Blockchain Papers

Kashfi Shormita Kushal, Tanvir Ahmed, Md. Ashraf Uddin, Muhammed Nasir Uddin

In recent years, COVID-19 has impacted millions of individuals worldwide, resulting in numerous fatalities across several countries. While RT-PCR technology remains the most reliable method for detecting COVID-19, it is both expensive and time-consuming. As a result, researchers have explored various machine learning and deep learning-based approaches to rapidly identify COVID-19 cases using X-ray images, with reduced costs and shorter processing times. However, preserving patient confidentiality poses challenges within third-party-controlled systems, potentially failing to safeguard patients from potential disgrace and discomfort. Nonetheless, blockchain technology offers the potential to securely store sensitive medical data anonymously, without requiring third-party intervention. Consequently, the combination of deep learning and blockchain could offer a viable solution to mitigate the spread of COVID-19 while ensuring patient privacy protection. In this paper, we propose a hybrid model of blockchain and deep learning model for automatically detecting COVID-19 using chest X-rays (CXR). The deep learning model includes a stacking ensemble of three modified pre-trained Deep Learning (DL) models: VGG16, Xception, and DenseNet169. The model obtained an accuracy of 99.10% and 98.60% for binary and multi-class respectively. Further, To ensure COVID-19 patients’ privacy and security, the Ethereum blockchain has been adopted to store information related to COVID-19 cases. In addition, a smart contract on the blockchain has been designed for handling X-ray images in the Interplanetary File System (IPFS).

Abdulaziz Aljaloud

In smart systems context, the storage and distribution of health-critical data – medical images, test reports, clinical information etc. that is processed and transmitted via web portal and pervasive devices which requires a secure and efficient management of patients’ medical records. The reliance on centralized data centers in the cloud to process, store, and transmit patients’ medical records poses some critical challenges including but not limited to operational costs, storage space requirements, and importantly threats and vulnerabilities to the security and privacy of health-critical data. To address these issues, this research proposes a framework and provides a proof-of-the-concept named Patient-Centric Medical Image Management System (PCMIMS). The proposed solution PCMIMS utilizes the Ethereum blockchain and Inter-Planetary File System (IPFS) to enable secure and decentralized storage capabilities that lack in existing solution for patients’ medical image management. The PCMIMS design facilitates secure access to Patient-Centric information for health units, patients, medics, and third-party requestors by incorporating the Patient-Centric access control protocol, ensuring privacy and control over medical data. The proposed framework is validated through the deployment of a prototype based on smart contract executed on Ethereum TESTNET blockchain that demonstrates efficiency and feasibility of the solution. Validation results highlight a correlation between (i) number of transactions (i.e., data storage and retrieval), (ii) gas consumption (i.e., energy efficiency), and (iii) data size (volume of Patient-Centric medical images) via repeated trials in Microsoft Windows environment. Validation results also indicate computational efficiency of the solution in terms of processing three most common types of Patient-Centric medical images namely (a) Magnetic resonance imaging (MRI) (b) X-radiation (X-Rays), (c) Computed tomography (CT) scan. This research primarily contributes by designing, implementing, and validating a blockchain based practical solution for efficient and secure management of Patient-Centric medical image management in the context of smart healthcare systems.

Nouhaila El Akrami, Mohamed Hanine, Emmanuel Soriano Flores, Daniel Gavilanes Aray · 5 authors

Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML.

Rima Kaafarani, Leila Ismail, Oussama Zahwe

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

Lennart Ante, Ender Demir

ChatGPT is an artificial intelligence (AI) chatbot that provides users with detailed responses and accurate answers to any questions. It has garnered significant attention after its launch in November 2022. We analyze the returns of AI-themed crypto assets around the launch and widespread attention towards ChatGPT. We reveal significant abnormal returns for AI tokens after the launch of ChatGPT, up to 41% over the course of two weeks. Moreover, 90% of tokens exhibit positive abnormal returns. This suggests that the attention towards ChatGPT and AI in general has transitioned to cryptocurrency markets, resulting in positive price effects for AI-related cryptocurrencies.

