Evaluating the Impact of Blockchain Models for Secure and Trustworthy Electronic Healthcare Records

Blockchain technology is among the most significant developments and revolutionary innovations of the Information Technology industry. It corners a crucial space in the present digital era and has already made significant differences in human life. Moreover, it is anticipated that the Blockchain technology will improvise the existing IT facilities in the next several years in many domains. Recent technological developments are allowing for a major advancement in Healthcare sectors. Information security and accessibility are critical considerations for the integration and communication with Electronic Healthcare Record (EHR) systems when sharing private medical information. In this context, selecting the most effective blockchain model for secure and trustworthy EHRs in the healthcare sector requires an accurate mechanism for evaluating the impact of different available blockchain models for its features. The present study uses a scientifically proven approach for evaluating the impact of blockchain technology and provides a novel idea and path to the future researchers. This research analysis garnered the feedback of 56 domain experts in the healthcare management for assessing the impact of different blockchain models. To eliminate the ambiguities that arose due to multiple opinions of these experts and for the externalization and organization of information about the selection context of the blockchain model, the study used a decision model. Fuzzy Analytic Analytical Network Process (F-ANP) method was used to calculate the weights of the criteria as well as the Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique was used to evaluate the effect of alternative solutions. Further, the results obtained through this empirical investigation will be an instrumental reference for choosing the most appropriate Blockchain model for maintaining breach-free EHRs.


I. INTRODUCTION
Nowadays several countries are experiencing massive growth in the healthcare challenges, although accessibility to primary physicians or practitioners has become more challenging for patients. Taking into consideration the word ''blockchain,'' it's becoming increasingly apparent that such a technology is not only significant but also indispensable in the The associate editor coordinating the review of this manuscript and approving it for publication was Chao Shen . era of the World Wide Web [1]. Generally, a blockchain is considered as a decentralized record database or a distributed ledger of all transaction processing or electronic activities that have been conducted and exchanged between the involved parties. A blockchain includes a definite and provable documentation of each transaction that has ever been made [2], [3]. A transaction may be carried out in a decentralized manner using blockchain technology. Blockchain can therefore reduce costs considerably as well as increase performance [4]. VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The blockchain technology-based modern emerging technological innovations have revolutionized almost every sector, such as energy [5], [6], e-commerce [7], banking [8], government administration [9], medical services [10], [11], education [12], agricultural development [13] and several other industries. A renowned scientific study and market consulting firm, Gartner, estimated investment decisions worth $3.1 trillion in blockchain technology by 2030 [14]. The following Figure 1 shows the blockchain investment growth rate forecast by Gartner.
The blockchain technology's transformative capability has been rapidly recognized by leading companies, identifying it as a turning point in many market use case scenarios including healthcare sectors. Due to this substantial scale of enterprise implementation of blockchain technology, a large amount of work has been conducted in this area. According to the HIPAA Journal report, there were 3,054 data breaches in the healthcare sector affecting over 500 records between 2009 and 2019. Those breaches caused the destruction, theft, disclosure, or unauthorized release of 230,954,151 records in the healthcare sector. This is equal to even more than 69.78 percent of the US population. Breaches of healthcare data were recorded at a rate of 1.4 per day in the year 2019 [82].
