Exploring tourism business model importance with the emergence of blockchain system: directions for tourism industry of China.

The study aims to explore the importance of the tourism business model with the emergence of the blockchain platform in China. The study focused on the importance of the tourism business model of china, studied the need to improve the tourism business infrastructure, and traced the value of the blockchain system in the tourism industry of china. For this, the researchers used a semi-structured interview approach to conduct a qualitative research design. About nine Chinese tourism and travel industry experts were interwar after initial screening using purposive sampling techniques. The respondents' responses were analyzed by applying a thematic analysis approach, and by this, the researchers extracted the main themes on study topicality to fill the gap in the literature. The study's novelty is in its topicality and context, for which it also provides viable, practical directions for stakeholders.

Toward an Intelligent Blockchain IoT-Enabled Fish Supply Chain: A Review and Conceptual Framework.

The fish industry experiences substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) are expected to transform the fish supply chain (SC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promote secure data sharing and employ IUU prevention and detection methods. We have reviewed current research efforts directed toward incorporating Blockchain in fish SC systems. We have discussed traceability in both traditional and smart SC systems that make use of Blockchain and IoT technologies. We demonstrated the key design considerations in terms of traceability in addition to a quality model to consider when designing smart Blockchain-based SC systems. In addition, we proposed an Intelligent Blockchain IoT-enabled fish SC framework that uses DLT for the trackability and traceability of fish products throughout harvesting, processing, packaging, shipping, and distribution to final delivery. More precisely, the proposed framework should be able to provide valuable and timely information that can be used to track and trace the fish product and verify its authenticity throughout the chain. Unlike other work, we have investigated the benefits of integrating machine learning (ML) into Blockchain IoT-enabled SC systems, focusing the discussion on the role of ML in fish quality, freshness assessment and fraud detection.

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.

Implementation of Blockchain Technology Could Increase Equity and Transparency in Organ Transplantation: A Narrative Review of an Emergent Tool.

In the last few years, innovative technology and health care digitalization played a major role in all medical fields and a great effort worldwide to manage this large amount of data, in terms of security and digital privacy has been made by different national health systems. Blockchain technology, a peer-to-peer distributed database without centralized authority, initially applied to Bitcoin protocol, soon gained popularity, thanks to its distributed immutable nature in several non-medical fields. Therefore, the aim of the present review (PROSPERO N° CRD42022316661) is to establish a putative future role of blockchain and distribution ledger technology (DLT) in the organ transplantation field and its role to overcome inequalities. Preoperative assessment of the deceased donor, supranational crossover programs with the international waitlist databases, and reduction of black-market donations and counterfeit drugs are some of the possible applications of DLT, thanks to its distributed, efficient, secure, trackable, and immutable nature to reduce inequalities and discrimination.

Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions.

OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

Enabling Value Co-Creation in Healthcare through Blockchain Technology.

The COVID-19 pandemic highlighted the need to manage complex relations within the healthcare ecosystem. The role of new technologies in achieving this goal is a topic of current interest. Among them, blockchain technology is experiencing widespread application in the healthcare context. The present work investigates how this technology fosters value co-creation paths in the new digital healthcare ecosystems. To this end, a multiple case study has been conducted examining the development and application of blockchain by 32 healthcare tech companies. The results show blockchain technology adoption's current and potential impacts on value co-creation regarding data and resource sharing, patient participation, and collaboration between professionals. Three main areas of activity emerge from the case studies where blockchain implementation brings significant benefits for value co-creation: improving service interaction, impacting actors' engagement, and fostering ecosystem transparency.

Analyzing the Prospects of Blockchain in Healthcare Industry.

Deployment of secured healthcare information is a major challenge in a web-based environment. eHealth services are subjected to same security threats as other services. The purpose of blockchain is to provide a structure and security to the organization data. Healthcare data deals with confidential information. The medical records can be well organized and empower their propagation in a secured manner through the usage of blockchain technology. The study throws light on providing security of health services through blockchain technology. The authors have analyzed the various aspects of role of blockchain in healthcare through an extensive literature review. The application of blockchain in COVID-19 has also been analyzed and discussed in the study. Further application of blockchain in Indian healthcare has been highlighted in the paper. The study provides suggestions for strengthening the healthcare system by blending machine learning, artificial intelligence, big data, and IoT with blockchain.

Applying Blockchain Technology in Network Public Opinion Risk Management System in Big Data Environment.

