Introduction

The healthcare industry generates, accesses, and distributes enormous amounts of data daily1. Isolated patient records lead to fragmentation and a range of issues from ineffective care coordination to missing vital information in an emergency2. As a solution, interoperability standards were implemented; nonetheless, a system that facilitates thorough integration of patient records is still unattainable3. Additionally, Health Information Technology described how technology providers and suppliers restrict patient access through a practice that has subsequently been standardized as information blocking in a Congressional report4. Consequently, patient data continues to be fragmented, and obstacles put up by data holders discourage patient participation and information sharing, which leads to the loss of agency5. Integrating blockchain technology with officially recognized interoperability standards has been proposed as a workable resolution to the issues above6. Blockchain is a distributed ledger network that allows entries to be added through a consensus mechanism, typically requiring a majority vote, and prevents removal to ensure immutability7.

Each block in a blockchain gets a unique hash value based on its contents. This hash value connects to previous blocks, creating an unbreakable chain. If someone tries to alter any information, the hash would no longer match, revealing the tampering8. All network users can access and rely on the data because it is not handled in a centralized location due to the distributed Blockchain ledger architecture9. The decentralized system is strengthened and secured because there is no chance of a single attack10. Reducing medical practice and monitoring by double helps improve control over health data and patient care while saving time and money for both patients and practitioners11. By maintaining medical records on a blockchain, the patient can monitor where the information is going and take action12. Healthcare systems are racing to keep up with our growing medical needs. As patients, we are seeing doctors and hospitals rush to adopt new technologies faster than ever before13. The requirement is for superior health facilities backed by modern and newer technology. The Blockchain would be crucial in revolutionizing the healthcare business14.

The health system landscape is shifting towards a patient-centered approach concentrating on two primary aspects, accessible services and suitable healthcare resources at all times15. The Blockchain enables healthcare organizations to deliver enough patient care and high-quality health facilities16. Health Information Exchange is another time-consuming and repetitive activity contributing to high health sector expenditures, which are quickly sorted out with this technology17. Using Blockchain technology, individuals may take part in health research programs. More research and shared data on public welfare will increase therapy for diverse groups18. A decentralized database administers the whole healthcare system and organizations19. When used in the healthcare industry, blockchain smart contracts provide a trustworthy technological framework for consistent data, which improves treatment quality, facilitates easy communication between doctors and patients, and ultimately leads to better results20.

Adversaries targeting Electronic Health Record (EHR) systems typically include cybercriminals, malicious insiders, and state-sponsored hackers. Cybercriminals aim to exploit weaknesses in the system to steal patient data for financial gain, such as selling stolen medical records on the black market or using them for identity theft and fraud. Malicious insiders, such as healthcare employees with legitimate access, may be motivated by personal grievances, financial incentives, or the desire to alter or leak sensitive data intentionally. State-sponsored actors may target EHR systems for espionage, seeking access to confidential health information for political or strategic advantages, or to disrupt healthcare systems for national security reasons. These adversaries exploit security gaps, such as weak access controls or outdated encryption protocols, to gain unauthorized access to sensitive medical data, potentially compromising patient confidentiality, data integrity, and the reliability of healthcare services.

However, typical blockchain frameworks that use basic hashing algorithms like SHA-256 provide immutability and integrity, yet could not be able to meet the dynamic, high-throughput, and context-sensitive access needs of current healthcare systems. Access to medical data typically depends on the role, the urgency, and the context of consent. These are variables that traditional, static hashing structures can’t rapidly manage. This framework uses a hybrid hashing strategy that combines SHA-256 with lightweight, entropy-aware hashing and dynamic hash chaining to obtain around these problems. This multi-layered approach provides both strong cryptography and the capacity to adapt to changing situations. It allows for context-aware access verification, better protection against hash collision and replay attacks, and reduced storage space needed through selective compression. The hybrid architecture makes sure that patient data is not just safe but also easy to access in a variety of clinical circumstances. It indicates that blockchain healthcare systems require progressing beyond monolithic hashing methods.

The main contributions of this article are:

  • Hybrid hashing mechanism We propose a novel cryptographic model that integrates SHA-256 with lightweight entropy-based dynamic hashing. This hybrid approach generates context-sensitive hash chains, enhancing resistance to collision, pre-image, and replay attacks.

  • Optimized data compression A selective, lossless compression method is embedded prior to hashing, which reduces storage usage by 27% and accelerates data retrieval by 35%, without compromising data integrity.

  • Role-based smart contract access control PDA-HIHM employs smart contracts to enforce decentralized, real-time access validation based on user roles. This approach ensures fine-grained permission enforcement and immutable audit trails.

  • Comprehensive security modeling Threat modeling and simulation validate the system against multiple attacks including hash collision, unauthorized access, and man-in-the-middle attacks. The framework achieved a 99.8% access control success rate and demonstrated zero hash collisions under brute-force testing.

  • Improved trust and transparency By integrating verifiable audit logs and introducing the Patient Trust Score (PTS), the system achieved a 97.62% increase in trust metrics, enhancing transparency and patient confidence in data handling.

The rest of this paper is structured as follows: Sect. 2 studies the related work of the hybrid integrated hashing method. In Sect. 3, the proposed methodology, PDA-HIHM, is explained. In Sect. 4, the efficiency of PDA-HIHM is discussed and analyzed, and finally, in Sect. 5, the paper concludes with future work.

Related works

We have introduced a blockchain system with a focus on patients Health Chain in a safe, interoperable setting which aims to improve patient participation, data curation, and controlled sharing of collected data. A hybrid blockchain is being considered to accommodate immutable logs and redactable patient blocks. Patient records are created and shared using Health Level-7 Fast Healthcare Interoperability Resources to facilitate smooth data transfer with compliant systems. Patients also get a digital identity comprising a public and private key pair. The blockchain is not an ideal place to store public keys; instead, it stores references or hashes to them, which may be used to secure and validate transactions. In the remaining content of this section, we present a comprehensive review of existing literature relevant to our study, organizing this survey into three distinct sections. We conclude the related work section with a comparative analysis that summarizes the existing methods.

Hybrid hash algorithms in secure healthcare data access

Zaman et al.21 delve into Deep Learning Techniques (DLT) into the specifics of the IoT ecosystem, including its security, performance, and development-stage supporting technologies. As a first step, it maps the security and performance advantages inferred by IoT technology to find areas lacking research. The second part of the article delves into real-world concerns surrounding integrating DLT devices. Third, it reviews some healthcare applications that use DLT in healthcare settings. It discusses the limits of enabling technologies, research gaps, and prospects.

Numerous areas of intelligent healthcare have used Big Data Analysis (BDA). Invading patients’ privacy and perhaps endangering their lives, these data are at risk of leakage or modification during transmission. Encrypted PHRs were the focus of several studies. Data leakage during deep learning and training models is an ongoing issue, and some users are concerned that the data may be compromised if it were to get into the wrong hands at the analytics company Ge, C. et al.,22.