The Healthcare industry alone has recently begun to emphasis more on the implementations allowed by blockchain [15]- [20].
The blockchain is a technological innovation now attracting tremendous interest in the healthcare sector. However, blockchain can be seen as the top 5 preferences by 40 percent of health executives. Besides, blockchain technology spending on the worldwide healthcare sector is projected to reach $5.61 billion by 2025, as per a study from BIS Research. As per this report, the implementation of blockchain technology may save up to $100-$150 billion annually by 2025 in costs related to data loss, IT costs, operating costs, assistance structure costs and administration costs, and by the deception and fraudulent products in the healthcare sector [21]. Adoption of EMR (Electronic Medical Record) has now been regarded as a pivotal step in enhancing the knowledge, efficiency, customer experience and associated costs of access to healthcare. Kemkarl et al. estimated that, ultimately, the EMR program can save more than billions annually [22]. Transferring health care data would allow us to become more informed, for example, to understand better behavioral patterns in community health and disease in order to guarantee better clinical care [23], better practice of the guidelines instituted by the doctors [24]. However, at the same time it is sensitive to a wide variety of security and privacy risks due to the functionality and design [25], [27]. A big problem for advanced health-care data structures is how to capture, control and interpret patient healthcare data without increasing privacy abuses [26].
Blockchain technology may combine medical and pharmaceutical records of a patient from different websites as well as data providers to create a single, updated record that a physician can refer to while treating patients. There are also major technological barriers to blockchain adoption in healthcare sectors [29], [30]. Therefore, evaluation of the impact of different blockchain technology models for securing web-based electronic healthcare records is a critical as well as a challenging task in general. Evaluating the impact of blockchain technology on the healthcare sector's growth is significant because it is the prerequisite for implementing an effective healthcare policy. In this research paper, we are modeling the effect of different blockchain models on healthcare applications as an issue involving Multiple Criteria Decision Making (MCDM) techniques.
There are several MCDM approaches available for solving this kind of problem [31]. Furthermore, the core challenge is to determine the impact of blockchain technology in the healthcare perspective. In this research paper, the researchers used the Fuzzy-based Analytic Network Process (ANP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches [32]. AHP is focused on the structure of the hierarchy whereas the ANP is focused on the structure of the network. Although many authors have used AHP-TOPSIS for this kind of assessment yet, ANP is another extensively used Analytic Hierarchy Process (AHP) tool implemented in MCDM based problems [33]. Many authors presented their work on fuzzy ANP-TOPSIS Multiple-criteria decision-making (MCDM) strategies available for solving problem creating choices of this kind. However, none of them performed study on evaluating the impact of blockchain technology for securing web based electronic healthcare records with the help of fuzzy based decision-making Process.
The rest of the paper is organized as follows: In Section 2, the paper describes the different blockchain technology models. Section 3 defines the methodology for the proposed research study. With the help of Fuzzy ANP-TOPSIS, impact of blockchain models for secure and trustworthy EHR is evaluated in Section 4. Comparisons of the findings and sensitivity analysis have been presented in Section 5 of this paper. Finally, discussion and conclusions are given in Section 6.

