm [49] |
Storj [51] |
|
Data Replication |
The replication factor is configurable by the user |
Encoded fragments stored across a network |
Encoded fragments stored across the neighborhood |
Encoded fragments stored across a network |
|
Availability of data over |
Proof-of-Spacetime (PoSt), Pledged collateral recurring payments |
Hashed fragments with proof of storage |
Recurring payments, Race Raffle, Proof of ownership, Race Raffle |
Recurring payments, data fragments audits, revenue withholding |
|
Proof of Data Stored |
Proof of Replication |
Hashed fragments Proof of Storage |
Merkle tree root Hash |
Data Fragments Audit |
|
Tracking Storage |
Blockchain and node gossip |
Blockchain and node gossip |
Data Chunks |
Satellite Node |
|
Storage Price |
$1.33/TB/month (Dynamic according to market price) |
$2/TB/month |
Not Defined |
$4/TB/month |
|
data transmission |
Retrieval miners |
Users pay as per bandwidth |
Using protocol |
Payment as per bandwidth usage |
|
Proof of data stored |
Proof-of-Replication (PoRep) |
Proof of Storage (PoS) of hashed fragments with Merkle Tree |
Merkle tree root hash |
Audits of Data Fragments |
2.1 Limitations of existing literature
Blockchain technology in IoT-integrated telemedicine systems faces several challenges that limit its current effectiveness. Scalability is a significant issue, as blockchains struggle to handle the large volume of data generated by IoT devices, with high transaction costs and limited throughput. Energy-intensive consensus mechanisms, like Proof-of-Work, further exacerbate this problem, making blockchain less sustainable for healthcare applications. Direct storage of healthcare data on the blockchain is impractical due to high costs, and while off-chain solutions like IPFS offer alternatives, they introduce complexities in data retrieval and redundancy management. Latency in blockchain transactions also poses a challenge, especially for real-time telemedicine systems that require immediate responses. The lack of universal standards complicates the integration of blockchain with diverse IoT devices and healthcare systems. Privacy concerns persist, as blockchain’s decentralized structure does not inherently safeguard sensitive data, requiring advanced cryptographic techniques that add complexity. Moreover, regulatory frameworks like GDPR and HIPAA may conflict with blockchain’s decentralized approach, particularly in areas like data ownership and the "right to be forgotten." Power consumption issues in IoT networks, particularly in wireless body area networks, reduce reliability for long-term use. Finally, decentralized storage solutions face risks of data unavailability, while storing even small amounts of IoT data on the blockchain remains costly, limiting scalability for healthcare systems.
3.1 Decision framework for healthcare big data on blockchain
Based on the gaps identified from the literature survey, and security challenges observed by the researchers working with storage of different types of healthcare data on blockchain, the following key criterion for storage decision making were identified. The complete framework with all the criteria consolidated in a single flowchart has been presented visually in Figure 1.
Figure 1. Decision framework for managing healthcare big data on blockchain applications
The decision-making criteria in the framework can be modularized into three distinct phases based on the identified criteria. Phase I covers blockchain use case feasibility and subsequent choice of the type of blockchain based on the use case being discussed. The detailed break-up of the criteria is presented below:
I. Criteria for blockchain use case based on application characteristics
• Requirement of persistent storage (If no, blockchain use is not recommended)
• Write access is shared (If no, blockchain use is not recommended)
• Presence of untrusted parties (If no, blockchain use is not recommended)
• Allowing third party access (If no, blockchain use is not recommended)
II. Criteria for blockchain type based on membership characteristics
• Membership is not controlled (Use public blockchain platform)
• Membership is controlled and members are from same organization (Use private blockchain platform)
• Membership is controlled and members are from multiple organizations (Use consortium blockchain platform)
Phase II and Phase III cover the on-chain vs off chain storage decision making process based on the following criteria:
III. Criteria for blockchain type based on transaction characteristics
• Transactions are public (Use public blockchain platform)
• Transactions are not public (Use private blockchain platform)
IV. Criteria for storage decision based on data characteristics
• Data ownership
• Does the owner of the Data change?
• Data volume
• Data sensitivity and confidentiality
• Data governance by privacy laws
• Data aggregation possibility
V. Criteria for blockchain type based on transaction characteristics
• Transactions are public (Use public blockchain platform)
• Transactions are not public (Use private blockchain platform)
Criteria for storage decision based on data characteristics
• Data ownership
• Does the owner of data change?
• Data volume
• Data sensitivity and confidentiality
• Data governance by privacy laws
• Data aggregation possibility
3.2 Data collection
On-Chain Storage:
Cost per unit of storage: Typically measured in gas costs per byte. This varies based on blockchain platform (e.g., Ethereum, Binance Smart Chain).
Transaction fees: Cost per transaction to store data on-chain.