Francisca Chibugo Udegbe, Ejike Innocent Nwankwo, Geneva Tamunobarafiri Igwama, Janet Aderonke Olaboye

The integration of blockchain technology in biomedical diagnostics offers a promising solution to the challenges of data security and privacy in infectious disease surveillance. As the digitalization of healthcare systems accelerates, the need to protect sensitive health information becomes increasingly critical. Blockchain, with its decentralized and immutable nature, provides a robust framework for ensuring the integrity and confidentiality of biomedical data. This abstract explores how blockchain technology can be leveraged to enhance data security and privacy in the context of infectious disease surveillance, where rapid and accurate data sharing is essential for effective public health responses. Infectious disease surveillance relies on the collection, analysis, and dissemination of large volumes of data, often shared across multiple institutions and geographical regions. Traditional systems for managing this data are vulnerable to breaches, unauthorized access, and data tampering, which can compromise public health efforts and patient privacy. Blockchain technology addresses these vulnerabilities by enabling secure, transparent, and tamper-proof data exchanges. Each transaction or data entry is recorded in a distributed ledger, accessible only to authorized participants, thus ensuring that the data remains secure and unaltered. Moreover, blockchain’s inherent transparency allows for real-time monitoring and auditing of data flows, which is crucial in the timely detection and response to infectious disease outbreaks. The use of smart contracts within blockchain networks further enhances the automation and efficiency of data management, ensuring that data is only accessed and shared according to predefined rules and conditions. This not only safeguards patient privacy but also builds trust among stakeholders, including patients, healthcare providers, and public health authorities. In conclusion, the integration of blockchain technology in biomedical diagnostics presents a transformative approach to addressing the critical issues of data security and privacy in infectious disease surveillance. By leveraging blockchain's unique features, healthcare systems can ensure that sensitive diagnostic data is protected, thus supporting more effective and secure public health interventions in the fight against infectious diseases. Keywords: Blockchain, Biomedical Diagnostics, Data Security, Privacy, Infectious Disease Surveillance.

Wei Zhang

Blockchain technology can reduce the need for intermediaries in various types of transactions by providing a decentralized and secure ledger that can be accessed and updated by all parties involved in the transaction. Clinical trials are essential for bringing new drugs and therapies to market, but the current clinical research process is often marred by inefficiencies, data inaccuracies, and a lack of transparency. The implementation of blockchain technology in clinical trials has the potential to address these challenges by providing a secure and transparent platform for data management. By leveraging the power of blockchain, healthcare providers can improve the integrity and accuracy of clinical trial data, enhance trust in the clinical research process, and ultimately improve patient outcomes. In this article, we propose the use of blockchain technology in clinical trials and explore its potential benefits for the healthcare. The implementation of a blockchain-based data management system for clinical trials holds significant potential to address several challenges associated with the current clinical research process. By improving the integrity and security of medical data, enhancing trust, and easing regulatory burden, such a system can promote the efficient and effective conduct of clinical trials. The adoption of a blockchain-based solution for clinical trial data management has the potential to optimize costs, contributing to the sustainability of healthcare services. It also provides a model for future research and development of blockchain-based solutions in the field of clinical research.

James Pietris, Stephen Bacchi, Sebastian Wiech, Yiran Tan · 8 authors

No abstract is available for this record.