II. DIFFERENT BLOCKCHAIN MODELS
If Different types of Blockchain models in this category include: Private Blockchain, Public Blockchain, Hybrid Blockchain, Permissioned Blockchain, Consortium Blockchain, and Decentralized application which are discussed in details in the following sub-sections.

A. PRIVATE BLOCKCHAIN
Private blockchain is a distributed ledger that functions as a closed, secure repository founded on principles of cryptography. It is a blockchain running with limitations or permissions only within a closed network. Private blockchains are typically used by an enterprise or company where users in a blockchain network are only selected participants. Write-permissions are tracked in a completely private ledger through a central vector of decision making while readpermissions may be public or confined [35]. It only enables authorized individuals or particular organizations to enter the ledger, access as well as display data. In this, some recognize most of the users' accounts before transacting. A version of the private blockchain is the concept of a decentralized system or cooperative, wherein the blockchain works under community governance. This kind of blockchain is a private network that preserves a public transaction record which can only be accessed by someone who is authorized [36].

B. PUBLIC BLOCKCHAIN
A public blockchain allows everyone to participate. It is indeed a distributed ledger platform which is non-restrictive, with no permission. Everyone who has internet connectivity can enter to become an approved node on a blockchain platform and thus become a component of the blockchain technology network. A node or user that is a member of the public blockchain who is allowed to view recent as well as old records, check transactions, or prove tasks for an upcoming chain, and perform mining activities. Public blockchain technologies enable everyone to communicate with one another participant involved in the transaction. It holds a history of unchangeable transactions. Anybody can report a transaction through conforming to a collection of established standards and participating in the network. The identification between each of the two participants can be either anonymous or completely pseudonymous, i.e., before the exchange, the transacting participants may not recognize one another [37].

