Enhancing Security and Privacy of Patient Data in Healthcare: A SmartPLS Analysis of Blockchain Technology Implementation

Artificial Intelligence Robots And Revolutionizing Society In Terms Of Technology, Innovation, Work And Power. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 3(1), 46-52. Retrieved


Introduction
In the realm of healthcare, the security and privacy of patient data are paramount for maintaining trust and ensuring the confidentiality of sensitive information [1].However, traditional data management systems in healthcare often face challenges in safeguarding patient data, leading to potential breaches and compromises in security and privacy [2].As the digitization of healthcare records increases, innovative solutions are required to effectively address these concerns [3].Blockchain technology has emerged as a promising solution for enhancing the security and privacy of patient data in healthcare [4].By offering a decentralized and tamperproof framework, blockchain has the potential to mitigate vulnerabilities associated with centralized databases [5].Through the utilization of cryptographic algorithms and distributed consensus mechanisms, blockchain ensures the integrity and immutability of data, thereby fortifying resistance against unauthorized access and manipulation [6].
Previous studies, as exemplified by the work of Xie et al. [7] and Gupta et al. [8], have explored the potential benefits of implementing blockchain technology in healthcare, demonstrating its feasibility in securing electronic health records and underscoring its potential to enhance data security and privacy.Nevertheless, despite these contributions, there remains a notable gap in empirical research to assess the direct impact of blockchain technology on data security and privacy within healthcare settings [10].Furthermore, a scarcity of research specifically employing the SmartPLS analysis method to examine the intricate relationships between blockchain technology implementation and data security, as well as data privacy within the healthcare system, underscores the need for further investigation [11].
Hence, the primary objective of this study is to bridge this aforementioned research gap by empirically investigating the impact of implementing blockchain technology on enhancing the security and privacy of patient data within the realm of healthcare [12].By harnessing the analytical power of the SmartPLS method, this research endeavors to comprehensively assess the implications of blockchain technology implementation on data security and privacy within healthcare organizations [13].The findings of this study are anticipated to yield invaluable insights into both the advantages and challenges entailed in adopting blockchain technology to safeguard patient data, thereby making a meaningful contribution towards the advancement of more secure and privacy-centric healthcare systems.Moreover, the study's hypothesis delves into explicating how blockchain's decentralized and tamper-proof nature renders it a robust system for ensuring the safety and confidentiality of patient data within healthcare services.

Research Method
This research will adopt a quantitative research approach to investigate the impact of implementing blockchain technology on enhancing the security and privacy of patient data in healthcare services [14].The research method involves collecting primary data from a sample of healthcare organizations that have implemented blockchain technology for data management [15].The data collection process will include the distribution of surveys or questionnaires to healthcare professionals and administrators involved in the implementation and usage of blockchain technology in their respective organizations [16].
The survey instrument will be designed to gather information on various aspects related to blockchain technology implementation, including the level of adoption, the specific features and functionalities of the implemented blockchain system, and the data security and privacy measures in place [17].The questionnaire will also assess the quality of healthcare services provided and the perceived impact of blockchain technology on data security and privacy [18].The collected data will then be analyzed using the SmartPLS analysis method.SmartPLS is a statistical technique that combines partial least squares (PLS) regression with structural equation modeling (SEM) [19].It allows for the examination of complex relationships between multiple variables, providing insights into the direct and indirect effects of blockchain technology implementation on data security and privacy in healthcare services.

A. Overview of Security and Privacy Issues in Healthcare
In recent years, the healthcare industry has witnessed a surge in security and privacy concerns related to patient data.With the increasing digitization of healthcare records and the adoption of electronic health record (EHR) systems, the vulnerability of patient data to unauthorized access and breaches has become a significant challenge.Security breaches in healthcare can have severe consequences, including identity theft, financial fraud, and compromised patient care [20].
Several security and privacy issues contribute to the vulnerability of patient data in healthcare settings [21].These include insider threats, where authorized personnel misuse or leak patient information, external hacking attempts, inadequate security measures, and the lack of standardized privacy policies [22].Additionally, the interoperability of different healthcare systems and the sharing of patient data among various stakeholders raise concerns about data integrity and unauthorized access.

