Blockchain for smart healthcare: A systematic review of security,interoperability,and AI–IoT integration

Focus areas

This review is structured around the following thematic objectives related to the adoption of blockchain in healthcare:

To assess the use of blockchain for strengthening EHRs, clinical decision and disease prediction systems, remote patient monitoring, and the integration of data from IoT-enabled and wearable devices.

Purpose

The objectives of this review are as follows:

To synthesize recent blockchain developments in smart health systems, focusing on applications published between 2019 and 2025.

Scope limitations

The review will concentrate on recent advances in blockchain-enabled healthcare solutions, with particular emphasis on studies published within the past seven years, in order to capture current trends, emerging architectures, and state-of-the-art implementations. The scope is intentionally limited to healthcare related applications of blockchain and will not cover non-healthcare directions except where such use cases, pharmaceutical supply chain management, have a direct bearing on medical, clinical, or health information systems.

This review makes three distinct contributions:

First, it provides a structured synthesis of 26 peer-reviewed studies on blockchain-enabled healthcare, systematically addressing security, interoperability, privacy, and data integrity challenges.

By addressing these dimensions, the review is intended to serve as a reference point for researchers, system architects, and policymakers seeking to design secure, patient-centric, and interoperable healthcare infrastructures.

Figure 1 maps to organization of the article as follows: the “Introduction” section establishes the comprehensive overview contribution; the “Methodology” section demonstrates the methodological rigor; the “Overview of blockchain in healthcare” section and the “Advancement in smart health systems” section address the critical analysis contribution; the “Security Issues in blockchain for smart health systems” section provides the security-focused evaluation; the “Challenges and limitations”section identifies the adoption barriers; and the “Future directions and recommendations” section delivers the research gaps and mitigation strategies contribution, finally, the “Conclusion” section concludes the work. This organizational structure ensures each section directly supports our stated contributions to the literature.

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Figure 1.

Organization of the article showing section by section details.

Comprehensive overview of blockchain in healthcare

This review provides an up-to-date and structured examination of blockchain applications in healthcare, highlighting recent developments and research focus areas. It clarifies how blockchain can strengthen security, support interoperable data exchange, and enable patient centered data governance, thereby demonstrating its potential to deliver meaningful improvements to digital healthcare systems.

Critical analysis of blockchain’s role in healthcare

The review offers the following key insights:

Data integrity and security: Blockchain can enhance protection of electronic health information by ensuring that records are tamper evident, cryptographically secured, and fully auditable, which preserves accuracy and reliability of clinical data over time. ²⁸

Addressing challenges and barriers to blockchain adoption

This review also identifies key obstacles that must be addressed for blockchain to achieve sustainable and large scale adoption in healthcare:

Scalability limitations: Current blockchain platforms often face performance constraints when processing high volumes of clinical and sensor-generated data, which complicates their deployment in nationwide or multi-institutional health networks.

Real-world case studies and practical implementations

This review shows how blockchain is already making a difference in healthcare. These cases highlight improvements in securing patient information, managing data, and delivering more patient centered care. The review also explores:

Blockchain-based EHR systems, real-world implementations of EHR systems secured by blockchain, ensuring data integrity and patient consent.

Decentralized clinical trials, how blockchain is used in managing clinical trials, improving transparency and data tracking across multiple sites.

Blockchain for medical supply chain management, practical applications of blockchain to increase validity of pharmaceutical products and medical devices.

Exploring blockchain’s future potential in healthcare

This review expands literature by proposing future directions for blockchain technology in healthcare. It explores trends and potential areas for innovation:

Integration with other technologies: How blockchain can work with other technologies like AI, IoT, and big data analytics to create more robust smart health systems.

Personalized healthcare: Blockchain’s role in allowing personalized healthcare through secure and transparent access to patient data, allowing for better tailored treatments and preventive measures.

Blockchain for global health initiatives, exploring how blockchain can address global health challenges, improving healthcare access in remote or underdeveloped areas and managing healthcare data on a global scale.

Filling gaps in existing literature

This review makes a significant contribution by filling gaps in existing research, particularly in the following areas:

Comprehensive coverage: The review provides a broad view on blockchain in healthcare, going single use case studies to provide a comprehensive view of technology’s applications and challenges.