C. HYBRID BLOCKCHAIN
Hybrid blockchain is based on a mixture of a private blockchain with a public one. This incorporates the functionality from both kinds of blockchains that someone can have a network dependent on private consent and also a network without public consent. In this kind of decentralized network, users are able to monitor who receives access to the information the blockchain holds. Besides this, a selected portion of blockchain information or documents may be permitted to go public while maintaining the secrecy of the rest of the information in private network as possible. The blockchain hybrid network is versatile, thus making it possible for users to enter a private blockchain alongside numerous public blockchains. Highly controlled businesses as well as governments may take advantage of hybrid blockchain. It allows consistency and flexibility including what information on a public ledger is kept secret or distributed. There are many implementations of hybrid blockchain that exist in the real world. For instance, on Ethereum (public blockchain) as well as Quorum (private blockchain), XinFin is a hybrid blockchain. XinFin has accomplished numerous pilots through supply chain logistics, transportation, and foreign trade agreements as well as financial services [38]. Hybrid blockchains are also used as a means of preserving security while providing greater transaction performance. It often takes the form of a public primary chain attaching the primary chain with private or permitted side chains [39].

D. PERMISSIONED BLOCKCHAIN
Anyone else involved in verifying transactions or accessing the information on the network has to get a centralized regulator's authorization. This really is valuable for businesses, financial institutions, and organizations which are confident complying with most of the restrictions as well as very conscious about maintaining full monitoring of the records [34]. Permissioned blockchains may be regarded as an enhanced blockchain protection mechanism since it maintains an authentication layer that allows specific acts to be done only by certain recognizable individuals involved. Permissioned blockchains function remarkably different from the private as well as public blockchains. It is constructed to reap the benefits of blockchains without compromising a centrally controlled system's authority component. A good example of permissioned blockchain is Ripple.

E. CONSORTIUM BLOCKCHAIN
The consortium blockchain is based on a semi-decentralized model, in which the blockchain network is operated by more than one enterprise. This is contradictory to a private ledger that only one entity manages. In this type of blockchain, more than one enterprise may function as a node as well as share knowledge, or do mining. The consortium blockchain incorporates elements from the private as well as public chains. At a consensus point, the most significant deviation from either system can be identified. Rather than an open platform in which anybody can verify blocks or even a closed platform in which only one party gets to appoint block suppliers, a consortium chain has a group of similarly powerful entities as validators functioning [40,[56][57].

F. DECENTRALIZED BLOCKCHAIN
Decentralized applications (dApps) are software applications or systems which run and operate on a blockchain as well as P2P database network rather than a single device, and are beyond the influence and authority of a single body. BitTorrent, Popcorn Time and Tor are some examples of a software application running on different computers which are members of a P2P network where there are several members from both sides, some downloading the data, others feeding and maybe even seeding the data, whereas others execute both operations concurrently. The dApps live and operate on a blockchain platform in a public, open-source, decentralized ecosystem in the sense of cryptocurrencies, and are free of any single authority's control and intervention [41]. DApps provide serverless specifications that can be implemented on the client-side and through a blockchain-based distributed network. The client tool handles the front-end and user credentials, while the back end operates inside a network of disbursed machines providing processing as well as storage needs [42].
The Table 1 discusses the different criteria which have been identified to evaluate the impact of blockchain technology on the different healthcare services.

III. MATERIALS AND METHOD
Research methodology included in this analysis aims to evaluate the impact of blockchain technology for Securing Web-based electronic healthcare records. The hierarchical structure of the proposed challenge of evaluating the impact of blockchain technology models for secure webbased electronic healthcare records is shown in Figure 2. This is designed to analyze the impact of different blockchain models on different digital healthcare services, and to implement MCDM technique in a fuzzy setting so as to choose the most appropriate solution. To accomplish the stated objective, the researchers of this study have used Fuzzy-ANP to calculate the weights of the factors and their respective interconnectivity with one another. We also used TOPSIS methodology to order the alternates. A comprehensive description of these procedures is provided in the following subsections.