The e-healthcare apps that save patient data are susceptible to security breaches. However, these attacks may be identified using these approaches, and hybrid models are necessary. Anand A. et al.23 propose a Convolutional Neural Network (CNN) that uses a classifier to identify malware assaults. Regarding smart applications, 5 G-IoT is now essential in e-health. To address the security risks associated with patients’ sensitive data, e-health applications need smart schemes and architectures.

Alkhdour et al.24 provide a new approach to these problems that combines blockchain security with sophisticated Fuzzy Logic Authentication (FLA) accuracy. It investigates how these state-of-the-art technologies improve healthcare system efficiency, privacy, and security by exploring this innovative merger. Digital healthcare that is safe and focused on the patient is about to enter a new age, and this innovative method might be the catalyst for that change.

Shen et al.25 utilize encrypted blockchain data to train Support Vector Machines (SVMs), protecting user privacy while leveraging the Internet of Things. Encrypting and storing IoT data on a distributed ledger using blockchain principles creates a safe platform for data interchange across many sources. Build secure polynomial multiplication and comparison blocks using the Paillier homomorphic cryptosystem. Second, SVM training must be secure, independent of other parties, and needs two interactions each iteration.

Particularly in accomplishing decentralization, tamper resistance, and anonymization within the domain of medical applications, the strengthening of the basic characteristics of blockchain technology depends on the integration of cryptographic algorithms such as Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC). Traditional AES and ECC attempts significantly compromise security and efficiency when handling broad medical data, compromising patient privacy efficiency26. This paper presents HAE (hybrid AES and ECC), a creative hybrid cryptographic algorithm that effectively combines AES’s reliability with ECC’s agility. HAE is intended to symmetrically encrypt original data using AES while using ECC for the asymmetric encryption of the initial AES key.

The idea of electronic health records (EHRs) for patient monitoring has become relatively common in the healthcare industry. This practical online tool allows patients to contact their medical professionals and request medical direction. Therefore, they suggested using hybrid encryption methods in this paper to protect the data transmitted over the EHR. A hybrid cryptographic method (HCT) makes use of the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC)27. Because it is slower and less efficient than symmetric encryption, asymmetric encryption is impractical for encrypting huge amounts of data. Hybrid encryption allows these limits to be overcome using symmetric and asymmetric encryption.

Blockchain-integrated frameworks for healthcare data protection

Secure data recording, transactions, and maintenance via smart contracts are just a few ways blockchain technology has found application in healthcare. Much effort has gone into enhancing blockchain applications by integrating Artificial Neural Networks (ANNs) to merge the two technologies’ greatest qualities. EHR management, remote patient monitoring and telemedicine, genomics, drug research and testing, specialized imaging, and outbreak prediction are just a few of the vital areas that have benefited greatly from the combination of blockchain and artificial intelligence. This review covers these technologies’ theoretical and practical sides, providing a solid grounding in both fields by Kumar, R. et al.28.

Raghav et al.7 designed a system to withstand criminal users and inquisitive but honest Cloud Computing (CC) with collaboration. Using a hybrid blockchain technology makes it possible to securely share data on a massive scale without relying on a central authority. Data stored in the cloud may be verified and authenticated by end users using blockchain technology and smart contracts. The privacy provisioning stage caused the OTRE scheme’s extended duration. It provides a secure implementation of KRE and OTRE and guarantees indistinguishability when subjected to chosen-plaintext attacks using a random oracle model.

The Machine Learning Algorithm (MLA) system, which focuses on patients, HealthChain, is introduced in this paper. In a safe, interoperable setting, we want to improve patient participation, data curation, and controlled sharing of collected data. Hylock, R. H. et al.29 provide immutable logging and redactable patient blocks, an MLA is suggested. Patient records are created and shared using Health Level-7 Fast Healthcare Interoperability Resources to facilitate smooth data transfer with compliant systems. Digital patient identification includes a public and private key. Blockchain public key storage optimizes transaction security and validity.

The Multi-Dimensional Privacy Attainment (MUDRA) architecture uniquely stores and retrieves HSP data to meet privacy issues30. The groundbreaking Dual Server Ring Private Information Retrieval (DS-RPIR) technology from MUDRA protects Personal Health Information (PHI) from the blockchain. Privacy is enhanced by preventing unauthorized entry. This technique has a throughput of 59 TPS, a decreased time complexity of \(\:O\left(nlogn\right)\), 8 ms of latency, 2.25 ms less, and a new measure called Privacy Attainment (PA) to assess private data retrieval and financial transaction secrecy. The Hyperledger Fabric network gives MUDRA a 3.5% advantage over previous methods.

A zero-watermarking approach to healthcare records authentication and security based on deep learning (DL-Z-WA) is introduced31. The hospital logo is virtually associated with the carrier picture to establish ownership and forestall unlawful copying and fraud. With zero watermarking based on NSST and SVD, no actual watermark is embedded, but rather the scrambled mark is linked to the characteristics of the carrier pictures that are marked. The suggested framework outperforms the state-of-the-art methods by as much as 47% in specific metrics of adaptability, durability, and imperceptibility.

Advanced computational models for healthcare efficiency

One method that has been suggested to make medical record document analysis more efficient is the Intuitionistic Fuzzy Trust Evaluation Method (IFTEM). It swiftly searches documents using the Latent Delicacy Allocation (LDA) topic model and extracts topic probability distributions from the target patient’s electronic medical data and other patients’ records32. The study proposes an intuitionistic fuzzy trust-based access control model; experimental findings show that it successfully detects user access behaviors 95% of the time and excessive user access behaviors 94.29% of the time.

FedCure is an intelligent IoMT-based healthcare application operating on a cloud-edge architecture33. Even when faced with heterogeneity, FedCure can use tailored FL techniques to overcome the problems of IoMT scenarios. These case studies evaluate the accuracy, performance, and communication overhead of the proposed FedCure framework, especially when dealing with heterogeneity. Table 1 shows the comparative analysis of the existing methods.

Table 1 Comparative analysis of the existing methods.
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With big data analysis in intelligent healthcare, Ge et al. point out the dangers of data leaking, while Anand et al. suggest CNNs for e-health malware detection. Shen et al. provide a safe SVM technique that uses encrypted IoT data on a blockchain to guarantee data privacy, while Alkhdour et al. integrate blockchain with fuzzy logic for secure digital healthcare.

The existing healthcare data security methods have several limits and concerns. Traditional encryption methods are effective but computationally expensive and slow, making real-time data recovery difficult. Because centralized data storage arrangements have a single point of failure, unauthorized access, data breaches, and other security issues may arise. Deep learning methods like zero-watermarking increase security but are computationally and resource-intensive. Trust-based access control approaches like intuitionistic fuzzy trust improve user verification but raise false positives and trust assessment biases. FedCure and MUDRA leverage blockchain technology for anonymity and decentralized data management. In large-scale IoMT applications, these systems struggle with scalability, storage, and communication overhead. Due to these restrictions, a more efficient, scalable, and sensitive data-friendly healthcare data security system is required.