Focus on security and privacy: While many studies explore blockchain’s potential, few focus on specific security and privacy concerns that arise in healthcare. This review places a strong focus on importance of these issues and how they can be addressed.

Regulatory and ethical frameworks: The review contributes to literature by discussing ethical and regulatory challenges that must be considered when implementing blockchain in healthcare systems, an often looked at different angle of blockchain research.

Proposals for future research and development

This review contributes by suggesting avenues for future research:

Scalability solutions: Proposing research on how to increase scalability of blockchain systems to handle large-scale healthcare data efficiently.

Legal and regulatory frameworks: Recommending development of legal frameworks that align with blockchain’s decentralized nature while following with healthcare data privacy regulations. Blockchain for global health: Exploration of blockchain’s potential for addressing global health challenges, particularly in underserved regions where healthcare access is limited.

By providing a detailed review of current state of blockchain in healthcare, this work contributes to growing body of knowledge on blockchain’s transformative role in smart health systems. It not only analyzes the state of current research but also proposes practical solutions and identifies gaps that need further exploration

Inclusion criteria

The following criteria were used to include studies in the review:

Peer reviewed journal articles, conference papers, and research reports.

Exclusion criteria

The following studies were excluded from the review:

Studies that focus on blockchain technology outside the healthcare sector.

These inclusion and exclusion criteria help ensure that selected studies are relevant, high quality, and recent.

Disease prediction and early detection

Blockchain’s decentralized and immutable architecture offers a powerful foundation for advancing disease prediction and early detection efforts. Through facilitating secure, transparent, and inference resistant sharing of patient data across institutions, blockchain addresses critical issues related to data integrity, privacy, and interoperability.^40,41

Blockchain allows the secure aggregation of diverse health data sources including electronic medical records, genomic sequences, lifestyle information, and data from wearable devices into a unified, inference proof system. ⁴² This comprehensive dataset can then be analyzed using advanced AI and ML algorithms to predict the onset of chronic conditions like diabetes, cardiovascular diseases, and various forms of cancer. Early and accurate disease detection enabled by this integrated approach allows healthcare providers to implement timely interventions, ultimately improving patient outcomes and reducing the burden of long-term disease management.^43,44

Leveraging blockchain to store predictive models and outcomes data can facilitate secure collaboration among medical professionals across institutions. By enabling transparent and tamper resistant access to critical insights, blockchain promotes cross institutional sharing of predictive analytics, thereby enhancing the scalability and effectiveness of early disease detection efforts at a population level. ⁴⁵

Blockchain enables privacy preserving collaborative AI through three mechanisms. Federated learning (FL) allows hospitals to train local models on private datasets while sharing only encrypted gradient updates via blockchain MedChain-FL deployed across eight European oncology centers achieved 94.3% breast cancer detection accuracy on 8.4M mammograms with zero breaches, though 4.5 slower than centralized training.^46,47 Homomorphic encryption enables computation on encrypted genomic data; GenomeChain pilot (125K genomes) achieved 89.7% rare disease prediction with perfect privacy but 31 slower processing (4.2 hours/genome).^48,49

Blockchain–AI integration imposes 10–30 computational overhead and 0.8%–5% accuracy penalty compared to centralized systems, but enables GDPR-compliant collaborative research previously impossible.^50,51 Trade-offs are acceptable for non-emergency diagnostics where privacy requirements outweigh speed. However, limitations remain, 4-hour processing unsuitable for acute care, FL requires ≥ 5000 local samples (excluding small rural hospitals), and homomorphic encryption’s black-box nature complicates FDA medical device approval. ⁵²

EHRs analysis

The management of EHRs has significant challenges in healthcare, particularly concerning data privacy, security, and interoperability. Traditional centralized systems often struggle with fragmented data, limited patient control, and vulnerability to breaches. ⁵³ Blockchain technology offers a transformative approach to EHR management by enabling a secure, decentralized framework for storing and sharing patient records across diverse healthcare providers. Its immutable and transparent ensures that data remains tamper proof while allowing patients and authorized clinicians seamless, real time access thereby improving care coordination, patient trust, and overall system efficiency. ⁵⁴

The main advantages of blockchain in this context is its ability to address data fragmentation persistent challenge wherein patient records are dispersed across multiple hospitals, clinics, and healthcare providers. Rather than relying on vulnerable centralized databases, blockchain facilitates a distributed and decentralized approach to data management. ⁵⁵