A. FUZZY-ANP
Saaty [43] introduced ANP as a method for decision-making across several criteria. Because of its strengths compared with the previous method for analytical multiple criteria tasks, Saaty [44] coined the name Analytical Hierarchy Process (AHP). ANP is chosen in this research study to solve the problem. AHP evaluates hierarchical relationships between different levels of decision without addressing interconnections between criteria or alternatives, whereas the ANP tests interconnections between criteria including level of decision using network connections. In some cases, the ANP has also been verified as well. ANP also represents inter-dependencies among elements of the same cluster using loops and with other clusters of the same network along with feedback [23], [45]. The fuzzy-ANP methodology is a combination of fuzzy logic with the ANP method to manage inaccurate data, thus aiding in the conduct of reliable and consistent tests.

B. FUZZY-TOPSIS
Yoon and Hwang [46] initially proposed the TOPSIS methodology. They designed the TOPSIS founded on the idea that the selected alternative must have the shortest distance from the ideal-positive solution as well as the longest distance from the ideal-negative. TOPSIS system is one of the popular multiple criteria decision-making strategies for dealing with real-world complex problems. It strengthened the Zelany's [47] conception of the displaced ideal solution [48]. This is one of the strongest MCDM approaches to tackle the problem of ranking reversal that is the shift in alternative ranks whenever a non-optimal alternative is implemented. This approach has already been frequently implemented in several existing works. Besides, the TOPSIS approach has been improved to fix Fuzzy MCDM issues [32].
The researchers have used a hybrid method of fuzzy-ANP and TOPSIS in this study to evaluate the impact of blockchain-oriented technology for securing web-based electronic healthcare records. As per the Figure 3, the step-bystep process for determining weighting as well as priority ranking with the help of Fuzzy ANP-TOPSIS is defined as follows: Step 1: The linguistic terms were first transformed into straight measurable numeric values, and after that into triangular fuzzy numbers (TFN). In this research study TFN may be defined as (c1, c2, c3), where (c1 c2 = c3) as well as c1, c2, c3 are variables indicating the smallest, intermediate, and the highest value in the TFN. Assume A is a mischievous number and can also be represented as in equations (1-2) and can be seen in [48].
First, different views have been taken from 56 academic and Blockchain industry experts, who had a variety of blockchain development and research experience for each attribute set and related data. The experts were requested to collect and analyze their perspectives in a virtual meeting environment and were informed about the scale of the attributes as regards the different groups and the linguistic values.
Thereafter with the help of the collected data, the researchers obtained network structure to assess the weights of the specified attributes about the impact of blockchain technology. Professionals and experts in blockchain research and development are given the answers by assigning ratings to the attributes which affect each other in an observable manner as per the scale shown in Table 2.
Triangular Fuzzy Number (TFN) is derived with the help of crisp numerical value systems by implementing equations (3-6) as well as presented as (c1ij, c2ij, c3ij) where, c1ij signifies low value, c2ij signifies mean value and c3ij signifies high value. In contrast, the concept of TFN [ηij] is as follows: where, c1 ij ≤ c2 ij ≤ c3 ij c1 ij = min J ijd (4) and c3 ij = max J ijd (6) Jijk describes relative impact of the values among two factors listed according to the above equations; as well as provided through the decision of the experts. Where, I and j indicate a pair of attributes determined by experts. TFN (ηij) is calculated for a specific comparison dependent on the geometric mean of domain expert opinions. Therefore, equation 7 to 9 allows combining TFN values. Two TFNs were A1 and A2, A1= (c11, c21, c31), and A2= (c12, c22, c32). The operating standards for them are as follows: Step 2: The matrix for a pair-wise comparison is developed with the help of the feedback obtained from the decisionmakers. Consistency index (CI) evaluation is performed using such a formula present in equation 10 as follows: From which, CI represents the Consistency Index and t represents the compared number of factors. Next Consistency Ratio (CR) calculation with the help of a Random Index (RI) is as follows: If CR < 0.1 therefore the matrix produced is fairly consistent. Where, RI determines the random index taken from the Saaty random index [49]. Phase 3: With the help of the defuzzification process, the TFN values are transformed into measurable value after receiving a remarkably consistent matrix. The technique of defuzzification used in this research work is drawn from [50] as developed in equation (12)(13)(14), generally referred to as alpha-cut.
where, 0 ≤ α ≤ 1 and 0 ≤ β ≤ 1 Such that, (14) α and β have been used in the previous mathematical formulas for the domain expert choices, α and β also differ among 0 and 1.
Step 4: The ANP method deals with dependency within a cluster as well as between various clusters. The aim of this step is the formulation of the supermatrix resulting from the pairing comparisons among groups like target, factors, subfactors, as well as alternatives resulting from the preference vector.
Step 5: Evaluating the output rating of any alternative over any fixed factor TOPSIS requires this equation to normalize the entire decision matrix.
Step 6: Estimation of I+ matrix positive-ideal solution, and I-matrix negative-ideal solution.