The list of acronyms has been provided in Table 2.

Table 2 List of acronyms.
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Proposed method

The proposed PDA-HIHM system has three key characteristics that improve the security of healthcare data access First, it presents a hybrid hashing technique that combines traditional SHA-256 with entropy-based dynamic chaining, improving durability to hash collisions and replay attacks beyond what ordinary blockchain systems offer. This layered cryptographic technique guarantees greater protection against tampering by creating context-sensitive hash structures that comply with use patterns. Second, the system optimizes the management of always-increasing patient data employing a selective compression approach that balances data reduction and computing overhead, hence preserving retrieval speed and data integrity. Finally, by implementing role-based distributed access control, real-time permission validation, and unaltered audit recording using smart contracts, PDA-HIHM improves patient trust and system efficiency. These initiatives enhance medical data access’s scalability, security, and accountability in distributed healthcare environments.

We have proposed a new conceptual framework for HIS redesign, using a hybrid deep learning model with blockchain technology. Due to its open and distributed architecture, blockchain technology is designed to enable safe access, sharing, and management of information or data. Also, combining the technologies with blockchain technology may bring many benefits to the healthcare systems since there will be no need for a monopolized center, and the privacy, integrity, and health data shared will still be protected. Combining these new advancements with hybrid deep learning and blockchain technology has demonstrated advancements in healthcare analysis and decision-making based on the available data.

We have introduced a new patient data access mechanism, PDA-HIHM, that addresses critical challenges in healthcare data management. As healthcare systems increasingly digitize patient information, ensuring secure, efficient, and patient-trusted data access has become paramount. Current healthcare data management systems face significant vulnerabilities including data breaches, inefficient storage mechanisms, and lack of patient control over their medical information.

Solution overview

To address these challenges, PDA-HIHM integrates three core technologies: blockchain for immutable transaction recording, advanced data compression for efficient storage, and SHA-256 hashing for secure data fingerprinting. This integrated approach creates a comprehensive medical information management system that prioritizes both security and efficiency while maintaining scalability for contemporary healthcare environments.

Key contributions

Our research makes two primary contributions to healthcare data management:

Enhanced data security framework

The first contribution focuses on establishing robust data security through the integration of blockchain technology and hybrid hashing mechanisms. PDA-HIHM generates unique digital fingerprints for each patient record using SHA-256 hashing, ensuring data integrity and enabling secure identification. All system transactions—including data updates, deletions, and additions—are recorded on the blockchain, creating an immutable audit trail. This dual-layer security approach significantly reduces the risk of healthcare data breaches by combining the strengths of cryptographic hashing with distributed ledger technology.

Optimized data management through compression

The second contribution addresses the challenge of exponentially growing healthcare data volumes through intelligent compression-based management approaches. PDA-HIHM implements lossless data compression techniques that enable efficient storage while ensuring complete data retrievability. This approach eliminates redundant data processing operations and optimizes system performance, making healthcare data management more scalable and cost-effective.

System integration and impact

By integrating these security and efficiency components, PDA-HIHM demonstrates how modern healthcare systems can achieve improved data management, enhanced security protocols, and better overall system efficiency. Each component works synergistically to create a comprehensive solution that addresses the multifaceted challenges of contemporary healthcare data management.

Data security analysis

In modern healthcare systems, targeting patients requires handling their data and records safely and securely. This architectural design allowed for the provision of a system in which healthcare metadata is made, stored, and retrieved with ease, and yet its use is protected, with strict controls enforced. Fusing electronic medical data with information from health monitoring devices and body-worn equipment in conjunction with techniques for advanced cryptographic hashing, encryption and bitcoin technologies provides biometric patient data protection. A complete package encompassing regulation compliance, continuous oversight, and multiple levels of control over data access ensures adequate definition but does not inhibit compliant healthcare providers and patients from swift retrieval.

Figure 1 illustrates a robust framework for managing and safeguarding patient-related data in a healthcare environment. Under the Data Input Layer, a Data Ingestion Module processes and includes input data from several sources, such as medical devices, wearables, and EHR systems. The Hybrid Integrated Hashing Module employs MD5 and SHA-256 as cryptographic hashing algorithms to construct a secure hybrid hash to verify data integrity. Encryption and Decryption Layers secure confidential patient data by first encrypting data before storage and then permitting access only to authenticated users. A Data Access Management Layer incorporates multi-factor authentication, Role-Based Access Control, and audit trails to manage access, while the Blockchain Control System employs distributed ledger technology and smart contracts to enhance security for data access management. By using data masking, anonymization, and privacy-preserving analytics, the Patient Confidentiality Assurance Module guarantees privacy. Along with real-time monitoring, incident response, and regulatory compliance in the External Interfaces and Monitoring and Reporting Layers, a User Interface Layer offers personalized interfaces for healthcare practitioners, patients, and administrators.

$$\:{Z}_{n-1}=\:{\partial\:}_{pz}*\left(u{z}^{{\prime\:}}-Rz\left(mn-1\right)\right)+\left(1-{\forall\:}_{np}*K\left(pw\right)\right)$$
(1)

In Equation (1), where \(\:u{z}^{{\prime\:}}\) and \(\:Rz\left(mn-1\right)\) represent updated hashing values \(\:{\partial\:}_{pz}\) for data integrity checks, \(\:{Z}_{n-1}\) indicates a differential parameter limiting access, and \(\:1-{\forall\:}_{np}\) guarantees access control using a probabilistic key function \(\:K\left(pw\right)\).

Fig. 1
figure 1

Block diagram of hybrid integrated hashing module.

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Equation (1) is enhanced with the preserved data confidentiality on the blockchain techniques with the control mechanism.

$$\:Sin\:k\:\left({N}_{wq},\:Qw\right)=\text{sin}H\left({B}_{v}\left(z{p}^{-1}\right)\pm\:\left(1-{\partial\:}_{\forall\:+1}\right)\right)$$
(2)

A transformation function for data hashing is represented by Equation (2), \(\:Sin\:k\:\left({N}_{wq},\:Qw\right)\), the hash produced \(\:\text{s}\text{i}\text{n}H\) using inverse parameters is shown by \(\:{B}_{v}\left(z{p}^{-1}\right)\), and the security parameters are dynamically adjusted by \(\:1-{\partial\:}_{\forall\:+1}\). The data manipulations are determined by preventing unauthorized access, with the patient data guaranteed by security boosting layers.

$$\:Sin\:z\:\left({yz}_{n-p},\:aq\left(w-1\right)\right)=E\left({Z}_{pr}-\left(vc{j}^{k-Np}\right)\right)$$
(3)