B. Blockchain Technology and its Potential for Enhancing Security and Privacy
Blockchain technology has emerged as a potential solution for addressing security and privacy issues in healthcare.Initially introduced as the underlying technology for cryptocurrencies like Bitcoin, blockchain offers a decentralized and immutable ledger system that can secure and authenticate transactions without the need for intermediaries [23].
The key features of blockchain, such as decentralization, immutability, transparency, and cryptographic security, make it suitable for healthcare applications.By leveraging blockchain, healthcare organizations can establish a secure and tamper-resistant system for storing and sharing patient data [24].Blockchain's decentralized nature eliminates the need for a central authority, reducing the risk of unauthorized access and data breaches.The immutability of blockchain ensures that once data is recorded, it cannot be altered without consensus from the network participants, enhancing data integrity.

C. Previous Studies on Blockchain Implementation in Healthcare
Several studies have explored the implementation of blockchain technology in healthcare to enhance security and privacy.These studies have highlighted the potential benefits of blockchain, including secure data exchange, interoperability, auditability, and patientcentric control over data sharing [25].
For instance, research has investigated the use of blockchain for securing electronic health records, tracking the provenance of pharmaceutical drugs, ensuring supply chain integrity, and enabling secure and consent-based sharing of patient data.These studies have provided valuable insights into the technical aspects, feasibility, and potential challenges associated with implementing blockchain in healthcare settings.

Hypotheses
To analyze the impact of blockchain technology implementation on the security and privacy of patient data in healthcare, a theoretical framework can be developed.The framework should incorporate relevant constructs and variables to measure the effectiveness of blockchain in enhancing security and privacy.
Hypotheses can be formulated based on the theoretical framework to test the relationships between the independent and dependent variables.These hypotheses may include statements such as: H1: Blockchain technology implementation positively affects the security of patient data in healthcare.H2: Blockchain technology implementation positively affects the privacy of patient data in healthcare.

H3:
The level of data integrity in healthcare systems positively influences the effectiveness of blockchain in enhancing security and privacy.H4: The level of interoperability among healthcare systems positively influences the effectiveness of blockchain in enhancing security and privacy.These hypotheses can be empirically tested using a suitable research methodology, such as SmartPLS (Partial Least Squares) analysis, to determine the impact of blockchain technology implementation on the security and privacy of patient data in healthcare.
By conducting a comprehensive literature review, researchers can gain a better understanding of the existing knowledge and identify research gaps in the field of enhancing security and privacy of patient data through blockchain technology implementation.

Findings
Various performance methodologies, such as representative performance indicators and performance evaluations, often need to be defined in a blockchain framework.We also need to understand the intricacies of blockchain performance evaluation and how deploying this technology will improve the security of patient data in healthcare.

A. Convergent validity
To assess the adequacy of convergence, SmartPLS provides several measures such as Average Variance Extraction (AVE), Cronbach's Alpha, Combined Reliability (CR), and Indicator Decomposition.To assess the validity of convergence, we need to check that the index is heavily loaded.This suggests his AVE > 0.5, Cronbach's alpha > 0.7, and CR > 0.7 for each indicator, especially those that measure latent variables.Moreover, the mutual loading of the indicators should be high, suggesting that each indicator primarily measures this latent variable and not the others.

B. Discriminant Validity
SmartPLS provides several measures to assess discriminant validity, including the Fornell-Laker criteria, heteromorphic-to-monomorphic ratio (HTMT) and indicator crossloads.The Fornell-Larcker criterion involves comparing the square root of the AVE of each latent variable with the correlation between that latent variable and other latent variables in the model.Discriminant validity is supported if the square root of AVE for a particular latent variable is greater than the correlation between that latent variable and the other latent variables in the model.  1 meet the requirement of being greater than 0.5.This is Cronbach's alpha requirement.The composite confidence score should also be 0.5 or higher.Therefore, all composite confidence scores meet the specified criteria.In addition, data were collected with a mean variance greater than 0.1.Figures 3, 4, and 5 also show that all requirements are met and all values are within the specified limits.