Network public opinion represents public social opinion to a certain extent and has an important impact on formulating national policies and judgment. Therefore, China and other countries attach great importance to the study of online public opinion. However, the current researches lack the combination of theory and practical cases and lack the intersection of social and natural sciences. This work aims to overcome the technical defects of traditional management systems, break through the difficulties and pain points of existing network public opinion risk management, and improve the efficiency of network public opinion risk management. Firstly, a network public opinion isolation strategy based on the infectious disease propagation model is proposed, and the optimal control theory is used to realize a functional control model to maximize social utility. Secondly, blockchain technology is used to build a network public opinion risk management system. The system is used to conduct a detailed study on identifying and perceiving online public opinion risk. Finally, a Chinese word segmentation scheme based on Long Short-Term Memory (LSTM) network model and a text emotion recognition scheme based on a convolutional neural network are proposed. Both schemes are validated on a typical corpus. The results show that when the system has a control strategy, the number of susceptible drops significantly. Two days after the public opinion is generated, the number of susceptible people decreased from 1,000 to 250; 3 days after the public opinion is generated, the number of susceptible people stabilized. 2 days after the public opinion is generated, the number of lurkers increased from 100 to 620; 3 days after the public opinion is generated, the number of lurkers stabilized. The data demonstrate that the designed isolation control strategy is effective. Changes in public opinion among infected people show that quarantine control strategies played a significant role in the early days of Corona Virus Disease 2019. The rate of change in the number of infections is more affected when quarantine controls are increased, especially in the days leading up to the outbreak. When the system adopts the optimal control strategy, the influence scope of public opinion becomes smaller, and the control becomes easier. When the dimension of the word vector of emergent events is 200, its accuracy may be higher. This method provides certain ideas for blockchain and deep learning technology in network public opinion control.

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach.

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic.

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.

Toward Better Food Security Using Concepts from Industry 5.0.

The rapid growth of the world population has increased the food demand as well as the need for assurance of food quality, safety, and sustainability. However, food security can easily be compromised by not only natural hazards but also changes in food preferences, political conflicts, and food frauds. In order to contribute to building a more sustainable food system-digitally visible and processes measurable-within this review, we summarized currently available evidence for various information and communication technologies (ICTs) that can be utilized to support collaborative actions, prevent fraudulent activities, and remotely perform real-time monitoring, which has become essential, especially during the COVID-19 pandemic. The Internet of Everything, 6G, blockchain, artificial intelligence, and digital twin are gaining significant attention in recent years in anticipation of leveraging the creativity of human experts in collaboration with efficient, intelligent, and accurate machines, but with limited consideration in the food supply chain. Therefore, this paper provided a thorough review of the food system by showing how various ICT tools can help sense and quantify the food system and highlighting the key enhancements that Industry 5.0 technologies can bring. The vulnerability of the food system can be effectively mitigated with the utilization of various ICTs depending on not only the nature and severity of crisis but also the specificity of the food supply chain. There are numerous ways of implementing these technologies, and they are continuously evolving.

Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images.

Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.

Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.

COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.

A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain.

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.

Blockchain Powered Vaccine Efficacy for Pharma Sector.

Infectious and contagious diseases exist in humanity for many centuries which causes a curb in the growth of the population. Immunization plays a vital role to prevent mortality and morbidity against infectious diseases. COVID-19 pandemic continues to rage the urgency of developing a vaccine that should ensure the safety, efficacy, swift and fair deployment, implementation, and monitoring of vaccines across the globe. In the present context, the vaccine production to immunization campaign is a critical challenge. Therefore, an effective vaccine supply chain mechanism is required to address issues such as counterfeit vaccines, reduce vaccine wastages, and vaccine record fraud. In this paper, a blockchain-enabled vaccine supply chain is proposed to ensure the correctness, transparency, trust, and immutable log and improve the efficiency of vaccine distribution in the cold chain. The uniqueness of the proposed system is to provide distributed system to verify the reliability and efficacy of the vaccine from production to end beneficiaries' feedback about the vaccine. Our proposed system gives a clear view to the users as well as to the healthcare provider about the vaccination and ensures the anticounterfeit vaccine. The proposed system minimizes counterfeit vaccines and records, provides transparent communication between stakeholders in the supply chain, and improves the security of the vaccine supply chain and immutable feedback system about the vaccine.

Urban monitoring, evaluation and application of COVID-19 listed vaccine effectiveness: a health code blockchain study.

