Blockchain Papers

Francesco Sanmarchi, F Toscano, M Fattorini, Andrea Bucci · 5 authors

Modern healthcare management and clinical practice strongly rely on data and scientific evidence. Digital technologies, tools, and services are core components of Healthcare Management and scientific Research (HMR). Data interoperability, security, privacy, and ease of sharing represent fundamental conditions for guaranteeing quality HMR. Current data management solutions in HMR are mainly built on two technological infrastructures: cloud-based (CB) or distributed ledger systems (DLTs). DLTs offer alternative and reliable alternatives for the management and sharing of data in HMR. Their use can help increase confidence and trust in the integrity of data and the resulting evidence. 
\nThe aim of this paper is to shed light on CB and DLT solutions, emphasizing the potential role of innovative digital solutions based on DLTs in creating a data-driven transformation of HMR, and to describe relevant examples and practical uses of DLT-based solutions for patients, healthcare management, and research activities. 
\nDLTs in particular can be increasingly useful for patients to truly have control over their health, for healthcare policymakers to increase the quality of organizational processes, and for research funders, editors and publishers to increase the return on investment, and the reuse and reproducibility of research. 
\nIn conclusion, harnessing the potential of digital technologies is essential to transform healthcare management and research, by enhancing data quality, reliability, and trust.

V Valko, Sergii Stirenko, Ihor Babarykin, Yuri Gordienko

In this article we provide a general framework using Ethereum smart contracts to track back the provenance and evolution of deep learning content (DLC) to its original source even if the DLC was edited (e.g. DL models were retrained or/and datasets were updated) by anonymous authors. The main principle behind the solution is that if the DLC can be credibly traced to a trusted or reputable source, the DLC can then be real and authentic. The solution is proposed in the healthcare context and for medical DLC, especially for federated machine learning, but it can be applied to any other form of DLC.

Leila Fetjah, Kebira Azbeg, Ouail Ouchetto, Said Jai Andaloussi

With the rapid development in smart medical devices, Internet of things has a large applicability in healthcare sector. The current system is based on a centralized communication with cloud servers. However, this architecture increases security and privacy risks. This paper describes an architecture of a smart healthcare system for remote patient monitoring. To ensure security and privacy, the architecture uses the Blockchain technology. For data analysis, smart contracts and artificial intelligence are used. The architecture is divided into three layers: smart medical devices layer, fog layer and cloud layer. To validate the proposed approach, a scenario based on diabetes management system is described. The architecture is applied to provide remote diabetic patients monitoring. The system could suggest treatments, generate proactive predictions and predict future complications as well as alerting physicians in case of emergency.

European Society of Radiology (ESR), Elmar Kotter, Luis Martí‐Bonmatí, Adrian P. Brady · 5 authors

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different "authors" including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Faheem Ahmad Reegu, Salwani Mohd Daud, Zaid Hakami, Kaiser Kariem Reegu · 5 authors

The digital archive of the patient's personal health records is the Electronic Health Record (EHR) that has various advantages. For finding the best solution to resolve the associated issues, a detailed study is required to utilize modern technologies and standards so that these issues and errors can be minimized. There are several issues associated with implementing the EHR, including data management, privacy, and patient data security. This research aims to examine the use of the Blockchain in EHR frameworks as per the national and international standards of EHR to reduce the associated issues. A blockchain-based framework can be successful in solving the current challenges in EHR. It allows storing, sharing, managing, controlling, and maintaining patient information between healthcare providers. The study illustrated the related difficulties of integrating EHR and proposed Blockchain as a solution to help manage the records and preserve privacy, confidentiality, usability and protection of patient-related details. This study helps to understand the current challenges better and help in the proper implementation of Blockchain technology in EHR.

Jan Witowski, Jongmum Choi, Soomin Jeon, Doyun Kim · 9 authors

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.

Daniel Cabrera, Christopher P Nickson, Damian Roland, Elissa Hall · 5 authors

UNSTRUCTURED Current health professions education (HPE) institutions are based on an assembly-line hierarchical structure. The last decade has witnessed the advent of sophisticated networks allowing the exchange of information and educational assets. Blockchain provides an ideal data management framework that can support high-order applications such as learning systems and credentialing in an open and a distributed fashion. These system management characteristics enable the creation of a distributed autonomous organization of learning (DAOL). This new type of organization allows for the creation of decentralized adaptive competency curricula, simplification of credentialing and certification, leveling of information asymmetry among educational market stakeholders, assuring alignment with societal priorities, and supporting equity and transparency.