Blockchain-based EHR systems also offer patients more control over their health data. Blockchain’s transparency ensures that every transaction is recorded, providing an audit trail that can help identify any discrepancies or unauthorized access. ⁵⁶

Different blockchain architectures present distinct trade-offs for EHR management. “Permissioned blockchains” like Hyperledger Fabric) offer superior transaction throughput (3000–5000 TPS) and energy efficiency, making them suitable for institutional deployments where participants are known and trusted.^17,54 However, they sacrifice the trustless nature of public blockchains and require governance frameworks to manage access control.

Ethereum provide greater transparency and censorship resistance but face significant scalability limitations (15–30 TPS) and higher transaction costs ( 2–50 per transaction during peak periods). ⁵⁷ Recent studies by Reegu et al. ⁵⁴ demonstrated that hybrid blockchain models combining private data storage with public audit trails achieved a balance, maintaining privacy while ensuring verifiability. However, their implementation revealed integration challenges with existing HL7 FHIR standards, requiring custom middleware solutions that increased system complexity by ∼ 40%.

Performance comparison across studies shows considerable variation, Al Mamun et al. ¹⁷ reported 98.7% data integrity preservation with 2.3-second average latency, while Ferreira et al. ⁵⁵ achieved 99.2% integrity but with 4.1-second latency due to enhanced cryptographic protocols. This suggests a direct trade-off between security strength and system responsiveness a critical consideration for time-sensitive clinical applications like emergency care.

For non-technical decision-makers, the choice reduces to three trade-offs, speed versus decentralization: permissioned blockchains (Hyperledger) process 3000–5000 transactions/second but require trusted institutions. Public blockchains (Ethereum) process only 15–30 transactions/second but need no central authority. Cost versus security: permissioned systems have lower transaction costs ($0.01–0.10) but centralized governance risks. Public systems cost $2–50 per transaction but provide censorship resistance. Privacy versus transparency: permissioned blockchains keep data private by default. Public blockchains require additional privacy layers (zero-knowledge proofs (ZKPs)), adding 150%–200% computational overhead.

Most healthcare institutions choose permissioned blockchains, accepting governance complexity for superior performance and privacy.

Remote nonitoring and wearable device ontegration

Blockchain technology is particularly well suited to support the expanding field of RPM and the integration of wearable health devices. ⁵⁸ Blockchain offers a decentralized, secure infrastructure that can validate and protect this data while validating it is accessible to authorized healthcare professionals. ²⁴

Blockchain technology enables the secure storage and exchange of data generated by wearable health devices through the creation of a decentralized, tamper resistant ledger. This infrastructure ensures data integrity and trustworthiness, which are essential in clinical decision making and long-term health monitoring. ⁵⁹ Healthcare providers can gain real-time access to patient generated health data, facilitating timely interventions, treatment adjustments, and continuous management of chronic conditions. Importantly, blockchain also empowers patients by giving them granular control over their data allowing them to selectively grant access to healthcare professionals, researchers, or institutions, thereby preserving privacy while enhancing transparency and autonomy in car. ⁶⁰

Figure 4 expressing the environment of patient health conditions with devices integrated with blockchain. The integration of blockchain with IoT devices, like wearable health monitors, enables the creation of a secure and interoperable healthcare ecosystem in which devices can autonomously and reliably transmit data to healthcare providers. This validates that patient information remains accurate therefore supporting real-time clinical decision making and increasing the continuity and quality of care. ⁶¹

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Figure 4.

A remote health monitoring system where wearable body sensors transmit physiological data to medical devices, which is then visualized on a dashboard and accessed by healthcare professionals in real time.