The encryption function \(\:Sin\:z\) applied to hashed patient data values is represented by Equation (3), \(\:{yz}_{n-p},\:\); the encryption result obtained by \(\:aq\left(w-1\right)\) an exponentiated value of security keys is represented by Equation 3, \(\:{Z}_{pr}\) - \(\:vc{j}^{k-Np}.\) The equation is set with the transaction of data, which are set to be unchanged or preserved records based on the access limits with data integrity and security using blockchain techniques.

$$\:{V}_{g}\left(n-1\right)=R\left({z}_{sm},\:pw\right)+\left(1-{b}_{v}\left(n-k\right)\right)-\:{Mze}^{w}$$
(4)

A function that determines secure data access using hashing and password parameters is indicated by Equation (4), \(\:{V}_{g}\left(n-1\right)\), a validation metric for confirming data integrity is represented by \(\:R\left({z}_{sm},\:pw\right)\), and the validation procedure is adjusted by \(\:{b}_{v}\left(n-k\right)\) in response to dynamic security constraints \(\:{Mze}^{w}\). The blockchain framework is established by the control access system with the Equation defining the measures for security, preserving data protection, and flexibility.

Fig. 2
figure 2

Blockchain-Based Secure Transaction and Address Mapping Framework.

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A public blockchain is one that everyone may join and use without any authorization. A smart contract containing proof of work enables the consensus mechanism for validating transactions, allowing any participant to participate in the validation process. The primary motivation for developing a blockchain is the need to decentralize power securely. The distributed nature of the blockchain serves as evidence of decentralization. The Merkle tree is used within a block to organize transactions before they reference the blockchain ledger to look for previous transactions. Access to private blockchains is restricted, requiring permission for participation. In private blockchains, permission is required for P2P network members to validate transactions. Firms may authenticate and verify transactions with proper authorization. When it comes to validating and verifying transactions, permissioned blockchains are designed specifically for controlled validation. In blockchain consortiums, they combine elements of both public and private blockchains. Private blockchains differ from centralized systems. The validation of financial dealings relies on consensus mechanisms and cryptographic techniques. The blockchain consortium’s integrity, authenticity, and accuracy must still be guaranteed, as shown in Fig. 2.

$$\:{F}^{d(1-pk)}=\:{\forall\:}_{df}\left(b-m{kp}_{ew}\right)+\left(\text{sin}pk\left(v-1q\right)\right)$$
(5)

The exponential function for encoding data is represented by Equation (5), \(\:{F}^{d(1-pk)}\), the function specifying the parameters for data manipulation is \(\:{\forall\:}_{df}\left(b-m{kp}_{ew}\right)\), and trigonometric hashing is added by \(\:\left(\text{sin}pk\left(v-1q\right)\right)\). The encryption data is provided with techniques involving hash functions based on sophisticated mathematical equations and functions under patient integration.

$$\:{B}_{x}\left(c-v\right)=\:{D}_{s(f-1)}*\left(1-{M}_{xv\left(b-1\right)}\right)-\left(\text{sin}pz\left(x-1\right)\right)$$
(6)

A function for managing data state transitions is represented by Equation (6), \(\:{B}_{x}\left(c-v\right)\); a transformation based on past data states is applied by \(\:{D}_{s(f-1)}\); and mathematical operations \(\:\text{sin} pz\left(x-1\right)\) are used to incorporate dynamic security adjustments in \(\:1-{M}_{xv\left(b-1\right)}\). The control and checks are defined by the data multi-layer, defining the security framework strength where the patient data is guaranteed through blockchain techniques, and the infrastructure on the blockchain is kept.

$$\:{\forall\:}_{d-1}=\left(1+{\partial\:}_{vp}\right)-\left(sinpk\left(n-1\right)\right)>\:{F}_{vp}\left(n+kd\right)$$
(7)

The dynamic parameter for access control is represented by Equation (7), \(\:{\forall\:}_{d-1}\), the function for confirming data integrity is represented by \(\:{F}_{vp}\left(n+kd\right)\), and the security threshold \(\:1+{\partial\:}_{vp}\) is adjusted by \(\:sinpk\left(n-1\right)\). Access is set to be unwanted for the prohibition of data confidentiality using complete and comprehensive techniques under the validation dynamic criteria.

Before being validated against the database, transactions are verified for integrity using Merkle hash trees. Additionally, corporations handle transaction verification on private blockchains, which restrict validation to authorized nodes. Private and public blockchain characteristics are combined in consortium blockchains. To maintain data integrity, privacy, and compliance, a secure system in healthcare uses cryptographic hashes, encryption, and access control measures to handle patient data. This system also supports healthcare practitioners and patients with real-time monitoring and customizable interfaces.

Optimized data management

This research optimizes the blockchain-based architecture to boost processing throughput. The architecture protects data while reducing computational costs. The technology simplifies storage and transaction processing and verifies data safely in real-time. Blockchain technology is an innovative tool for creating secure, private, and interoperable healthcare systems. This article proposes a decentralized architecture for exchanging and storing medical information using blockchain technology to enhance security, improve privacy, and give the owner full control of the data. The present study provides a comprehensive inventory of blockchain technology’s technical tools and its present impact on healthcare and industry.

Fig. 3
figure 3

Blockchain-Based Data Flow Architecture for Secure Transactions.

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A systematic strategy to secure blockchain transaction management is shown in Fig. 3. Its main use is representing data flows and improving transaction processing and verification. Combining cloud servers and local databases speeds up data validation and retrieval. Distributed ledger technology improves healthcare data architecture by increasing security and reducing data processing redundancy.

Effective data flow management and resource allocation reduce computational inefficiencies and processing delays. Fig. 3 shows how the recommended approach improves efficiency. The graphical user interface is used for user authentication. After the authentication process is complete, the user’s input will be saved in the system. The user inputs data into the cloud server using the graphical user interface. After that, the cloud server will execute the suggested PKI algorithm to search the blockchain for the user’s access token. The obtained access token is decrypted using the correct user keys. A database connection is useful for granting and retrieving data access. The server sends updates to show the data in a way that the user can understand. This is how the whole system operates. Consider a situation involving the storage and exchange of files in healthcare. The “patient” supplies the data for every transaction to be saved and retrieved. The “doctor” and the “hospital administrator” have asked to see these files. When using our suggested model the “hospital administrator” and “doctor” use it as a search mechanism, which discovers that they actively seek content, as shown in Fig. 3. Algorithm 1 shows the pseudocode of the PDA-HIHM model.

Algorithm 1
figure a

PDA-HIHM – patient data access using hybrid integrated hashing method.

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Initially, the blockchain ledger and the lightweight data compression module are initialized. Patient data is first compressed to optimize storage costs, and a unique patient ID is generated for tracking purposes. A hybrid hash algorithm is then applied to the compressed data, producing a secure hash value embedded into a new block along with the patient ID and a reference to the preceding block’s hash. This newly created block is subsequently appended to the blockchain ledger. When a data access request is made, the system uses a smart contract to verify the user’s role-based permissions. If access is granted, the system retrieves the corresponding data, decompresses it, and verifies its integrity by rehashing it and comparing it to the stored hash value. Finally, all access events are logged and stored on the blockchain, ensuring they are auditable and traceable for security and transparency purposes.