D. Tests results for Discriminant Validity
The HTMT ratio compares the correlation between measures of different latent variables to the correlation between measures of the same latent variable.Discriminant validity is supported when the correlation ratio between different latent variables is less than 1.8.Two tests were performed to determine the difficulty of the structure.Validity of convergence and discrimination.A convergence validity test confirms that items related to the variable of interest are well correlated with that variable.Discriminant validity tests, on the other hand, are used to show the lack of association between different subsets of variables.This is done to show that items associated with different variables in different datasets are irrelevant.Since there is no relationship between the items, the model can assess the importance of the interest rate variables.2, 3, and 4. Note that based on the graph in Fig. 7, all values exceed the cutoff of -0.280 and may also exceed the hetero--mono trait value.No hetero-mono traits were found.This condition is also met when the diagonal values of the corresponding columns are large, as is the case for Fornell and Lercher criteria.Table 3 shows that all items in each variable are significantly correlated with the variable itself, but not with other items or items in other variables.Add one or more elements from different variables.Finally, you can also check indicator loadings to ensure that each indicator is more closely related to that latent variable than to other latent variables in the model.If the indicator has high cross-loading with other latent variables, this suggests that the indicator may be measuring multiple components.on patient data privacy in the medical field, there will be significant benefits.The impact of blockchain on competitiveness also has significant benefits.Moreover, the t-test for the directional hypothesis is greater than 2.211.Positive Impact of Variables The level of data integrity in healthcare systems has a positive impact on blockchain's effectiveness in enhancing security and privacy.The desired t-test value is then examined and evaluated.In addition, the initial mean beta also shows a positive or negative direction, and these positive values being positive indicates that all values are positively related.The effects of independent factors on path coefficients and dependent variables are shown in Figures 8, 9, 10, and 11. Figure 8 shows the correlations between various variables and also shows the image model created with SmartPLS.The formula for the startup-investor matching algorithm is:

Startups and investor representatives:
Each launch is denoted by s. s = {s1, s2, ..., sn}, where n is the number of launches.Each investor is denoted by i. i = {i1,i2,...,im}, where m is the number of investors.

Criteria for Startup and Investor Representation:
Each startup has up-to-date funding and performance criteria represented by criteria_s.

Each investor has investment criteria represented by criteria_i. Computing the fitness score:
Fit Score (FS) is used to assess the fit between startup criteria and investor criteria.FS(s,i) is the function that produces the fit score between startup and investor i.

Matchmaking algorithm:
step 1: For each startup s and each investor i, compute the match score FS(s,i) based on the match score formula.
Step 2: Determine the startup-investor pair with the highest match score as a result of matchmaking.
Step 3: View matchmaking results that provide matching pairs of startup investors.

Recommendation system:
Startups and investors can use our recommendation system to receive suggestions based on scores and suitability criteria.We can propose recommendations based on the latest investment standards and the "Investor Catalog" that allows you to view the performance of individual investors.In practical implementations, the weight values (α, β, γ, ..., δ) and the functions for calculating specific characteristic values (FS1, FS2, FS3, ..., FSn) require a special study should be used to determine Context and business needs.

Conclusion
The conclusions drawn appear to stem from a limited dataset, which may raise concerns about potential overgeneralization.It is crucial to ensure the representative nature of the results and exercise caution in drawing conclusions.This study delved into the effects of implementing blockchain technology on bolstering the security and privacy of patient data within the healthcare sector.Employing the SmartPLS analysis method, the investigation scrutinized the intricate connections between blockchain technology implementation and data security and privacy within the healthcare framework.The study sample encompassed healthcare organizations that had integrated blockchain technology for data management purposes.The findings unveiled a positive correlation between blockchain technology implementation and the enhancement of

Figure 11 .Enhancing
Figure 11.Model T-test ResultF.Explanation of ResultsAs you can see, the beta value, p-value, and t-value can be used to determine if a hypothesis is supported.As you can see, the adoption of blockchain technology positively impacting patient data security in healthcare has a p-value of 0.006, well below the threshold of 0.05.Moreover, if factors such as the adoption of blockchain technology have a positive impact

Table 1 .
Tests Results For Convergent Validity