In this equation, z + j is Max zij if j is an advantage factor as well as Max zij if j is a cost factor; z − j is Min zij if j is an advantage factor and Min zij if j is a cost factor?
Step 7: The next step is to determine the difference between each alternative value and the positive-ideal solution as well as the negative-ideal solution: The positive-ideal solution: The Negative-ideal solution: where, D C j Describes Positive-Ideal solution distance for i option and D − i is the distance from the ideal-negative approach. Measuring the importance of output for each alternative (Pi).
The above-mentioned step-by-step assessment process would be accompanied through the use of the Fuzzy-ANP TOPSIS system with a specific number of alternatives to evaluate the impact of blockchain technology for EHRs. The next section conducts a case study which provides the quantitative framework for achieving blockchain technology strategies.
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IV. DATA ANALYSIS AND RESULTS
Estimating the impact of blockchain technology objectively is a qualitative measurement. For evaluation, six criteria of blockchain technology at Level 1, namely Patients' Identity, Data Security, Data Monitoring, Immutability, Consensus and Value are defined, respectively, as T1, T2, T3, T4, T5 and T6.
Regarding blockchain technology impact evaluation for EHRs at Level 2: the attributes of patient identity are authentication and authorization and are represented as T11, T12, respectively. The attributes of data security are information privacy, data management, and authorization which are represented as T21, T22, and T23, respectively. The attributes of data monitoring are synchronization and control, represented as T31, T32 respectively. The attributes of immutability are cryptography and hashing which are represented as T41, T42, respectively. The attributes of consensus are Proof-of-Stake and Proof-of-Work which are represented as T51, T52, respectively. The attributes of value are performance, convenience and demand, represented as T61, T62 and T63, respectively, in the tables specified below. The impact evaluation of blockchain technology for securing electronic healthcare records with the help of fuzzy-ANP-TOPSIS has been measured by using the Equations (1)-(20) as follows: By using the standardized Saaty scale which can be seen in Table 1 as well as using Equations (1)-(9), we transformed the linguistic-terms into quantitative values and afterwards aggregated triangular fuzzy numeric (TFN) values. Then, the consistency indexes, as well as random index, were determined using Equations (10) and (11). A pair-wise comparison matrix has a random index of less than 0.1 which implies that our matrix is consistent in the pair-wise matrix. Further the pair-wise comparison matrix of the Level-1 parameters is then determined.
With the help of Equations (12)- (14), the defuzzification of pair-wise comparisons matrix was performed to use the alpha-cut process, and uniform values as well as defuzzified local weights of all these sub-attributes are therefore shown in Tables 4, accordingly. Through implementing the very same procedures used through the hierarchy, pair-wise comparison matrixes and local weights were measured accordingly.  To get an unweighted super matrix, the priorities obtained from the various pair-wise comparisons are utilized. Following the initial estimation of the weighted super matrix as shown in Table 5, the super matrix limit is also estimated. By using local weights, weighted super matrix as well as limits super matrix, global weights and ranks of VOLUME 8, 2020 attributes are calculated by hierarchy, which can be seen in Table 6.
The researchers applied the inputs given by 56 domain experts onto the technical specifications data of six alternatives. These alternatives included: Private Blockchain, Public Blockchain, Hybrid blockchain, Permissioned blockchain, Consortium blockchain and Decentralized application, represented as A1, A2, A3, A4, A5 and A6, respectively [54]. The Fuzzy-TOPSIS approach is assigned global weights of different specified factors generated through fuzzy-ANP as inputs towards generating priority rank for every alternative. The performance result with the help of fuzzy-ANP-TOPSIS has been checked by implementing these equations (15)- (20) as follows: with the help of equations (1)-(9) and equation (15). The Equation (16) has been used for that purpose, and a hierarchical decision-matrix was constructed. Then each standardized decision-matrix cell score (also called the normalized performance value) becomes multiplied by weights of every criterion as well as a fuzzy weighted normalized decision-matrix has been generated by the equation 16 and can be seen in Table 7.
Further, the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative-Ideal Solution (FNIS) were calculated through implementing Equation (17). After that the distance for each choice value from both the FPIS as well as FNIS is calculated applying through Equations (18) and (19) and can be seen in Table 8-9 under the column called D+I and D-I. Subsequently, the output value of each criterion has been determined through implementing Equation (20). Alternatives rating are achieved on the account of the measured success score that has also been showed in Table 10.
Six blockchain technology alternatives defined output as the A1, A4, A2, A5, A3 and A6. In the findings of the research study, A1 (Private Blockchain model) received much more spread exposure in higher weighted alternatives than the other blockchain models, demonstrating its first rank amongst public blockchain, hybrid blockchain, permissioned blockchain, consortium blockchain, and decentralized application to be implemented to deliver more secure and efficient EHR services in the healthcare organizations.

