Decentralized Telemedicine Framework for a Smart Healthcare Ecosystem

The healthcare sector is one of the most rapidly growing sectors globally. With the ever-growing technology, patient care, regulatory compliance, and digital transformation, there is an increased need for healthcare sectors to collaborate with all stakeholders – both within the healthcare ecosystem and in concurring industries. In recent times, telemedicine has proven to provide high quality, affordable, and predominantly adapted healthcare services. However, telemedicine suffers from several risks in implementation, such as data breach, restricted access across medical fraternity, incorrect diagnosis and prescription, fraud, and abuse. In this work, introduce blockchain-based framework that would unlock the future of the healthcare sector and improved services. Our proposed solution utilizing Ethereum smart contracts to develop a transparent, tamper-proof telemedicine healthcare framework, and ensure the integrity of sensitive patient data eliminating a central administrator. Moreover, the smart contract regulates the interaction between all the parties involved in the network and keeps the patient meticulously informed about the transactions in the network

The Use of Blockchain Technology in the Health Care Sector:Systematic Review

BACKGROUND: Blockchain technology is a part of Industry 4.0’s new Internet of Things applications: decentralized systems, distributed ledgers, and immutable and cryptographically secure technology. This technology entails a series of transaction lists with identical copies shared and retained by different groups or parties. One field where blockchain technology has tremendous potential is health care, due to the more patient-centric approach to the health care system as well as blockchain’s ability to connect disparate systems and increase the accuracy of electronic health records. OBJECTIVE: The aim of this study was to systematically review studies on the use of blockchain technology in health care and to analyze the characteristics of the studies that have implemented blockchain technology. METHODS: This study used a systematic review methodology to find literature related to the implementation aspect of blockchain technology in health care. Relevant papers were searched for using PubMed, SpringerLink, IEEE Xplore, Embase, Scopus, and EBSCOhost. A quality assessment of literature was performed on the 22 selected papers by assessing their trustworthiness and relevance. RESULTS: After full screening, 22 papers were included. A table of evidence was constructed, and the results of the selected papers were interpreted. The results of scoring for measuring the quality of the publications were obtained and interpreted. Out of 22 papers, a total of 3 (14%) high-quality papers, 9 (41%) moderate-quality papers, and 10 (45%) low-quality papers were identified. CONCLUSIONS: Blockchain technology was found to be useful in real health care environments, including for the management of electronic medical records, biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas. The main reasons for the implementation of blockchain technology in the health care sector were identified as data integrity, access control, data logging, data versioning, and nonrepudiation. The findings could help the scientific community to understand the implementation aspect of blockchain technology. The results from this study help in recognizing the accessibility and use of blockchain technology in the health care sector

Blockchain Is the Key To Facilitating the Healthcare System

The healthcare industry is one of the world’s largest industries, accounting for 17.7% of the United States Gross Domestic Product (GDP). Unsurprisingly, the health spending share is projected to rise from 17.7 percent, as reported in 2018, to 19.7 percent by 2028. The U.S. spends more on healthcare than any other country in the world. Being that we spend the most on healthcare, one would assume that we are the healthiest nation in the world. Unfortunately, despite the highest spending, “Americans experience worse health outcomes than [our] international peers.” It is time for the United States to take appropriate measures to reduce healthcare spending and improve the overall healthcare experience. Without an initiative for greater efficiency and innovation, the cost of the U.S. healthcare system will continue to rise. While there are several flaws within our current healthcare system, this paper will discuss how blockchain technology has the capability to revolutionize the healthcare industry. Accordingly, part II of this paper will provide an overview of blockchain; Part III will explain blockchain’s popularity; Part IV will discuss three critical areas plaguing our current healthcare system; Part V will analyze current healthcare services; Part VI will explain how healthcare will benefit from blockchain; and Part VII will analyze legal implications that may arise

Distributed Knowledge Modeling and Integration of Model-Based Beliefs into the Clinical Decision-Making Process