Other costs: Include any additional costs like contract deployment fees or storage maintenance fees if applicable.
Off-Chain Storage:
Cost per unit of storage: Monthly or annual subscription costs or per GB costs.
Transaction fees: Costs associated with uploading, downloading, or accessing data.
Other costs: Any maintenance fees, retrieval fees, or other hidden costs.
Hypothetical Data:
Define a hypothetical dataset with different sizes (e.g., 1GB, 10GB, 100GB).
Estimate the costs for storing this data both on-chain and off-chain.
The framework should include various medical data collection methods, such as data from wearable devices, imaging systems, and electronic health records, to ensure a broad and comprehensive approach to healthcare data management. Additionally, its credibility can be enhanced by validating the framework with large, real-world datasets that reflect different healthcare scenarios, demonstrating its practicality and ability to scale effectively in real-world applications.
Analysis and visualization
A comparison of on-chain and off-chain storage costs for managing healthcare big data on a blockchain platform in IoT-integrated telemedicine systems is shown in Figure 2.
Figure 2. Comparison of on-chain and off-chain data storage costs
3.3 Practical implications and challenges
The cost differences between on-chain and off-chain storage solutions have significant implications for businesses using blockchain for storage in several key areas. table summarizes the implications of using off-chain versus on-chain storage solutions in blockchain applications. It highlights the key differences in cost efficiency, scalability, performance, security, and suitable use cases, as well as the prevalence of hybrid solutions to optimize these factors.
3.4 Adapting blockchain platforms to support future medical technologies
To ensure adaptability to future medical data types and emerging technologies, the framework can be designed with a modular structure, allowing for the seamless addition of new components or functionalities as needed. Integrating machine learning models can enable dynamic analysis and classification of data, ensuring efficient storage and management of novel medical information. The use of interoperable standards and APIs will facilitate smooth integration with evolving telemedicine technologies and IoT devices. A hybrid storage model that combines blockchain with cloud or edge computing can provide the scalability needed to handle diverse and expanding datasets. Lastly, employing advanced cryptographic techniques, including those resistant to future threats like quantum computing, will enhance the framework's resilience and security over time.
3.5 Cost-benefit analysis
The graph above illustrates the cost-benefit analysis of implementing a blockchain-based framework in IoT-integrated telemedicine systems. The red bars show the various initial costs, such as setup, IoT devices, storage, and compliance with regulatory standards. The green bars represent the potential benefits, including enhanced data security, reduced long-term data management expenses, improved healthcare outcomes, scalability, and regulatory compliance. While the upfront costs are considerable, the graph highlights that the long-term advantages, particularly in security and healthcare efficiency, provide significant value over time as shown in Figure 3.
Figure 3. Cost benefit analysis
In real-world deployment, several challenges may arise, particularly with the technical aspects of integrating blockchain into healthcare systems. Issues such as the scalability of blockchain, compatibility with existing infrastructure, and the high energy demands of consensus mechanisms can impede smooth implementation. To resolve these, using a hybrid blockchain structure, incorporating edge computing for real-time data processing, and selecting more energy-efficient consensus algorithms could help mitigate these technical hurdles.
Organizational resistance is another potential barrier, often due to concerns about privacy, the complexity of adopting new technology, and disrupting established workflows. To address this, providing thorough training for healthcare providers, demonstrating the security advantages of blockchain, and introducing the system gradually can ease the transition. Gaining early support from key stakeholders and showcasing.
Telemedicine and healthcare have the widest data variety amongst most of the present day applications, and is also considered the most trust deficit due to the sensitive nature of the data. Blockchain solutions can be considered for data storage in such applications, but no single solution can possibly fit the diverse needs of healthcare data, and thus a storage decision framework has been proposed in this paper, with due validations.
An implementation of the framework is due yet, and should be coincided with measurement of performance characteristics including queries, runtime and real time use cases of data storage and retrieval. New system improves patient care outcomes will also help in reducing resistance and fostering acceptance.
In emergency medical situations, the need for rapid access to patient data is essential, but it must be managed alongside the need for security to protect sensitive information. To address this, the framework could introduce a special protocol for emergency access, allowing healthcare professionals to access critical data quickly while ensuring that security is not compromised. This can include time-sensitive access permissions, ensuring that emergency responders are granted temporary access to necessary medical information. Role-based access controls and multi-factor authentication could further ensure that only authorized individuals are granted access. Additionally, it is important to maintain encryption during emergency access to preserve data confidentiality, with automatic logging for auditing and compliance purposes.
The framework could be enhanced by implementing fine-grained access control using smart contracts, which would allow the definition of specific rules for each participant. These smart contracts would set clear permissions on who can view, modify, or share data, based on their roles or authority level. By embedding these access control mechanisms directly within the blockchain, it ensures secure, transparent, and efficient management of sensitive medical information while maintaining strict data privacy.
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