Enhanced security and privacy

One of the most significant advantages of blockchain technology in healthcare is its ability to enhance the security and privacy of sensitive patient data. Traditional healthcare information systems are often centralized, making them more susceptible to cyberattacks, and data breaches that can compromise patient confidentiality. ¹⁷ In contrast, blockchain employs advanced cryptographic techniques to encrypt health data, validating that it is stored securely and accessed only by authorized parties. ⁶³ Each data entry is timestamped and recorded on an immutable ledger, where it cannot be altered or deleted without reaching consensus across the distributed network. This tamperresistant architecture provides robust protection against data manipulation and fosters greater trust among patients and providers regarding the integrity and confidentiality of medical records. ⁶⁴

By decentralizing healthcare data storage, blockchain reduces the risks associated with centralized databases, which are attractive targets for hackers. Blockchain’s decentralized nature means that even if one part of network is compromised, the integrity of the overall system remains intact. Blockchain allows patients to have greater control over their personal data by using cryptographic keys to manage who has access to their health information.^15,62

Transparency and trust

Blockchain’s inherent transparency ensures that all transactions and data exchanges are recorded in a public ledger, accessible to all authorized parties. This feature is particularly valuable in healthcare, where trust is critical. Blockchain allows for transparent tracking of medical procedures and medical supply chains, creating an audit trail that can be traced back to its origin. ⁶⁵ In clinical trials, blockchain can ensure that data generated throughout the study is securely recorded and remains unaltered. The transparency of the blockchain also promotes accountability and helps prevent fraud, like falsifying patient records or clinical trial data. ³⁹

In terms of EHRs, blockchain enables patients to see exactly who has accessed their medical data, when, and why. This promotes trust between patients and healthcare providers, knowing that access is transparent and accountable. ⁶⁶

Improved interoperability

Healthcare systems across the globe face significant challenges in terms of data interoperability. Different healthcare providers often use incompatible systems that prevent seamless data exchange. Blockchain can address these challenges by providing a standardized, decentralized platform for securely storing and sharing patient information. ⁶⁷

Blockchain allows for creation of a universal health record that can be accessed by any authorized healthcare provider, regardless of the software or system they are using. This enables healthcare professionals to access a comprehensive, update record of the patient’s medical history, improving the efficiency of care coordination and reducing errors that arise from incomplete or inaccurate information. ⁶⁸ Its ability to connect various healthcare entities like hospitals, clinics, insurance companies, and pharmacies, ensures that patient data flows smoothly across systems, improving overall healthcare delivery. ⁶⁹

Cost reduction

The integration of blockchain technology into healthcare systems has the potential to generate substantial cost savings by eliminating intermediaries, reducing administrative overhead, and enhancing operational efficiency. ⁷⁰ Blockchain’s decentralized architecture removes the need for such centralized intermediaries by enabling direct, trustless interactions among patients, and insurers. Through the use of smart contracts and automated validation mechanisms, blockchain can streamline processes like health data exchange, accelerating workflows and reducing transaction costs across the healthcare ecosystem. ⁷¹

Blockchain can help reduce fraud and errors in healthcare by validating the integrity and authenticity of medical data. By securely tracking medical supplies and transactions, blockchain can minimize the incidence of counterfeit drugs and fraudulent claims, resulting in lower overall healthcare costs. ⁷²

Key applications of IoT in healthcare

Remote patient monitoring: IoT devices like wearables can continuously monitor vital signs like heart rate, blood pressure, oxygen levels, and glucose. This data is transmitted in real time to healthcare providers, enabling them to monitor patients’ conditions remotely. Patients with chronic conditions like diabetes can wear glucose monitors that provide real time data to their healthcare providers, allowing for timely intervention. ⁷³ Smart medical devices: Smart medical devices like infusion pumps, pacemakers, and insulin pumps are connected to healthcare networks. These devices can adjust parameters autonomously based on the data they collect from patients, reducing human error and improving patient outcomes. Insulin pumps can automatically adjust insulin delivery based on realtime glucose readings from continuous glucose monitors. ⁷⁴ Telemedicine and virtual care: IoT enabled telemedicine platforms allow patients and healthcare providers to interact remotely through video consultations, diagnostic assessments, and treatment monitoring. Remote consultations, combined with IoT monitoring, have become increasingly important in providing healthcare to patients in rural or underserved areas. ⁷⁵

Benefits of IoT in healthcare

The integration of IoT in healthcare systems brings several benefits: Real-time monitoring and early detection: IoT devices enable continuous monitoring of patient conditions, leading to early detection of potential health issues like irregular heartbeats or oxygen saturation levels, allowing for quicker intervention. ⁷⁶ Improved patient engagement: Wearables and mobile health apps empower patients to take an active role in managing their health, thus improving adherence to prescribed treatments and enhancing health outcomes. ⁷⁷ Operational efficiency: IoT technologies optimize hospital operations by automating data collection, improving inventory management, and streamlining workflows. ⁷⁸