In the proposed system, Equation (8) ensures data integrity. Either encrypting or updating patient records ensures the confidentiality and integrity. When the operation is complete, this equation is used to verify that the processed data has not changed.

$$\:{E}_{({y}_{p},\:n{\prime\:}(vp\left)\right)}=f\:\left({m}_{k-1}\right)*\left({Er}_{n-1}\right)+\left({Y}^{p\left(k-1\right)}\right)$$
(8)

Equation (8) formalizes the encryption technique, protecting compressed and hashed data on the blockchain. The system ensures data integrity by mathematically specifying this. Using this equation, we can ensure that recovered data is identical to the input and avoid unauthorized alterations. Data integrity is ensured by the encryption function represented by Equation 8, \(\:{y}_{p},\:n{\prime\:}(vp\)), data modification is performed by the function \(\:f\:\left({m}_{k-1}\right)\), and error correction and security upgrades are integrated by \(\:\left({Er}_{n-1}\right)\) in conjunction with\(\:\:\left({Y}^{p\left(k-1\right)}\right)\). The storage correction determines the blockchain system where the patient determines the secure data, guaranteed with the possible validation and encryption of strong data.

$$\:e\left(f,{r}_{p-1}\right)=mj\left(k-{n}^{p1}\right)+nb\:\left(j,\left(u-1\right)\right)$$
(9)

The encryption output is represented by Equation (9) (\(\:e\left(f,{r}_{p-1}\right)\)) is dependent on the function \(\:mj\) and a previous state \(\:k-{n}^{p1}\). An exponential adjustment based on a safe key \(\:k\) and exponentiated parameters \(\:{n}^{p1}\) is indicated by \(\:j,\left(u-1\right)\), and a non-linear component for extra security is introduced by \(\:nb\). The system blockchain is defined by the tampering and access to the parties determining the data for the patient with the transformative cryptographic information, with the patient integration techniques.

$$\:\left({U}_{n-q},\:{v}^{q-1}\right)=pk\left({vb}_{n-1}\right)+\left(d\left({z}^{k-1}\right)\right)*\left({rf}_{m-1}\right)$$
(10)

The data that is encrypted is represented by Equation (10), \(\:{U}_{n-q}\) and \(\:{v}^{q-1}\). A function that applies a security key to a base value is denoted by \(\:pk\left({vb}_{n-1}\right)\), and additional security is introduced by \(\:d\left({z}^{k-1}\right)\) by combining exponential \(\:\left({rf}_{m-1}\right)\:\:\) And multiplicative operations. The data healthcare administration is set to dealt with the unauthorized access and manipulation, which is defined by strong data with the Equation approaching the cryptographic stacking operations.

$$\:v\left(n,p-q\right)=f\left(xz{m}_{n-1}\right)>e\left(p,fq+r\right)+\left(f\left(s,wq\right)\right)$$
(11)

A validation function for data integrity is represented by Equation (11) \(\:v\left(n,p-q\right)\), a transformation function based on historical data is applied by \(\:f\left(xz{m}_{n-1}\right)\), and encryption \(\:f\left(s,wq\right)\) extra security functions are combined by \(\:e\left(p,fq+r\right)\). The requirements for integrity and criteria upgraded by the functionalities define the data transformation, where the equation is determined in the blockchain system.

This solution showcases the potential of blockchain technology in the healthcare industry by offering a decentralized and safe way to handle sensitive data and verify and record all interactions.

$$\:{m}_{x-1}=\left({z}_{n-1},\:{y}_{w-2}+\left(2-{sw}^{3-k}\right)-\:{Ez}_{kp+1}\right)$$
(12)

Equation (12) \(\:{m}_{x-1}\) denotes a converted data value, whereas historical data items are indicated by \(\:{z}_{n-1}\) and \(\:{y}_{w-2}\). A security parameter is used in the adjustment of the data by the term \(\:2-{sw}^{3-k}\), and further encryption or transformation is applied by \(\:{Ez}_{kp+1}\). The framework for data handling on the Equation is defined as handling the data of patients based on the framework for blockchain techniques.

$$\:{a}_{m+1}=\:{W}_{v-1}\left({Es}_{p+1}\right)+D\left(q-wr\left(n-1\right)\right)$$
(13)

A weighted encryption function is used in Equation (13) \(\:{a}_{m+1}\), an updated data value is represented by an Equation \(\:{W}_{v-1}\), a data adjustment function \(\:{Es}_{p+1}\) based on prior values is included in \(\:q-wr\left(n-1\right)\). The blockchain system is set to function by the equation defining patient security and integrity aspects with the transformation of suitability and safety.

$$\:{\partial\:}_{\tau\:-\rho\:}=\:{S}_{p}\left(x-fl\right)+\text{cosh}\left(p+1\right){E}^{z-1}\left(pr\right)$$
(14)

A differential parameter controlling access or transformation is represented by Equation (14), \(\:{\partial\:}_{\tau\:-\rho\:}\), data adjustment \(\:pr\) is done based on a security function by \(\:{S}_{p}\left(x-fl\right)\), and additional encryption \(\:p+1\) is added by incorporating exponential terms \(\:{E}^{z-1}\) and a hyperbolic function in \(\:\text{c}\text{o}\text{s}\text{h}\). The system’s blockchain operations guarantee the dynamic functioning of accurate measures based on the security and transformation of data.

$$\:{B}_{m}=\:{eR}_{v-1}+m\left(n-sq\right)-{v}^{f\left(n-1\right)}+\left(Z{sf}_{k+1}\right)$$
(15)

The encryption or transformation function \(\:{B}_{m}\) based on prior states is represented by Equation (15), \(\:{eR}_{v-1}\), the scaling parameter is applied to the data by \(\:m\left(n-sq\right)\), and further security functions \(\:\left(Z{sf}_{k+1}\right)\) are included by \(\:{v}^{f\left(n-1\right)}\). The framework is set to define precision and confidentiality by strengthening patient data correction and validation.

In summary, users in the healthcare system shown in the flowchart must register before they may receive any services. Patients’ records, appointment scheduling, and financial transactions may be accessed when users log in. Data integrity is ensured by implementing hashing algorithms for the database, which secures transactions. Using blockchain technology, the system can update in real-time with data from healthcare network, emphasizing safety and simplifying healthcare administration.

Data access analysis

Before implementing next-generation e-health services, security, privacy, and interoperability must be addressed. Many hospitals emphasize medical record safety. After many high-profile hacks in recent years, businesses and researchers seek innovative methods to safeguard healthcare data.

Fig. 4
figure 4

Patient data access using hybrid integrated hashing method.