Das Treffen komplexer medizinischer Entscheidungen wird durch die stetig steigende Menge an zu berücksichtigenden Informationen zunehmend komplexer. Dieser Umstand ist vor allem auf die Verfügbarkeit von immer präziseren diagnostischen Methoden zur Charakterisierung der Patienten zurückzuführen (z.B. genetische oder molekulare Faktoren). Hiermit einher geht die Entwicklung neuartiger Behandlungsstrategien und Wirkstoffe sowie die damit verbundenen Evidenzen aus klinischen Studien und Leitlinien. Dieser Umstand stellt die behandelnden Ärztinnen und Ärzte vor neuartige Herausforderungen im Hinblick auf die Berücksichtigung aller relevanten Faktoren im Kontext der klinischen Entscheidungsfindung. Moderne IT-Systeme können einen wesentlichen Beitrag leisten, um die klinischen Experten weitreichend zu unterstützen. Diese Assistenz reicht dabei von Anwendungen zur Vorverarbeitung von Daten für eine Reduktion der damit verbundenen Komplexität bis hin zur systemgestützten Evaluation aller notwendigen Patientendaten für eine therapeutischen Entscheidungsunterstützung. Möglich werden diese Funktionen durch die formale Abbildung von medizinischem Fachwissen in Form einer komplexen Wissensbasis, welche die kognitiven Prozesse im Entscheidungsprozess adaptiert. Entsprechend werden an den Prozess der IT-konformen Wissensabbildung erhöhte Anforderungen bezüglich der Validität und Signifikanz der enthaltenen Informationen gestellt. In den ersten beiden Kapiteln dieser Arbeit wurden zunächst wichtige methodische Grundlagen im Kontext der strukturierten Abbildung von Wissen sowie dessen Nutzung für die klinische Entscheidungsunterstützung erläutert. Hierbei wurden die inhaltlichen Kernthemen weiterhin im Rahmen eines State of the Art mit bestehenden Ansätzen abgeglichen, um den neuartigen Charakter der vorgestellten Lösungen herauszustellen. Als innovativer Kern wurde zunächst die Konzeption und Umsetzung eines neuartigen Ansatzes zur Fusion von fragmentierten Wissensbausteinen auf der formalen Grundlage von Bayes-Netzen vorgestellt. Hierfür wurde eine neuartige Datenstruktur unter Verwendung des JSON Graph Formats erarbeitet. Durch die Entwicklung von qualifizierten Methoden zum Umgang mit den formalen Kriterien eines Bayes-Netz wurden weiterhin Lösungen aufgezeigt, welche einen automatischen Fusionsprozess durch einen eigens hierfür entwickelten Algorithmus ermöglichen. Eine prototypische und funktionale Plattform zur strukturierten und assistierten Integration von Wissen sowie zur Erzeugung valider Bayes-Netze als Resultat der Fusion wurde unter Verwendung eines Blockchain Datenspeichers implementiert und in einer Nutzerstudie gemäß ISONORM 9241/110-S evaluiert. Aufbauend auf dieser technologischen Plattform wurden im Anschluss zwei eigenständige Entscheidungsunterstützungssysteme vorgestellt, welche relevante Anwendungsfälle im Kontext der HNO-Onkologie adressieren. Dies ist zum einen ein System zur personalisierten Bewertung von klinischen Laborwerten im Kontext einer Radiochemotherapie und zum anderen ein in Form eines Dashboard implementiertes Systems zur effektiveren Informationskommunikation innerhalb des Tumor Board. Beide Konzepte wurden hierbei zunächst im Rahmen einer initialen Nutzerstudie auf Relevanz geprüft, um eine nutzerzentrische Umsetzung zu gewährleisten. Aufgrund des zentralen Fokus dieser Arbeit auf den Bereich der klinischen Entscheidungsunterstützung, werden an zahlreichen Stellen sowohl kritische als auch optimistische Aspekte der damit verbundenen praktischen Lösungen diskutiert.:1 Introduction 1.1 Motivation and Clinical Setting 1.2 Objectives 1.3 Thesis Outline 2 State of the Art 2.1 Medical Knowledge Modeling 2.2 Knowledge Fusion 2.3 Clinical Decision Support Systems 2.4 Clinical Information Access 3 Fundamentals 3.1 Evidence-Based Medicine 3.1.1 Literature-Based Evidence 3.1.2 Practice-Based Evidence 3.1.3 Patient-Directed Evidence 3.2 Knowledge Representation Formats 3.2.1 Logic-Based Representation 3.2.2 Procedural Representation 3.2.3 Network or Graph-Based Representation 3.3 Knowledge-Based Clinical Decision Support 3.4 Conditional Probability and Bayesian Networks 3.5 Clinical Reasoning 3.5.1 Deterministic Reasoning 3.5.2 Probabilistic Reasoning 3.6 Knowledge Fusion of Bayesian Networks 4 Block-Based Collaborative Knowledge Modeling 4.1 Data Model 4.1.1 Belief Structure 4.1.2 Conditional Probabilities 4.1.3 Metadata 4.2 Constraint-Based Automatic Knowledge Fusion 4.2.1 Fusion of the Bayesian Network Structures 4.2.2 Fusion of the Conditional Probability Tables 4.3 Blockchain-Based Belief Storage and Retrieval 4.3.1 Blockchain Characteristics 4.3.2 Relevance for Belief Management 5 Selected CDS Applications for Clinical Practice 5.1 Distributed Knowledge Modeling Platform 5.1.1 Requirement Analysis 5.1.2 System Architecture 5.1.3 System Evaluation 5.1.4 Limitations of the Proposed Solution 5.2 Personalization of Laboratory Findings 5.2.1 Requirement Analysis 5.2.2 System Architecture 5.2.3 Limitations of the Proposed Solution 5.3 Dashboard for Collaborative Decision-Making in the Tumor Board 5.3.1 Requirement Analysis 5.3.2 System Architecture 5.3.3 Limitations of the Proposed Solution 6 Discussion 6.1 Goal Achievements 6.2 Contributions and Conclusion 7 Bibliograph

Blockchain and IoMT against physical abuse: bullying in schools as a case study

© 2020 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/jsan10010001By law, schools are required to protect the well-being of students against problems such as on-campus bullying and physical abuse. In the UK, a report by the Office for Education (OfE) showed 17% of young people had been bullied during 2017–2018. This problem continues to prevail with consequences including depression, anxiety, suicidal thoughts, and eating disorders. Additionally, recent evidence suggests this type of victimisation could intensify existing health complications. This study investigates the opportunities provided by Internet of Medical Things (IoMT) data towards next-generation safeguarding. A new model is developed based on blockchain technology to enable real-time intervention triggered by IoMT data that can be used to detect stressful events, e.g., when bullying takes place. The model utilises private permissioned blockchain to manage IoMT data to achieve quicker and better decision-making while revolutionising aspects related to compliance, double-entry, confidentiality, and privacy. The feasibility of the model and the interaction between the sensors and the blockchain was simulated. To facilitate a close approximation of an actual IoMT environment, we clustered and decomposed existing medical sensors to their attributes, including their function, for a variety of scenarios. Then, we demonstrated the performance and capabilities of the emulator under different loads of sensor-generated data. We argue to the suitability of this emulator for schools and medical centres to conduct feasibility studies to address sensor data with disruptive data processing and management technologies.This research was funded by Innovate UK, grant number 133891.Published onlin