Stefano Marzo, Royston Pinto, Lucy McKenna, Rob Brennan

Federated learning (FL) is a distributed machine learning<br> approach that enables remote devices i.e. workers to collaborate to compute<br> the fitting of a neural network model without sharing their data.<br> While this method is favorable to ensure data privacy, an imbalanced<br> data distribution can introduce unfairness in the model training, causing<br> discriminatory bias towards certain under-represented groups. In this paper,<br> we show that imbalance federated data decreases indexes of equity<br> i.e. differences in treatment for underrepresented classes. To address the<br> problem, we propose a federated learning framework called Z-Fed that 1)<br> balances the training without exchange of privacy protected data using<br> a zero knowledge proof (ZKP) technique, and 2) allows for the collection<br> of information on data distributions based on one or more categorical<br> features to produce metadata about population proportions. The proposed<br> framework infers the precise data distribution without exchanging<br> knowledge of the data categories and uses it to coordinate a balanced<br> training set. Z-Fed aims to mitigate the effect of imbalanced data in<br> FL while respecting privacy and without using mediators or probabilistic<br> approaches. Compared to a non-balanced framework, Z-Fed improves<br> fairness and equality measured in equal opportunities (EPD) by 53.54%,<br> equal odds (EOD) by 56.41%, and statistical parity (SPD) by 46.1% on<br> imbalanced UTK datasets, reducing biased predictions among subgroups.<br> EPD, EOD, and SPD measure the disparity of treatment between privileged<br> e.g. over-represented and non-privileged groups. Given the results<br> obtained, Z-Fed can reduce discriminatory behaviors and enhance trustworthy<br> of federated learning.

Veronika Stephanie, Ibrahim Khalil, Mohammed Atiquzzaman, Xun Yi

The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of Things-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in artificial intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This article presents a secure multiparty computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.

SACHI CHAUDJARY, Riya Kakkar, Rajesh Gupta, Sudeep Tanwar · 6 authors

The advent of telemedicine with its remote surgical procedures has effectively transformed the working of healthcare professionals. The evolution of telemedicine facilitates the remote monitoring of patients that lead to the advent of telesurgery systems, i.e. one of the most critical applications in telemedicine systems. Apart from gaining popularity, the telesurgery system may encounter security and trust issues of patients? data while communicating with the surgeon for their remote treatment. Motivated by this, we have presented a comprehensive survey on secure telesurgery systems comprising healthcare, surgical robots, traditional telesurgery systems, and the role of artificial intelligence to deal with the numerous security attacks associated with the patients' health data. Furthermore, we propose a blockchain and federated learning-based secure telesurgery system to secure the communication between patient and surgeon. The results of the proposed system are better than those of the traditional system in terms of improved latency, low data storage cost, and enhanced data offloading. Finally, we explore the research challenges and issues associated with the telesurgery system.

Abdulhammed Opeyemi Babatunde, Taofeeq Oluwatosin Togunwa, Olutola Awosiku, Mohd Faizan Siddiqui · 8 authors

OPINION article Front. Public Health, 24 October 2022Sec. Digital Public Health Volume 10 - 2022 | https://doi.org/10.3389/fpubh.2022.1024203

Sebastian Griewing, Michael Lingenfelder, Uwe Wagner, Niklas Gremke

This study aims at evaluating the use case potential of breast cancer care for artificial intelligence and blockchain technology application based on the patient data analysis at Marburg University Hospital and, thereupon, developing a digital workflow for breast cancer care. It is based on a retrospective descriptive data analysis of all in-patient breast and ovarian cancer patients admitted at the Department of Gynecology of Marburg University Hospital within the five-year observation period of 2017 to 2021. According to the German breast cancer guideline, the care workflow was visualized and, thereon, the digital concept was developed, premised on the literature foundation provided by a Boolean combination open search. Breast cancer cases display a lower average patient case complexity, fewer secondary diagnoses, and performed procedures than ovarian cancer. Moreover, 96% of all breast cancer patients originate from a city with direct geographical proximity. Estimated circumference and total catchment area of ovarian present 28.6% and 40% larger, respectively, than for breast cancer. The data support invasive breast cancer as a preferred use case for digitization. The digital workflow based on combined application of artificial intelligence as well as blockchain or distributed ledger technology demonstrates potential in tackling senological care pain points and leveraging patient data safety and sovereignty.