A. SENSITIVITY ANALYSIS
Assessment of sensitivity is conducted by adjusting the variables to determine the validity of the obtained findings [51]. The sensitivity evaluation on resulting weights (variables) was carried out during this data analysis. Throughout this study, at last (2nd) stage 15 variables are taken so that the sensitivities are tested with the help of 14 experiments. The level of satisfaction (CC-i) is determined in every experiment by making adjustments in weights of each factor, whereas the weight of the other factor remains unchanged through both the Fuzzy-ANP-TOPSIS method. Table 11 and Figure 4 indicate estimated effects.
Alternative one (A1) has a strong degree of satisfaction (CC-i), based on the actual performance. Fifteen experiments are performed. The obtained findings show that in 15 experiments the alternative-1 (A1) still maintains a high degree of satisfaction (CC-i). In thirteen other experiments the  lowest weight of alternative is A3 and A6 in two different experiments. Performance outcome comparisons with each other indicate that the alternatives' scores are weight prone. VOLUME 8, 2020

B. COMPARISON OF THE RESULTS
In this research we used various different symmetrical approaches to test the accuracy of the outcome of this study. The researchers used a Fuzzy ANP-TOPSIS method in this analysis to analyze the accuracy of the results of this study. In fuzzy ANP-TOPSIS, the data collection and evaluation process for that dataset is much like the classical ANP-TOPSIS method. Fuzzification and defuzzification are therefore necessary for the Fuzzy-ANP-TOPSIS. Therefore, data is collected in its original numerical form for fuzzy ANP-TOPSIS, and then translated into fuzzy numbers. The differences in the Fuzzy and classical ANP-TOPSIS findings can be seen in Figure 5.
The findings obtained from this study are special and yet essentially the very same. This empirical research took the Pearson Correlation technique for evaluating the association among results. The coefficient correlation demonstrates the effect of the two-value association. The scale ranges from −1 to + 1 [52]. The value close to −1 indicates the lower connection among values, and the value close to + 1 indicates the higher connection among values. The Pearson correlation between both the Fuzzy-ANP findings and the classical-ANP findings is 0.89176, indicating the clear similarity between the outcomes obtained. As can be seen in Table 15, the findings with different criteria of Blockchain Technology of the same dataset have already been produced, and all these findings indicate that the correlation among the results of fuzzy-ANP as well as classical-ANP is strongly correlated. The findings of our analysis also demonstrate that the identified variables and their relation to efficient security mechanisms in security tactics perspective are remarkable. Khan et al. [53] exclusively incorporated Fuzzy ANP-TOPSIS approach in their study. The reason behind this is that ANP methodology is different than the AHP methodology because of the network structure rather than tree structure. Hence the researchers have adopted design strategies as a participant in the network's first stage in the current study, which essentially improvises the outcomes. There is no symmetric approach for evaluating software security in the context of design strategies with the aid of the fuzzy-ANP-TOPSIS process.

V. DISCUSSION
The main objective of this research paper is to evaluate the impact of blockchain technology models for maintaining the security of EHRs. The results drawn from this investigation would help the engineers to identify and pick the most effective blockchain model, thus making significant technological advancement in the healthcare sector. The researchers utilized MCDM's hybrid fuzzy-ANP TOPSIS system to evaluate the impact of blockchain technology models. Blockchain technology specific features including Decentralization, Immutability, as well as Security have been incorporated in this analysis so as to base the research on important issues that are currently being faced by the healthcare sector. According to the final tabulations of our study, the Private Blockchain model was acknowledged as the highest weighted alternative, thus securing the first rank amongst the selected Blockchain models to be implemented to deliver secure EHR services in the healthcare organizations. Another author, Castaldo and Cinque [55] developed a monitoring network to promote as well as enhance the sharing of electronic health data over several European countries with the help of private blockchain model and found it to be the safest possible way. Electronic health records are the foundation of the modern health-care. Therefore, supplying consumers with optimal secure healthcare frameworks with the help of private blockchain model is a much needed endeavour in healthcare sectors. The pros and limitations of the overall results in our research work are as follows:

A. PROS
Healthcare professionals as well as blockchain engineers may take support from this research study to prioritize and pick specified attributes of blockchain technology criteria to create secure and trustworthy systems for the healthcare blockchain system. Security of EHRs is a significant concern for developers as well as stakeholders, yet it is overlooked. This research study would provide practitioners with ample understanding to follow different strategies, rather than informal and conventional methods while developing a security mechanism for EHRs.

B. LIMITATIONS
This evaluation can be appropriate for healthcare professionals but is still not final, since the protection of EHRs is both a diverse and complex activity in the blockchain technology setting. Numerous new challenges were posed each day and confronted by users and developers alike. Integrated fuzzy-ANP-TOSIS is an effective and important method for impact evaluation of the blockchain technology for securing EHRs but there may be other effective MCDM symmetrical technology for multiple-criteria decision-making issues.