Dominique Vervoort, Camila R. Guetter, Alexander W. Peters

Health disparities remain vast around the world and are perpetuated by error-prone information technology systems, administrative inefficiencies and wasteful global health spending. Blockchain technology is a novel, distributed peer-to-peer ledger technology that uses unique, immutable and time-stamped blocks of records or sets of data that are linked as chains through cryptography to more reliably and transparently store and transfer data. Various industries have successfully leveraged blockchain technology to disintermediate and reduce costs, but its use in healthcare and global health has remained limited. In this narrative review, we describe blockchain technology and elaborate on the experiences and opportunities for leveraging blockchain within global health in terms of cryptocurrencies and health financing, supply chain management, health records, identification and verification, telehealth and misinformation. We conclude each section with an analysis of the restrictions imposed by the COVID-19 pandemic to highlight blockchain’s unique opportunities for improving healthcare services and access to care during future pandemics or natural disasters.

Tim Joda, Nikolaos Pandis

No abstract is available for this record.

Anjum Khurshid, Cole Holan, Cody Cowley, Jeremiah Alexander · 9 authors

OBJECTIVE: Healthcare systems suffer from a lack of interoperability that creates "data silos," causing patient linkage and data sharing problems. Blockchain technology's unique architecture provides individuals greater control over their information and may help address some of the problems related to health data. A multidisciplinary team designed and tested a blockchain application, MediLinker, as a patient-centric identity management system. METHODS: The study used simulated data of "avatars" representing different types of patients. Thirty study participants were enrolled to visit simulated clinics, and perform various activities using MediLinker. Evaluation was based on Bouras' criteria for patient-centric identity management and on the number of errors in entry and sharing of data by participants. RESULTS: Twenty-nine of the 30 participants completed all study activities. MediLinker fulfilled all of Bouras' criteria except for one which was not testable. A majority of data errors were due to user error, such as wrong formatting and misspellings. Generally, the number of errors decreased with time. Due to COVID-19, sprint 2 was completed using "virtual" clinic visits. The number of user errors were less in virtual visits than in personal visits. DISCUSSION: The evaluation of MediLinker provides some evidence of the potential of a patient-centric identity management system using blockchain technology. The results showed a working system where patients have greater control over their information and can also easily provide consent for use of their data. CONCLUSION: Blockchain applications for identity management hold great promise for use in healthcare but further research is needed before real-world adoption.

Ya Gong, Shi Qiuxia, Deling Duan, Zhiqiang Liu

In recent years, the IVD industry has developed rapidly based on the increasing market demand, and plays an important role in disease prevention, clinical diagnosis, health monitoring and guiding treatment. Therefore, followed quality and safety issues are highly concerned. The unique advantages of blockchain technology, decentralization, distrust and non-tampering, can write into trusted node data in every link covering production, circulation and usage of IVD reagents, and establish a distributed ledger with full backup, which makes the anti-conterfeiting and traceability for IVD reagents possible. We discuss whole process intelligent tracing system for IVD reagents based on blockchain technology. Through the strong mechanism of pre-supervision and post-punishment, the source of reagents can be traced, quality and responsibility can be investigated, and the medical inspection quality and diagnostic safety can be guarded.

Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.

Xiangfeng Zhang, Yanmei Wang

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Lamine Amour, Matthieu Quiniou, Sara Tucci-Piergiovanni, Hichem Bourak · 5 authors

No abstract is available for this record.

S. Vijayalakshmi, SAVITA SAVITA, S. P. Gayathri, S. Janarthanan

No abstract is available for this record.

Naeem Iqbal, Faisal Jamil, Shabir Ahmad, Do‐Hyeun Kim

The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries. However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few. Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies. Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system. Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc. This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques. The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules. First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric. Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework. The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms. The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients. Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency. Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model. The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.

P. G. Shynu, Varun G. Menon, R. Lakshmana Kumar, Seifedine Kadry · 5 authors

Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT’s open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. Purity and NMI metrics are used to analyze the performance of the rule based clustering and the accuracy is used for prediction performance. The experimental results show that the proposed work efficiently predicts the disease. The proposed work reaches more than 81% of prediction accuracy compared to the other neural network algorithms.

Wendy Charles

No abstract is available for this record.

Karamo Kanagi, Cheng‐Yuan Ku, Li-Kai Lin, Wen-Huai Hsieh

BACKGROUND: While electronic health records have been collected for many years in Taiwan, their interoperability across different health care providers has not been entirely achieved yet. The exchange of clinical data is still inefficient and time consuming. OBJECTIVES: This study proposes an efficient patient-centric framework based on the blockchain technology that makes clinical data accessible to patients and enable transparent, traceable, secure, and effective data sharing between physicians and other health care providers. METHODS: Health care experts were interviewed for the study, and medical data were collected in collaboration with Ministry of Health and Welfare (MOHW) Chang-Hua hospital. The proposed framework was designed based on the detailed analysis of this information. The framework includes smart contracts in an Ethereum-based permissioned blockchain to secure and facilitate clinical data exchange among different parties such as hospitals, clinics, patients, and other stakeholders. In addition, the framework employs the Logical Observation Identifiers Names and Codes (LOINC) standard to ensure the interoperability and reuse of clinical data. RESULTS: The prototype of the proposed framework was deployed in Chang-Hua hospital to demonstrate the sharing of health examination reports with many other clinics in suburban areas. The framework was found to reduce the average access time to patient health reports from the existing next-day service to a few seconds. CONCLUSION: The proposed framework can be adopted to achieve health record sharing among health care providers with higher efficiency and protected privacy compared to the system currently used in Taiwan based on the client-server architecture.