Shengwen Ding, Chenhui Hu

Machine learning (ML) has penetrated various fields in the era of big data. The advantage of collaborative machine learning (CML) over most conventional ML lies in the joint effort of decentralized nodes or agents that results in better model performance and generalization. As the training of ML models requires a massive amount of good quality data, it is necessary to eliminate concerns about data privacy and ensure high-quality data. To solve this problem, we cast our eyes on the integration of CML and smart contracts. Based on blockchain, smart contracts enable automatic execution of data preserving and validation, as well as the continuity of CML model training. In our simulation experiments, we define incentive mechanisms on the smart contract, investigate the important factors such as the number of features in the dataset (num_words), the size of the training data, the cost for the data holders to submit data, etc., and conclude how these factors impact the performance metrics of the model: the accuracy of the trained model, the gap between the accuracies of the model before and after simulation, and the time to use up the balance of bad agent. For instance, the increase of the value of num_words leads to higher model accuracy and eliminates the negative influence of malicious agents in a shorter time from our observation of the experiment results. Statistical analyses show that with the help of smart contracts, the influence of invalid data is efficiently diminished and model robustness is maintained. We also discuss the gap in existing research and put forward possible future directions for further works.

Chaka Chaka

Much has been written about the fourth industrial revolution’s (4IR) contributions to and its impact on higher education (HE). In addition, review studies have been conducted on the contributions of 4IR technologies to and on their impact on HE. Most of these studies have reviewed single 4IR technologies in isolation as attested to by the review studies cited in the current study. Against this backdrop, the current study reviewed, discussed, and synthesized the applications, prospects, and challenges of artificial intelligence (AI), robotics, and blockchain at given higher education institutions (HEIs) between 2013 and 2019 as reported by 26 selected journal articles. Employing a slightly modified version of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for searching and screening, three of the findings of this study are worth mentioning. Firstly, the dominant AI technologies for learning are chatbots, and AI holds the prospect of personalized, scalable, and affordable learning. Secondly, the applications of robotics are exploratory in nature, and have a meta-teaching and a meta-learning orientation. Thirdly, some of the applications of blockchain relate to digital grading, digital credentialing and digital certification, and to real-time contracting and time stamping of learning. The implications of this review are that the three sets of technologies reviewed, have a lot applications for HE, barring the challenges that have been outlined.

Ziyu Wang, Lei Cai, Xuewu Zhang, Chang Choi · 5 authors

Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.

Md Rahat Ibne Sattar, Md. Thowhid Bin Hossain Efty, Taiyaba Shadaka Rafa, Tusar Das · 8 authors

Nowadays, the online platform has been used by many educational institutions, to conduct tests, especially for secondary to tertiary level students. The most popular online test program is run by providing a user id and password to the candidates, and subsequently, they log in to the given web page to answer the questions. However, this system has a lot of bugs, the password can be misused followed by cheating in the test. This shows the importance of a secure system being implemented to avoid such a problem. This paper presents a blockchain framework that secures the online examination system. The proposed framework has been used to secure a data management system that connects to existing educational data. Institutions can simply compile their data history without requiring a copy from the central servers. The proposed blockchain framework improves data security and removes any potential cheating between users or third-party institutions that access applications and services. In this regard, this study provides a secured framework for conducting and evaluating subject tests to ensure consistency between student and server, and secure delivery of questionnaire from the server.

R. Durga, E. Poovammal

With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

Rucha Shinde, Shruti Patil, Ketan Kotecha, Vidyasagar Potdar · 6 authors

Abstract Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

Ana Côrte-Real, Tiago Nunes, Clara Cruz Santos, Paulo Rupino da Cunha

The increase of forcibly displaced people worldwide is a challenge for health systems and their ability to provide access and equity in Health as a universal right. Health information systems should be strengthened to collect and disseminate migrant health data enabling analytics for strategic decisions.This Viewpoint focuses on blockchain technology as an emergent digital tool to improve communication and overcome gaps in medical data sharing, conceptualizing a global health space. Anchored in the security, privacy, and medico-legal regulation of medical data, Blockchain technology would empower inter-organizational services or workflows, in real-time, by the users, inside and outside the national health systems, anywhere in the world. As an innovative approach, this Viewpoint highlights the future directions in IT-supported health.