Key applications of AI and ML in healthcare

AI is playing a transformative role in various aspects of healthcare. In disease diagnosis and prediction, deep learning algorithms are being used to analyze medical images like X-rays, CT scans, and MRIs to detect conditions like cancer and tuberculosis. ML models also help predict the risk of developing certain diseases by analyzing patient data, including genetic information and lifestyle factors.^79,80 In the area of personalized medicine, AI-powered systems can assess an individual’s genetic profile, medical history, and lifestyle to design customized treatment plans tailored to their unique needs. ⁸¹ Clinical Decision Support Systems (CDSS) further enhance care by providing healthcare professionals with data-driven recommendations. These systems use predictive analytics and historical data to guide clinical decisions and improve patient outcomes. ⁸² Predictive healthcare uses AI and ML to anticipate disease outbreaks, monitor patient deterioration, and predict hospital readmissions—allowing for timely and proactive medical intervention.^83,84

Benefits of AI and ML in healthcare

The application of AI and ML in healthcare offers numerous benefits: Enhanced accuracy and speed: AI models can process vast amounts of data much faster than human practitioners, improving diagnostic accuracy and enabling faster decision making. ⁸⁵

Cost efficiency: By automating routine tasks like data entry, image analysis, and medical coding, AI can significantly reduce administrative overhead, leading to cost savings. ⁸⁶ Improved patient outcomes: AI and ML allow for early detection of diseases and the creation of personalized treatment regimens, improving health outcomes and reducing mortality rates. ⁸⁷

Key applications of blockchain in healthcare

Secure health data sharing: Blockchain offers a tamper-proof way to store patient records, making sure all involved parties have access to reliable and secure information. A patient’s EHR can be safely stored on the blockchain, allowing approved healthcare providers to access it from anywhere while ensuring the data has not been altered without permission. ⁸⁸ Smart contracts for healthcare transactions: blockchain-based smart contracts are used to automate healthcare transactions, that is, billing, insurance claims, and payments. For example, smart contracts can automate the insurance claim process by validating that payments are made only when certain conditions are met. ⁸⁹ Clinical trials and research: blockchain brings transparency and accountability to clinical trials by keeping all data on a permanent, unchangeable record. It allows researchers to share trial data securely, while patients can follow the progress of the trials they’re involved in helping build trust and ensure ethical standards are met. ⁹⁰

Benefits of blockchain in healthcare

Table 5 shows the advancement in blockchain in the area of smart health, as well as showing the benefits of AI and specifically ML in smarthealth. The use of blockchain in healthcare provides several key benefits: enhanced data privacy and security: blockchain ensures that patient data is encrypted and stored in a decentralized manner, reducing the risk of data breaches and unauthorized access. ⁹¹ Transparency and trust: blockchain provides a transparent and immutable ledger of all healthcare transactions, enhancing trust among healthcare providers, patients, and insurers. ⁹² Reduced fraud and errors: blockchain’s tamper resistant nature helps prevent fraud like the falsification of medical records, and reduces errors in healthcare billing and insurance claims. ⁹³

Adversarial disturbance and evasion attacks

Adversarial interference attacks involve inserting false or misleading information into the blockchain. In healthcare, this might include tampered medical records or fake clinical trial data. An attacker could change a patient’s medical history to make them seem healthy when they actually have a serious condition. ¹⁰⁷

A malicious actor might launch an attack to influence blockchain consensus decisions or fake records by taking advantage of weaknesses in how transactions are verified. In healthcare, this could lead to dangerous changes like modifying treatment plans or medication doses putting patient safety at serious risk. ¹¹⁷

Mitigation techniques for adversarial attacks

Enhanced consensus mechanisms: More robust methods like Practical Byzantine Fault Tolerance (PBFT) or proof of stake (PoS) help protect the blockchain by requiring multiple network nodes to agree before a transaction is approved. ¹⁰⁸

Anomaly detection: Using ML-based systems to detect unusual patterns can help spot suspicious activity or harmful transactions. If there’s an unexpected change in a patient’s data, the system can flag it and notify healthcare providers, helping prevent data tampering or misuse. ¹¹⁸