OBJECTIVE: By using health code blockchain, cities can maximise the use of personal information while maximising the protection of personal privacy in the monitoring and evaluation of the effectiveness of listed vaccines. DESIGN: This study constructs an urban COVID-19 listed vaccine effectiveness (VE) monitoring, evaluation and application system based on the health code blockchain. This study uses this system and statistical simulation to analyse three urban application scenarios, namely evaluating the vaccination rate (VR) and determining the optimal vaccination strategy, evaluating herd immunity and monitoring the VE on variant. MAIN OUTCOME MEASURES: The primary outcomes first establish an urban COVID-19 listed VE monitoring, evaluation and application system by using the health code blockchain, combined with the dynamic monitoring model of VE, the evaluation index system of VE and the monitoring and evaluation system of personal privacy information use, and then three measures are analysed in urban simulation: one is to take the index reflecting urban population mobility as the weight to calculate the comprehensive VR, the second is to calculate the comprehensive basic reproduction number (R) in the presence of asymptomatic persons, the third is to compare the difference between the observed effectiveness and the true effectiveness of listed vaccines under virus variation. RESULTS: Combining this system and simulation, this study finds: (1) The comprehensive VR, which is weighted to reflect urban population mobility, is more accurate than the simple VR which does not take into account urban population mobility. Based on population mobility, the algorithm principle of urban optimal vaccination strategy is given. In the simulation of urban listed vaccination involving six regions, programmes 1 and 5 have the best protective effect among the eight vaccination programmes, and the optimal vaccination order is 3-5-2-4-6-1. (2) In the presence of asymptomatic conditions, the basic reproduction number, namely R0*(1-VR*VE), does not accurately reflect the effect of herd immunity, but the comprehensive basic reproduction number (R) should be used. The R is directly proportional to the proportion of asymptomatic people (aw) and the duration of the incubation period (ip), and inversely proportional to the VR, the VE and the number of days transmitted in the ip (k). In the simulation analysis, when symptomatic R0=3, even with aw=0.2, the R decreases to nearly 1 until the VR reaches 95%. When aw=0.8, even when the entire population is vaccinated, namely VR=1, the R is 1.688, and still significantly greater than 1. If the R is to be reduced to 1, the VE needs to be increased to 0.87. (3) This system can more comprehensively and accurately grasp the impact of the variant virus on urban VE. The traditional epidemiological investigation can lose the contacts of infected persons, which leads to the deviation between the observed effectiveness and the true effectiveness. Virus variation aggravates the loss, and then increases the deviation. Simulation case 1 assumes the unvaccinated rate of 0.8, the ongoing VR of 0.1, the completed VR of 0.1 and an average infection rate of 2% for the variant virus. If a vaccine is more than 90% effectiveness against the premutant virus, but only 80% effectiveness against the mutant virus, and because 80% of the unvaccinated people who are not infected are not observed, the observed effectiveness of the vaccine is 91.76%, it will lead to the wrong judgement that the VE against the variant virus is not decreased. Simulation case 2 assumes the unvaccinated rate of 0.8, the ongoing VR of 0.1, the completed VR of 0.1 and an average infection rate of 5% for the variant virus. Simulation finds that the higher the proportion of unvaccinated infected people who are not observed, the lower the estimate of observed effectiveness; and the lower the true effectiveness, the larger the gap between observed effectiveness and true effectiveness. Simulation case 3 assumes the unvaccinated rate of 0.2, the ongoing VR of 0.2, the completed VR of 0.6 and an average infection rate of 2% for the variant virus. Simulation finds that the higher the proportion of unobserved completed vaccination patients who are not infected, the lower the estimate of observed effectiveness; and the lower the true effectiveness, the larger the gap between observed effectiveness and true effectiveness. Simulation case 4 assumes the unvaccinated rate of 0.2, the ongoing VR of 0.2, the completed VR of 0.6 and an average infection rate of 5% for the variant virus. If a vaccine is more than 90% effectiveness against the premutant virus, but only 80% effectiveness against the mutant virus, and because 80% of the infected people with complete vaccination are not observed, the observed effectiveness of the vaccine is 91.95%, similar to case 1, it will lead to the wrong judgement that the VE against the variant virus is not decreased. CONCLUSION: Compared with traditional epidemiological investigation, this system can meet the challenges of accelerating virus variation and a large number of asymptomatic people, dynamically monitor and accurately evaluate the effectiveness of listed vaccines and maximise personal privacy without locking down the relevant area or city. This system established in this study could serve as a universal template for monitoring and evaluating the effectiveness of COVID-19 listed vaccines in cities around the world. If this system can be promoted globally, it will promote countries to strengthen unity and cooperation and enhance the global ability to respond to COVID-19.

Evaluating a Smart Healthcare System Design Through Participatory Approach.

Advances in computer communication technology have enabled the rapid growth of e-health services for delivering healthcare, such as facilitating online consent and data sharing between patients and health professionals. Developing a patient-centric healthcare system is challenging because by necessity, it should be secure, reliable, and resilient to cyber threats, whilst remaining user-friendly. Key to any development aiming for a refined proof-of-concept (PoC) system is the pursuit of comprehensive public system testing and evaluation. This paper focuses on the methodology and results obtained from the participatory approach adopted by the EU H2020 project Serums to evaluate and demonstrate the effectiveness of a smart healthcare system based on emergent technologies like blockchain, data lake, and multi-factor authentication. We discuss the challenges faced by remote PoC system evaluations with end-users as a consequence of the Covid-19 pandemic.