Ioannis Skalidis, Olivier Müller, Stéphane Fournier

The recent pandemic launched an acceleration in adopting telemedicine by cardiovascular health and triggered the flourishing of technological advancements, such as the metaverse, which is a novel interactive mix of digital worlds that leverages augmented reality with virtual reality. The CardioVerse represents a theoretical term for the embracement of the metaverse by cardiovascular medicine, encompassing the endless possibilities as well as the challenges that it holds and introduces new dimensions to disease education, prevention and diagnosis. Its applications are numerous, notably in enhancing medical visits, assisting cardiovascular interventions and reshaping the way medical education is provided. Although obstacles are expected in diverse domains such as security, technical, legislative and regulatory, the utilization of non-fungible tokens as a security asset for patient data appears as potential solution.

Arash Heidari, Shiva Toumaj, Nima Jafari Navimipour, Mehmet Ünal

No abstract is available for this record.

Amirhossein Peyvandi, Babak Majidi, Soodeh Peyvandi, Jagdish C. Patra

No abstract is available for this record.

Mustafa Abdul‐Moheeth, Muhammad Usman, Daniel Toshio Harrell, Anjum Khurshid

Background: The current healthcare ecosystem in the United States is plagued by inefficiencies in transitions of patient care between healthcare providers due in large part to a lack of interoperability among the many electronic medical record (EMR) systems that exist today. Both providers and patients experience significant frustration due to the negative effects of increased costs, unnecessary administrative burden, and duplication of services that occur because of data fragmentation in the system. Blockchain technology provides a potential solution to mitigate or eliminate these gaps by allowing for exchange of healthcare information that is distributed, auditable, immutable, and respectful of patient autonomy. Our multidisciplinary team identified key tasks required for a transition of care to design and develop a blockchain application, MediLinker, which served as a patient-centric identity management system to address issues of data fragmentation ultimately aiding in the delivery of high-value care services. Methods: The MediLinker application was evaluated for its ability to accomplish various key tasks needed for a successful transition of patient care in an outpatient setting. Our team created 20 unique patient use cases covering a diversity of medical needs and social circumstances that were played out by participants who were asked to perform various tasks as they received case across a simulated healthcare ecosystem composed of four clinics, a research institution, and other ancillary public services. Tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. With this iteration of MediLinker, we specifically focused on the functionality of digital guardianship and patient revocation of healthcare information. In addition, throughout the simulation, we surveyed participant perceptions regarding the use of MediLinker and blockchain technology to better ascertain comfortability and usability of the application. Results: Quantitative evaluation of simulation results revealed that MediLinker was able to successfully accomplish all seven clinical scenarios tested across the 20 patient use cases. MediLinker successfully achieved its goal of patient-centered interoperability as participants transitioned their simulated healthcare data, including COVID-19 vaccination status and current medications, across the four clinic sites and research institution. In addition to completing all key tasks designated, all eligible participants were able to enroll with and subsequently revoke data access with our simulated research site. MediLinker had a low data-entry error rate, with most errors occurring due to work-flow vulnerabilities. Our qualitative analysis of user perceptions indicated that comfortability and trust with blockchain technology, such as MediLinker, grew with increased education and exposure to such technology. Conclusions: The ubiquitous problem of data fragmentation in our current healthcare ecosystem has placed considerable strain on providers and patients alike. Blockchain applications for health identity management, such as MediLinker, provide a viable solution to stem the inefficiencies that exist today. The interoperability that MediLinker provided across our simulated healthcare system has the potential to improve transitions of care by sharing key aspects of healthcare information in a timely, secure, and patent-centric fashion allowing for the delivery of consistent and personalized high value care. Blockchain technologies appear to face similar challenges to widespread adoption as other novel interventions, namely recognition, trust, and usability. Further development and scaling are required for such technology to realize its full potential in the real world and transform the practice of modern health care.