VI. CONCLUSION
The proposed research uses an integrated fuzzy-ANP-TOPSIS method to evaluate the impact of blockchain technology models for securing electronic healthcare records. The hybrid fuzzy-ANP-TOPSIS method offers an effective way to analyze any MCDM issue with various variables and alternatives, such as blockchain technology assessment. Different factors for the blockchain models impact evaluation are estimated, their weights are measured, alternative rankings are determined and the overall impact of blockchain models for securing EHR is assessed. It has been concluded that alternative-Private Blockchain model is the most acceptable means for offering effective and robust service in healthcare blockchain technology. Private Blockchain technology would offer more secure platforms for sharing health data in the healthcare sector by protecting the data over a distributed peer-to-peer infrastructure, thus transforming the way in which the EHRs of patients are exchanged and maintained. This research study will serve as a model or motivation for future research as well as projects of blockchain technology in healthcare settings. Our discussed methodological approaches and categorization will lead to an infrastructure or model's proposal which solves the issues addressed in healthcare blockchain technology. Therefore, a potential path for future research is to assess the implementation of healthcare blockchain technology-based services prioritized on their impact to achieve positive improvements in the healthcare sector.
MD TARIQUE JAMAL ANSARI received the bachelor's degree in computer application from the University of Lucknow, India, in 2012, the M.Sc. degree in information technology from Babasaheb Bhimrao Ambedkar University, Lucknow, in 2015, and the Ph.D. degree in information technology from the Department of Information Technology, Babasaheb Bhimrao Ambedkar University, in 2020. He is currently a Researcher with the Department of Information Technology, Babasaheb Bhimrao Ambedkar University. His research interests include security requirements engineering, software engineering, software security, blockchain, cloud security engineering, software risk management, health informatics, and big data. He received the Gold Medal for the master's degree from Babasaheb Bhimrao Ambedkar University.
MAMDOUH ALENEZI received the M.S. degree from DePaul University, in 2011, and the Ph.D. degree from North Dakota State University, in 2014. He has extensive experience in data mining and machine learning, where he applied several data mining techniques to solve several software engineering problems. He has conducted several research areas and development of predictive models using machine learning to predict fault-prone classes, comprehend source code, and predict the appropriate developer to be assigned to a new bug. He is currently the Dean of the Educational Services, and the Chief Information and Technology Officer (CITO) with Prince Sultan University.
AMAL KRISHNA SARKAR is currently pursuing the Ph.D. degree in information technology from Babasaheb Bhimrao Ambedkar University, India. He is a System Manager with the Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India. He has more than 19 years' experience in Healthcare and IT sector and four years in telecom manufactures industry. He is a passionate researcher and frequently involved in various research initiatives and his area of interest in research is healthcare data security, malware analysis, digital forensic, and cyber security methods, data migration, and so on.
MOHD FAIZAN received the bachelor's degree from the National P.G. College, University of Lucknow, India, in 2013, and the master's degree in information technology, in 2015. He is currently pursuing the Ph.D. degree in information technology with Babasaheb Bhimrao Ambedkar University (A Central University), India. He has more than two years of research and teaching experience. He has also published and presented articles in refereed journals and conferences. His research interest includes security engineering.
ALKA AGRAWAL received the Ph.D. degree from Babasaheb Bhimrao Ambedkar University (A Central University), Vidya Vihar, Lucknow. She has research/teaching experience of more than 12 years. She is currently an Assistant Professor with Babasaheb Bhimrao Ambedkar University. She is also a Passionate Researcher. She has published a number of research articles in national and international journals. Her research interests include software security, software vulnerability, big data security, genetic algorithms, and software security.
RAJEEV KUMAR received the master's and Ph.D. degrees in information technology from Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, India, in 2014 and 2019, respectively. He has more than five years of research and teaching experience. He is currently an Assistant Professor with the Department of Computer Application, Shri Ramswaroop Memorial University, Lucknow. He is also a Guest Faculty Member with the Department of Information Technology, Babasaheb Bhimrao Ambedkar University. He is a Young and Energetic Researcher. He worked on two Major Projects (With PI) funded by the University Grants Commission, New Delhi, and the Council of Science and Technology, India. He has published and presented articles in refereed journals and conferences. His research interests include security engineering and computational techniques.
RAEES AHMAD KHAN (Member, IEEE) is currently a Professor and the Head of the Department with the Department of Information Technology. He is also the Dean of the School for Information Science and Technology, Babasaheb Bhimrao Ambedkar University (A Central University), Vidya Vihar, Lucknow, India. He has more than 20 years of teaching and research experience. He has published more than 300 research publications with good impact factors in reputed international journals and conferences, including the IEEE, Springer, Elsevier, Inderscience, Hindawi, IGI Global, and so on. He has also published a number of national and international books (Authored and Edited), including Chinese Language. His research interests include software security, software quality, and quality metrics. VOLUME 8, 2020