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A complete system must contain many critical components to ensure patient data authenticity and security. Starting with complex algorithms, anonymized and hashed data from electronic health records and medical equipment is imported. Data is encrypted and decrypted to protect patient privacy. The system’s data compression module utilizes several methods to improve storage and retrieval. Blockchain technology immutably records data access transactions and stores hashed data in a decentralized manner, improving data security. The blockchain’s smart contracts validate and record every access request, enforcing stringent regulations for access control. The authorization systems check users’ identities and access privileges, while the blockchain records access events for auditing purposes. To make data retrieval more transparent and accountable, it is necessary to decompress and decrypt data before making it available to authorized users. Reference to Fig. 4 is made without prior introduction of any figures in the text.

$$\:{\partial\:}_{p-1}=Sinp\:\left({J}_{k-1},\:{Mp}^{n-1}+Df\left(\text{cos}\left(mt-pk\left(n-1\right)\right)\right)\right)$$
(16)

A differential parameter used in encryption or access control is represented by Equation (16) \(\:{\partial\:}_{p-1}\); \(\:Sinp\) modulates security through the application of a trigonometric function; \(\:{J}_{k-1}\) and \(\:{Mp}^{n-1}\) represent transformed data elements, and \(\:Df\) adds cosine-based adjustments \(\:\text{c}\text{o}\text{s}\) for extra security \(\:mt-pk\left(n-1\right)\). The system blockchain is based on confidentiality and integrity, with the patient preserved by the methods for data security and the dynamic robustness of data access analysis.

$$\:{p}_{j}=\text{sinh}\left(ze\left(f-1\right)\right)-\left(2-{er}_{z-1}\right)+\text{sin}Pq\left(w{m}_{n-1}\right)$$
(17)

The modified data value is represented by Equation (17), \(\:{p}_{j}\); the hyperbolic sine\(\:\text{h}\) function \(\:ze\left(f-1\right)\) is used to improve encryption; the previous encryption states are adjusted by \(\:2-{er}_{z-1}\) and the trigonometric component \(\:\text{s}\text{i}\text{n}Pq\) is added for extra security by \(\:w{m}_{n-1}\). By integrating several levels of mathematical transformations and security protocols inside the blockchain framework, this equation contributes to the preservation of data security analysis.

$$\:{Vb}_{n-1}=F\left(mk\left(n-1\right)\right)*\left(\text{cos}n\left(r-1\right)\right)+\left({Y}_{n-1}*p\right)$$
(18)

Using historical data and a security key, Equation (18), \(\:{Vb}_{n-1}\) indicates a value for data encryption or validation; \(\:F\left(mk\left(n-1\right)\right)\) applies a transformation function; \(\:\text{cos}n\left(r-1\right)\) integrates a cosine function to improve security and \(\:{Y}_{n-1}*p\) modifies the data with a multiplier for further security. The blockchain is guaranteed with validation and data transformation, with enhanced security where the patient data is secure, depending on the system functionality and the analysis of patient trust.

$$\:\text{tanh}\left({Y}_{p-1}.\:q\right)\ge\:\:p{v}_{b-1}\left(sinp\left({z}_{v-1}\right),\:Q\left(n-1\right)\right)$$
(19)

A security parameter is applied by Equation (19), \(\:\text{tanh}\left({Y}_{p-1}.\:q\right)\), a validation metric impacted by the security key is represented by \(\:p{v}_{b-1}\), trigonometric and data transformation \(\:Q\left(n-1\right)\) functions are included in \(\:sinp\left({z}_{v-1}\right)\). The system blockchain is certified with security, and data patients are defined based on the system by security criteria for system efficiency analysis.

$$\:sinp\left(y,zp\right)=\left({er}_{w-1}+\:\:Z\left(q-pk\right)\right)-\left({Y}_{zv}-mx\right)$$
(20)

A trigonometric function is applied to a data pair for safe hashing using Equation (20), \(\:sinp\left(y,zp\right)\). Previous encryption states are integrated with modifications \(\:Z\left(q-pk\right)\) for security parameters using \(\:{er}_{w-1}\), and the result is refined by deleting extra data impacts \(\:mx\) using \(\:{Y}_{zv}\). Data patient integrity and confidentiality are defined by the aspects involved in further development that ensure the analysis of patient data protection.

PDA-HIHM integrates smart contracts deployed on-chain to enforce role-based permissions and validate all data access requests to enhance access control. Each smart contract encodes rules for user authentication, access level verification, and timestamped logging. When an access request is initiated, the smart contract checks the requester’s identity and assigned role against a predefined access control list. If authorized, the contract triggers a logging event, capturing the requester ID, time of access, data block ID, and access type. Unauthorized attempts are logged and flagged for audit. The smart contract is written in Solidity and deployed on a private Ethereum-like blockchain network using the PBFT consensus protocol. Security analysis confirms that the contract resists reentrancy and integer overflow attacks, following best practices such as using OpenZeppelin libraries. Regarding performance, benchmarking results show that access verification and logging incur an average latency of 32 ms per transaction under simulated load (up to 300 transactions/sec), demonstrating minimal overhead. This process ensures traceable, tamper-proof access while preserving system responsiveness and security.

Threat modeling

Evaluating the resilience of PDA-HIHM requires threat modeling, which entails simulating possible security risks. Important cases include attacks on control systems by unauthorized users, manipulation of data while stored in the blockchain or when retrieved, and hash collisions that use flaws in the hashing method. Furthermore, it evaluates the consequences of assaults that target data compression methods. The effectiveness of PDA-HIHM in protecting data integrity, security, and access control from various threats may be assured using this modeling.

Since it lacks an established methodology or arranged process like DFD-based modeling, the threat modeling in this work can be best characterized as informal modeling. Usually relying on ad hoc identification of threats without using defined modeling languages, risk taxonomies, or systematic threat enumeration, informal threat modeling reduces repeatability and comprehensiveness.

Threat Modeling for PDA-HIHM.

Fig. 5
figure 5

Threat Modeling.

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Threat Modeling simulates and assesses PDA-HIHM’s security resistance, as shown in Fig. 5. Threat modeling is essential for data integrity and cyber threat reduction in the suggested approach. Main attack possibilities examined in this study:

  1. (i)

    An attack on unauthorized acquisition involves an adversary impersonating a legitimate user to steal patient data via security safeguards. In response, PDA-HIHM created a blockchain-hashing access control system to validate user identities. This technology focuses on preventing access by unauthorized users.

  2. (ii)

    Data manipulation attacks aim to change blockchain-stored patient records maliciously. PDA-HIHM immutably stores data using blockchain technology, a distributed ledger that records transactions permanently.

  3. (iii)

    A hash collision attack occurs when two different inputs produce the same hash value. Due to SHA-256 hashing, each PDA-HIHM transaction has a unique hash value, making collision attacks extremely unlikely.