Robust encryption: Applying strong encryption methods like homomorphic encryption or advanced cryptography helps keep data safe, even if it’s intercepted during transmission. This ensures that sensitive health information stays unreadable and secure from hackers or other malicious threats. ¹¹⁹

Types of data manipulation attacks

Data poisoning: Data poisoning is a type of attack where false or corrupted data is intentionally added to the blockchain to damage the integrity of the information. In healthcare, this can be specially dangerous. For example, if an attacker alters a patient’s record to remove a known drug allergy, it could result in the patient being given a harmful medication, possibly causing life threatening reactions. Since blockchain records are permanent and cannot be changed, poisoned data stays in the system. This highlights the need for strong data validation, strict access control, and effective anomaly detection before any data is added to the blockchain. ¹⁰⁹

Double spending attack: While often linked to cryptocurrencies, double-spending can also affect healthcare blockchains. In this case, an attacker might try to reuse the same insurance information to pay for multiple treatments. This kind of manipulation can lead to fraud or financial losses for healthcare providers and insurers. ¹¹⁰

Mitigation techniques for data manipulation

Several measures can be taken to prevent data manipulation attacks:

Strict validation protocols: Applying strong consensus methods like proof-of-work (PoW) or PoS ensures that new transactions are only approved when most of the network agrees. This makes it much harder for attackers to make unauthorized changes or tamper with the data. ¹²⁰

Smart contracts for data verification: Smart contracts on the blockchain can automatically check data for accuracy before it’s added. Patient records can be verified using preset rules to make sure the information is correct and trustworthy before being stored on the blockchain. ¹²¹

Data provenance: Keeping track of where healthcare data comes from and how it changes over time helps ensure transparency. Every update to a patient’s record is recorded and traceable, making it easier for healthcare providers to spot any unauthorized or suspicious modifications. ¹²²

Techniques to minimize model inversion

To mitigate model inversion risks, several techniques can be applied: differential privacy protects individual patient identities by adding a small amount of random noise to the data before it is used by ML models. This technique enables the model to identify general patterns and make accurate predictions like forecasting disease outcomes without revealing any specific patient’s information. ¹²³ Another key privacy preserving method is homomorphic encryption, which allows computations to be performed directly on encrypted data without the need for decryption. This ensures that even if an unauthorized party accesses the blockchain, they cannot retrieve meaningful health information. Healthcare providers can analyze encrypted patient data securely while maintaining complete confidentiality. ¹¹² Integrating FL with blockchain enhances data security by keeping sensitive patient data on local devices. Instead of sharing raw data, only encrypted model updates are transmitted via blockchain, allowing collaborative learning across multiple sources while preserving privacy and security. ¹²⁴

Secure aggregation methods

Several secure methods can be used to ensure privacy and integrity during model aggregation in healthcare applications. Multiparty computation (MPC) enables multiple parties to collaboratively compute an aggregate model without revealing their individual private data, thus preserving confidentiality throughout the process. ¹²⁵ Encrypted aggregation further enhances security by allowing data to be aggregated in encrypted form. Even during training, individual records remain protected and can only be accessed by authorized users with the appropriate decryption keys making it specially suitable for sensitive healthcare data. ¹²⁶ Alockchain technology can be utilized to ensure trustworthy aggregation by recording each step as a transaction on the ledger. This guarantees transparency, immutability, and accountability, helping verify that the aggregation has not been tampered with. ¹¹³

Privacy-enhancement techniques

Several privacy-preserving techniques can increase security of blockchain-based health systems: ZKPs: ZKPs allow one party to prove to another party that a statement is true without revealing any additional information. Like ZKPs can be used to prove that a patient is eligible for treatment without revealing their exact diagnosis or treatment history. ¹¹⁴ Decentralized identifiers (DIDs): DIDs are a new form of digital identity that can be used in healthcare to protect patient data. DIDs allow individuals to control their personal information and selectively share it with authorized parties. ¹¹⁵

Blockchain with FL

Integrating blockchain with FL allows healthcare systems to collaborate on training ML models while validating that patient data never leaves the local nodes. Blockchain ensures that only secure and validated model updates are aggregated, while FL keeps sensitive data localized, validating both data privacy and security. ¹²⁷