Jonathan Passerat‐Palmbach, Tyler Farnan, Mike McCoy, Justin D. Harris · 7 authors

Machine learning and blockchain technology have been explored for potential applications in medicine with only modest success to date. Focus has shifted to exploring the intersection of these technologies along with other privacy preserving encryption techniques for better utility. This combination applied to federated learning, which allows remote execution of function and analysis without the need to move highly regulated personal health information, seems to be the key to successful applications of these technologies to rapidly advance evidence-based medicine. We give a brief history of these technologies in medicine, outlining some of the challenges with successful use. We then explore a more detailed combination of usage with an emphasis on decentralizing or federating the learning process along with auditability and incentivization blockchain can allow in the machine learning process. Based on the cost-benefit analysis of previous efforts, we provide the framework for an advanced blockchain-orchestrated machine learning system for privacy preserving federated learning in medicine and a new utility in health. Six critical elements for this approach in the future will be:(a) Data and analytic processes discoverable on secure public blockchain while retaining privacy of the data and analytic processes(b) Value fabricated by generating data/compute matches that were previously illegal, unethical and infeasible(c) Compute guarantees provided by federated learning and advanced cryptography(d) Privacy guarantees provided by software (e.g., Homomorphic Encryption, Secure Multi-Party Computation, ...) and hardware (e.g., Intel SGX and AMD SEV-SNP) cryptography(e) Data quality incentivized via tokenized reputation-based rewards(f) Discarding of poor data accomplished via model poisoning attack prevention techniques.

Pushpa Singh, Narendra Singh, Ganesh Chandra Deka

Presently, machine learning (ML) techniques have gained considerable attention, with growing interest in various areas and applications. Healthcare, agriculture, and bioinformatics are the most identified areas to study with the help of ML. This chapter introduces about the basic principle of ML such as data, model, basic mathematical details of ML, and types of learning. The important aspect of ML is “how to teach a machine.” This chapter focuses on the types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Some commonly used ML algorithms such as decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes, k-mean, q-learning, etc. are briefly discussed for understanding. Finally, the author offers the application of ML with blockchain that is reforming the traditional healthcare and agricultural sector to a more reliable means.

Xiangfeng Zhang, Yanmei Wang

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

Nogol Memari, Saranaz Abdollahi, Mahdi Maghrouni Ganzagh, Mehrdad Moghbel

Diabetes is a serious medical condition and regular screening for diabetes is of great importance as treatment options are most effective in the early stages of diabetes. Digital imaging of retina is considered as a low-cost method for screening and could be used in conjunction with computer-based image processing techniques to automatically detect early signs of diabetes utilizing diabetes-related pathologies visible in retinal fundus images. This research proposes a novel computer-assisted diagnosis (CAD) system for assisting with the screening of the population as up to 50% of the affected population are not aware of having diabetes. Moreover, these screenings are often carried out by an optometrist who receives some training with the patients being referred to an ophthalmologist if they show symptoms. Having a computer-assisted diagnosis system assisting the optometrist during the screening can greatly increase the detection rate for patients with diabetes by providing a second opinion and highlighting any suspicious pathologies. For achieving the highest detection rate possible, a hybrid machine learning approach is proposed in this research by combining Deep Learning with the AdaBoost classifier. The proposed computer-assisted diagnosis system starts with the segmentation of the blood vessels. Then, microaneurysms and exudates are segmentation from the image. Statistical and regional features are then extracted utilizing first, second, and higher-order image features. A Deep Learning framework will be utilized for extracting additional statistical image descriptors as a Deep Learning has superior contextual analysis capabilities compared to other machine learning techniques. Finally, the most informative features are selected by a minimal-redundancy maximal-relevance feature selection approach with an AdaBoost classifier analyzing all the features and informing the operator regarding the patient’s condition. Ethereum Swarm blockchain-based decentralized cloud file storage provides the proposed CAD users with a secure storage olution to access the patient information and related images. The sensitivity, specificity, and accuracy of the classification will be measured under clinical conditions. Healthcare, government, and public users would receive the most benefit from this project.

Raja Wasim Ahmad, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob · 6 authors

<div><b>Objectives: </b>Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID‐19. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Methods: </b>The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials.</div><div><b>Results: </b>Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis.</div><div><b>Conclusions: </b>Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.</div><div> </div><div><br></div>