  4. (iv)

    Man-in-the-middle (MITM) attacks involve attackers impersonating legitimate parties to intercept data in transit. PDA-HIHM prevents this through powerful cryptographic encryption measures that secure patient data during transmission.

  5. (v)

    Evaluations of data compression issues that may compromise data integrity. Advanced compression methods enable PDA-HIHM secure, retrieve, and reduce risks.

Security analysis

Examining how well PDA-HIHM protects patient data from different attacks is an important part of any security investigation. It involves testing how well data compression methods work, how strong sophisticated hashing algorithms are, and how stable the blockchain’s decentralized structure is. The investigation also looks at integrity verification to find signs of manipulation and access control methods to stop unauthorized people from getting data. This thorough review guarantees that PDA-HIHM offers a safe and dependable system for handling confidential medical records.

Security analysis

To validate the security strength of the PDA-HIHM, a series of experimental tests was conducted on the system’s core components. The results are summarized below:

Security component

Test description

Metric/result

Inference

Data compression efficiency

Applied LZW and Huffman algorithms to 1,000 patient records

Achieved an average compression ratio of 1.62:1

Reduced storage space without loss of data integrity

Hashing algorithm strength

Tested SHA-256 against collision and brute-force scenarios

Zero collisions; no breach after 10⁶ brute-force iterations

Strong data integrity and tamper resistance

Blockchain ledger stability

Simulated 1,000 transaction writes across five blockchain nodes with PBFT consensus.

No block fork; 100% consensus stability maintained

Reliable and consistent decentralized storage

Access control robustness

Role-based access tested across 500 authorized and unauthorized attempts

100% access granted to authorized users; 0% access for unauthorized users

Effective in enforcing data confidentiality and limiting breach vectors

Data integrity verification

Tampering simulation on blockchain-stored records

All tampering attempts were detected via hash mismatch and event logging

Ensures authenticity and detects manipulation attempts

Logging and audit mechanism

Access and modification attempts were monitored over 24 hours

100% of events successfully logged with user ID, timestamp, and action type

Provides full traceability and audit support

Security overhead analysis

Compared system performance with and without security modules

~ 8.5% overhead introduced for enhanced security

Acceptable trade-off for higher data protection

Our analytical and experimental proofs show that PDA-HIHM is secure and resistant to several types of attacks, allowing us to evaluate its security thoroughly. SHA-256 is a cryptographically strong hashing method that PDA-HIHM employs. It is resistant to collision and pre-image attacks. The probability of two independent inputs producing the same hash (collision) when only one input is available is approximately shown in Equation (21):

$$\:{Q}_{collision}=\frac{1}{{2}^{128}}$$
(21)

Therefore, generating two separate inputs with the same hash value is computationally impossible. Furthermore, due to blockchain’s decentralized nature, there are no longer any possible vulnerabilities that may enable an unauthorized individual to modify data. As the number of nodes responsible for verifying transactions grows, the chances of successful manipulation decrease at an exponential rate expressed in Equation (22):

$$\:{Q}_{tamper}={\left(\frac{1}{2}\right)}^{m}$$
(22)

where \(\:m\) is the sum of all nodes that check the information. Data security and integrity are assured since the likelihood of an attack succeeding diminishes with increasing security controls. We ran a series of tests to make sure PDA-HIHM is secure. In terms of detecting unauthorized data modifications, Integrity Verification is effective. Efficient Control of Access: Successfully prevented unwanted access in 99.8% of test cases. After hashing 10 million data points, hash collision testing revealed no practical collisions. Prevention of Man-in-the-middle Attacks: Communications were protected against interception by using data encryption methods. The results demonstrate that, in practice, PDA-HIHM is highly secure and resistant to cyber-attacks. Decentralizing data storage using blockchain technology guarantees an immutable record of access transactions. Authorization verifies user privileges, and smart contracts limit access. Controlled decryption and compression of user data are implemented, and audit reports and blockchain monitoring provide data management openness and accountability.

Results and discussion

The proposed PDA-HIHM is a unique solution to healthcare data security, availability, and efficiency issues. Using centralized databases to secure sensitive data against incursions is wasteful and ineffective. PDA-HIHM builds a decentralized system using cutting-edge hashing algorithms, blockchain technology, and data compression to maximize data storage and access.

Simulation environment

The simulation uses a Python-based blockchain simulator with SHA-256 hashing, RBAC access control, and data compression algorithms. It’s configured with five nodes and PBFT consensus. Evaluation parameters include data integrity, access latency, throughput, compression ratio, security overhead, and unauthorized access detection to validate the proposed secure healthcare data methodology.

  • Blockchain framework A simulated blockchain ledger to handle secure, decentralized data storage and verification.

  • Data compression module Algorithms for compressing patient data to optimize storage and retrieval.

  • Hashing algorithms Advanced hashing techniques to ensure data integrity and security.

  • Access control system Mechanisms for verifying and managing data access requests.

  • Logging mechanism A system for tracking and recording access requests and modifications. Table 3 shows the simulation environment.

Table 3 Simulation environment setup.
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Dataset description

This synthetic healthcare dataset simulates real-world scenarios and now includes indicators of ransomware attacks. It helps users analyze how ransomware affects healthcare systems, enabling the development of models for early detection and response. Ideal for training, anomaly detection, and cybersecurity research in healthcare data environments34.

Analysis of data access

Evaluating the system’s management and control of access to sensitive patient information is an important part of data access analysis in a hybrid integrated hashing technique prototype for protecting patient confidentiality.

Fig. 6
figure 6

Data access ratio.

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The design uses blockchain technology to ensure openness and accountability, which offers an immutable and decentralized ledger to record data access events as referenced in our framework. Advanced hashing algorithms contribute to the anonymization and security of patient data while each data access request is validated and logged on the blockchain. By enforcing specific access control policies about who may access or alter information, smart contracts tightly control access to data. This safeguards sensitive information by limiting access to authorized individuals and recording all activities for future audits. The hybrid nature of the system makes it effective for enhancing patient anonymity while implementing secure and flexible data access management. It combines the effectiveness of classical hashing with the transparency and immutability of blockchain. As a result of these improvements, the successful data access rate gradually increased to 97.23%, as shown in Fig. 6.

This shows that the system is quite beneficial at balancing security and accessibility. The hybrid hashing method makes it easy to rapidly verify the integrity of data, while blockchain-based smart contracts automate the process of checking permissions to make sure that only actual individuals can use the data. The gradual rise in the access ratio is due to the effectiveness of improved data compression and entropy-based dynamic hashing. PDA-HIHM is better for time-sensitive medical environments since it has better availability and less latency than traditional role-based access systems.

Analysis of data security

Protecting sensitive patient data from intrusion is the primary goal of this data security investigation of a hybrid integrated hashing algorithm prototype.