Challenges in data privacy

Transparency versus privacy: Blockchain’s transparency feature makes it difficult to ensure full privacy, specially when dealing with sensitive patient data. While the data itself is encrypted, the metadata like the time and identity of the transaction, can sometimes be exposed, potentially compromising patient privacy. ¹³² Regulation of sensitive data: healthcare data is often subject to stringent regulations like HIPAA in the US and GDPR in the EU. Blockchain’s immutable and decentralized nature may conflict with these regulations, particularly when it comes to the right to be forgotten, the ability for patients to request the deletion of their data. ¹³³

Challenges in scalability

Transaction latency: blockchain networks like Bitcoin and Ethereum experience latency issues, with transaction verification times often taking several minutes or even longer. In healthcare, delays in transaction processing could lead to delays in providing timely medical services, which could adversely impact patient outcomes. ⁵⁷ Network congestion: as blockchain networks become more widely adopted, they may face network congestion due to the sheer volume of transactions. This could significantly impact performance, specially in large scale healthcare applications. ²⁰ Cost of scaling: the cost associated with scaling blockchain networks can be prohibitive. Maintaining and expanding a blockchain network requires significant computational power, leading to high operational costs, particularly in healthcare systems that require fast processing of large datasets. ⁷

Empirical evidence reveals significant disparities in scalability solutions. Layer-2 solutions, like state channels and sidechains have shown promise in laboratory settings, with projects like Raiden network demonstrating 1000+ TPS for Ethereum-based healthcare applications. ⁵⁷ Real-world deployment studies indicate that only 23% of healthcare institutions successfully implemented these solutions without major system redesigns. ²⁰

Challenges in integration

Data interoperability: one of the biggest challenges in integrating blockchain with existing healthcare systems is validating that data can be seamlessly exchanged between blockchain based platforms and traditional EHR systems. Blockchain data structures may not align with the data formats used by legacy systems, making integration complex and costly. ⁵⁴ Resistance to change: healthcare providers and institutions are often resistant to adopting new technologies due to the high costs and disruptions caused by implementation. Many organizations are hesitant to invest in blockchain based solutions, particularly if they involve overhauling existing systems or require specialized expertise. ¹³⁴ Data standardization: healthcare data is often unstructured and exists in a variety of formats. For blockchain to be effective in healthcare, standardization of data formats and protocols is necessary. However, achieving universal standards across healthcare providers, payers, and regulators is a difficult task. ¹³⁵

Challenges in regulatory compliance

Data ownership and control: in healthcare, data ownership is a complex issue. While patients may own their health data, healthcare providers and other stakeholders also have access to it. Blockchain’s decentralized nature could make it difficult to establish clear lines of ownership and control, specially when dealing with the sharing of patient data across multiple organizations. ¹³⁸ Lack of standardized regulations: healthcare blockchain solutions must adhere to various national and international regulations, like HIPAA, GDPR, and others. The lack of universally accepted standards for blockchain in healthcare complicates the adoption process and creates legal uncertainties for organizations. ¹³⁹ Liability issues: the decentralized and immutable nature of blockchain means that once data is recorded, it cannot be changed. However, in healthcare, errors in data can have serious consequences. Figure 5 shows the audit process for healthcare system for patient and doctor with institutes. Establishing who is liable when a mistake occurs like inaccurate patient data or an incorrect diagnosis is a significant legal challenge. ¹²⁹

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Figure 5.

Blockchain audit process for smart healthcare.

Challenges in energy consumption

High operational costs: blockchain networks require a vast amount of computational power to validate transactions. This leads to high electricity consumption, contributing to increased operational costs. For large healthcare organizations with massive amounts of data to process, the energy costs associated with running blockchain systems could be a significant burden. ¹⁴⁰ Sustainability concerns: the environmental impact of blockchain technology, particularly in terms of carbon emissions, is a growing concern. As the healthcare industry increasingly adopts blockchain solutions, addressing sustainability through the development of more energy efficient consensus algorithms like proof of stake becomes essential. ¹³⁹

Challenges in blockchain awareness and education

Lack of expertise: healthcare providers and administrators may lack technical expertise required to implement and manage blockchain systems. This knowledge gap can prevent them from understanding how blockchain can be applied to their specific healthcare settings. ¹³⁶ Patient education: patients may not fully understand how blockchain can protect their health data, leading to skepticism about its adoption. For blockchain to be successful in healthcare, it is important to educate both healthcare professionals and patients about its benefits and limitations. ¹³⁷

Recommendations for improving data privacy

To protect personal data while maintaining the openness of blockchain, privacy enhancing technologies, ZKPs, and homomorphic encryption can be used. In the healthcare sector, permissioned blockchain networks offer a practical solution by allowing only authorized users to access sensitive patient data. This approach ensures better privacy control while preserving the key benefits of blockchain technology. Looking ahead, a significant advancement will be the adoption of decentralized identity systems. These systems empower patients to manage their own health information, enabling them to grant or revoke access as needed. This not only increases patient control but also strengthens trust in blockchain-based healthcare solutions.

Recommendations for improving scalability

Blockchain systems can be made more efficient by adopting alternative consensus mechanisms like PoS, delegated PoS (DPoS), or PBFT. These methods are significantly more energy efficient and capable of handling a higher volume of transactions compared to traditional PoW systems, making them wellsuited for the demands of high volume healthcare environments. scalability can be enhanced through sharding technique that breaks the blockchain network into smaller, more manageable segments. By allowing transactions to be processed in parallel, sharding speeds up validation and boosts the overall performance of the blockchain.

Layer-2 solutions, state channels and sidechains can reduce end-to-end latency from 4.1 seconds to less than 1 second while increasing throughput to above 1,000 TPS, as reported by Ejaz et al. ⁵⁷ This makes them suitable for real-time and emergency-care blockchain applications. Likewise, replacing PoW with a more communication efficient consensus like PBFT can increase throughput to ∼ 3000+ TPS, ¹¹⁷ directly addressing the network congestion discussed in the “Scalability and transaction speed issues” section. Although this shift slightly reduces decentralization, the trade-off is acceptable in permissioned healthcare networks where participating entities are verified institutions. In contrast, while sharding offers near-linear scalability in theory, cross-shard transactions introduce about 300%–500% latency overhead in multi-department hospital settings. ⁵⁷ For this reason, sharding is recommended only for single-department or pilot deployments before being adopted in larger, fully integrated healthcare system.

Recommendations for seamless integration

Develop interoperable standards: Establishing standardized protocols and data formats for blockchain-based healthcare systems will be essential for seamless integration. Collaboration between healthcare providers, software vendors, and blockchain developers can lead to the creation of common data formats, interfaces, and standards that facilitate interoperability between blockchain networks and existing healthcare systems.

Modular blockchain solutions: Blockchain solutions should be designed to be modular, allowing them to be easily integrated with different components of the healthcare ecosystem. By allowing healthcare organizations to adopt blockchain incrementally, it becomes easier for them to integrate blockchain-based technologies without completely overhauling their existing systems.

Hybrid blockchain models: Hybrid blockchains that combine both private and public chains can offer a viable solution for integrating blockchain with legacy systems. In these models, sensitive patient data can be stored in private chains, while non-sensitive information or transaction records can be stored on public chains, enabling privacy and security while validating accessibility and transparency.

Recommendations for regulatory adaptation

To ensure the safe and effective use of blockchain in healthcare, regulators must establish clear legal guidelines. These should define who owns the data, how it is managed and shared on blockchain systems, and ensure compliance with privacy laws, HIPAA. Given the global nature of healthcare, international collaboration is also essential countries should work together to develop common standards for blockchain use. Governments can support innovation by providing regulatory sandboxes, which are controlled testing environments that allow healthcare organizations to experiment with blockchain technologies without violating existing regulations.

Research directions

Blockchain-based telemedicine: The integration of blockchain with telemedicine platforms can improve the security, transparency, and accountability of remote consultations. Research into use of blockchain for telemedicine can help ensure that patient data is securely stored and shared during virtual consultations.

Blockchain for clinical trials: Blockchain has the potential to revolutionize clinical trials by providing transparent, immutable records of trial data, reducing the risk of fraud, and validating the integrity of trial results. Future research should explore how blockchain can be applied to clinical trials, from patient recruitment to data monitoring.

Interdisciplinary collaborations: Collaborations between blockchain experts, healthcare professionals, and regulators will be essential to designing effective blockchain-based solutions. Future research should foster interdisciplinary collaboration to address unique challenges of blockchain adoption in healthcare.