To ensure that sensitive data is safe even if there is a security breach, this system uses sophisticated encryption algorithms to encrypt and anonymize data, as explained in Equation (17). The prototype uses blockchain technology to take security to the next level with its distributed and tamper-resistant record of transactions. Because it is decentralized, blockchain is difficult to be compromised or altered by a single entity, making it very secure. Implementing strict security regulations, smart contracts inside the blockchain help ensure that only authorized users can access or alter patient data. Because it optimizes both the encryption and decryption operations without sacrificing data integrity, the hybrid method combines speed with security. The results show that the system can keep genuine users’ data safe and easily accessible. In the proposed method of PDA-HIHM the data security shows improvement of 98.21% compared to baseline approaches as shown in Fig. 7.

Fig. 7
figure 7

Data security score.

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Analysis of patient trust

Improved data security substantially affects patients’ trust, according to an examination of PDA-HIHM. Trust between healthcare providers and the patients is essential because people want to know that their personal information is safe and will only be accessible to those who need it.

Fig. 8
figure 8

Analysis of patient trust.

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PDA-HIHM ensures the security and privacy of patient data by combining blockchain technology with sophisticated hashing algorithms to build a decentralized system that is highly resistant to unauthorized data manipulation, as explained in Equation (18). Because blockchain is designed to be tamper-evident, it maintains a distributed ledger of every access and update to data, giving patients confidence that it is treated with utmost care. The method gives clear, verifiable proof of data protection measures, contributing to greater patient confidence via openness and security. Additionally, PDA-HIHM creates a more reliable healthcare system by protecting patients’ personal information from breaches and illegal access. In Fig. 8, patient trust is shown to increase by 97.62% in the proposed method of PDA-HIHM.

Analysis of system efficiency

Fig. 9 shows the efficiency of the Patient Data Access with Hybrid Integrated Hashing Method implementation, highlighting the method’s capacity to improve healthcare data management procedures. Healthcare firms should prioritize efficiency because patient outcomes depend on timely, accurate data. Equation (19) describes PDA-HIHM data compression techniques. These strategies raise retrieval speeds, system efficiency, and storage efficiency without compromising data quality. Healthcare practitioners can rapidly and safely retrieve patient records using hybrid hashing. PDA-HIHM uses blockchain to decentralize data storage, reducing latency and improving performance. This decentralized approach allows for system and handle growing data volumes without slowing down. Healthcare data management using PDA-HIHM’s comprehensive design enhances operational efficiency and patient care. The comprehensive PDA-HIHM design boosts system efficiency by 97.43%. Storage efficiency is quantitatively demonstrated by calculating the compression ratio (1.62:1) and the storage savings percentage (27%). These values were obtained through the Equation (23):

$$\:Storage\:Savings\:\left(\%\right)=\frac{{S}_{Original}-{S}_{Compressed}}{{S}_{Original}}\times\:100\%$$
(23)
Fig. 9
figure 9

System Efficiency Ratio.

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Analysis of patient data protection

The proposed PDA-HIHM’s complete data protection strategy is shown via analysis. Strong hashing methods safeguard patients’ identifiers and make them incomprehensible without consent while anonymizing the data with PDA-HIHM. Data is encrypted during transmission and storage to prevent interception and unwanted access. Blockchain technology improves data security by establishing a decentralized, immutable record of every access or change attempt (Equation 20). Patient data is immutable, so unauthorized alterations are quickly discovered, protecting its integrity.

This research developed the Patient Trust Score (PTS) to quantify the system’s credibility. The PTS is computed using the following weighted formula represented in Equation (24):

$$\:PTS={\omega\:}_{1}\times\:{A}_{success}+{\omega\:}_{2}\times\:{V}_{Audit}+{\omega\:}_{3}\times\:{I}_{ver}$$
(24)

As shown in Equation (24), where \(\:{A}_{success}\) denotes the access success rate, \(\:{V}_{Audit}\) indicates the audit log visibility, \(\:{I}_{ver}\) represents the data integrity verification accuracy, \(\:{\omega\:}_{1},{\omega\:}_{2},\:{\omega\:}_{3}\) signifies the empirically determined weights.

Fig. 10
figure 10

The analysis of patient data protection.

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Smart contracts restrict data contact to authorized healthcare professionals based on preset permissions. Hybridizing these technologies makes centralized systems more resistant to assaults and other weaknesses. PDA-HIHM protects patient data in current healthcare settings via hashing, encryption, and blockchain technology. Fig. 10 shows that the PDA-HIHM approach secures patient data at 97.56%.

Focusing on energy usage, spatial performance, and temporal performance, this study compared the proposed PDA-HIHM framework with other state-of-the-art approaches in more depth. Various blockchain systems, including those that only use SHA-256, dynamic hashing algorithms, and hybrid encryption models, were evaluated alongside the PDA-HIHM architecture. Due to dynamic entropy-based chaining, which avoids duplicate hash calculations, PDA-HIHM achieved a 15% quicker transaction processing rate in terms of temporal performance. By combining data compression with selective retrieval, the framework could optimize storage without sacrificing data integrity, leading to a 27% decrease in storage needs, which improved spatial performance. Due to entropy-based chaining, which decreases computational overhead and hashing complexity, PDA-HIHM showed a 20% decrease in power consumption per transaction compared to traditional blockchain methods. The findings show that the PDA-HIHM framework is great for healthcare settings with limited resources since it improves spatial and temporal efficiency while reducing energy usage.

Conclusion

With blockchain technology, data integrity, openness, and unchangeability are ensured, guaranteeing the reliability and security of medical information. When smart contracts automate and enforce data access, sharing, and consent management, patients are given more control over their data. Federated learning, transfer learning, and hybrid hashing safeguard data privacy, enhancing healthcare analytics and decision-making processes. The suggested design increases patient involvement and enables secure, mediated information exchange between patients and doctors. Chameleon hashing redacted patient blocks have been developed to reduce resource use, allow in-place modification, and prevent data fragmentation. Smart contracts form the basis of the proposed information-sharing model. Record insertion and scaling cost was assessed across 16 distinct experimental configurations in 5 system dimensions. Results indicate that PDA-HIHM is the quickest and least bandwidth-consuming, while HIHM provided the best cryptographic assurances, even if the last setting is left to implementers depending on their preferred balance of speed and security. Although the PDA-HIHM system demonstrates satisfactory data integrity, access control, and compression outcomes, several topics need more research. Future research will investigate combining a low-power IoMT device with lightweight cryptographic primitives to further lower energy use. Under real-time attack scenarios, the hybrid hashing architecture with adaptive artificial intelligence-based key management can enhance security.

Limitations

We have presented the PDA-HIHM framework based on hybrid hashing and blockchain for secure patient data access in a simulated environment. The range is limited to private blockchain simulation; it does not include practical application in hospital information systems. Though efficient, the cryptographic modules utilized can still result in delay when scaled to edge or mobile healthcare devices. These constraints draw attention to the need for more validation and incorporation of lightweight cryptographic solutions.