Health Code of Conduct: A Blockchain Model for Health Metrics and Sharing (Preprint)

2021 ◽  
Author(s):  
Reza Assadi ◽  
Ghazal GHasemi
Keyword(s):  
Data Storage ◽  
Healthcare Professionals ◽  
Data Transfer ◽  
Personal Information ◽  
Health Data ◽  
Security And Privacy ◽  
Global Network ◽  
Health Records ◽  
Related Data ◽  
Health Related

BACKGROUND Health is the most valuable property of all humans, and for long, scientists have had to cope with a tremendous amount of health-related data globally. Recording of health data has always faced challenges concerning privacy, accuracy, and interoperability. So in this study, we intended to summarize health records to a minimal and abridged string that can be easily reused and shared among health systems. For this purpose, we attempted to use various coding systems and combine them with disability codes defined in Global Burden of Disease (GBD) studies to reach a unique method for presenting health records. However, this type of data is prone to disclosing personal information and should be secured safely. Today, one of the safest methods for storing and sharing data is a blockchain network that makes data transactions safe and secure. OBJECTIVE Ultimately, we have envisaged a global network of interconnected health data communicating through approved protocols, namely the Internet of Health data (IoHd). METHODS In other words, we propose a decentralized, blockchain-based network where EHRs (Electronic Health Records) are stored in the form of a hashed health code, as explained earlier. The distributed system connects the health-related data among the trusted nodes, leading to the emergence of IoH. RESULTS This data would be hashed health codes stored on the blockchain, so all healthcare professionals and health-related corporations/institutions/companies may access this network using their login information. The network consists of three sub-networks, the private (for health wallets), the permissioned (for care wallets), and the or pseudonymous (for data wallets). CONCLUSIONS Considering blockchain technology's high security and privacy, it would be possible to safely and widely provide relevant health information for caregivers, healthcare professionals, research centers, big data studies, and artificial intelligence platforms to offer better access, data storage, care provision, data transfer, and surveillance.

scholarly journals Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies (Preprint)

2019 ◽  
Author(s):  
Xiaochen Zheng ◽  
Shengjing Sun ◽  
Raghava Rao Mukkamala ◽  
Ravi Vatrapu ◽  
Joaquín Ordieres-Meré
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Data Sharing ◽  
Data Access ◽  
Health Data ◽  
Security And Privacy ◽  
Access Management ◽  
Related Data ◽  
Distributed Ledger ◽  
Health Related

BACKGROUND Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

Ethics on Health Related Data Researches in Indonesia

2018 ◽  
Vol 24 (8) ◽  
pp. 6140-6142
Author(s):  
Risnandya Primanagara ◽  
Atik Sutisna
Keyword(s):  
Ethical Issue ◽  
Public Interest ◽  
Health Data ◽  
Health Records ◽  
Ethical Research ◽  
Related Data ◽  
The Past ◽  
Declaration Of Helsinki ◽  
Health Related

There have been mindset changes when considering research on health data. In the past, health records were not considered as an ethical issue according to the declaration of Helsinki in Ethic Human Researches. But nowadays, the latest guidelines on ethics on health related researches implemented to the health data. Indonesia was not fully apply this ethical research on health data, because of several reasons Multiple article described how to imply the latest ethical procedure on health related data, and reviewed to find the most beneficial for health data researchers in Indonesia. Several methods are found to be beneficial, but somehow were not feasible to Indonesian culture. There are options, such as: consent/assent, anonymization, or use under a public interest mandate. Discussions: There is still no best solution for Health related data ethic in Indonesia because somehow the procedures is unsatisfactory and problematic. Somehow, this ethical procedure is up to us to consider.

Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy
Keyword(s):  
Data Collection ◽  
User Experience ◽  
Health Data ◽  
Domain Model ◽  
Health And Wellness ◽  
Related Services ◽  
Related Data ◽  
Medical Apps ◽  
App Development ◽  
Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent

10.2196/16879 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16879 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

scholarly journals Use of Patient Generated Health Data collected from consumer wearable devices in clinical care: a systematic review and meta-analysis (Preprint)

2017 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Low Cost ◽  
Meta Analysis ◽  
Clinical Care ◽  
Self Care ◽  
Health Data ◽  
Related Data ◽  
Life Cycle Stages ◽  
Health Related

BACKGROUND PGHD (Patient Generated Health Data) are health-related data created or recorded by patients to inform their self-care. The availability of low-cost easy-to-use consumer wearable technologies has facilitated patients’ engagement in their self-care and increased production of PGHD but the uptake of this data in clinical environments has been slow. Studies showing opportunities and challenges affecting PGHD adoption and use in clinical care have not investigated these factors in detail during all stages of the PGHD life cycle. OBJECTIVE This study aims to provide deeper insight into various issues influencing the use of PGHD at each stage of its life cycle from the perspectives of key stakeholders including patients, healthcare professionals, and the health IT managers. METHODS A systematic review was undertaken on the scholarly and industry literature published from 2012 to 2017. Thematic analysis of content was applied to uncover perspectives of the key PGHD stakeholders on opportunities and challenges related to all life cycle stages of PGHD from consumer wearables. RESULTS Thirty-six papers were identified for detailed analysis. Challenges were discussed more frequently than opportunities. Most studies done in real-world settings were limited to the collection stage of PGHD life cycle that captured through consumer wearables. CONCLUSIONS There are many gaps in knowledge on opportunities and challenges affecting PGHD captured through consumer wearables in each stage of its life cycle. A conceptual framework involving all the stakeholders in overcoming various technical, clinical, cultural, and regulatory challenges affecting PGHD during its life cycle could help to advance the integration with and use of PGHD in clinical care.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent (Preprint)

2019 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

UNSTRUCTURED Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

scholarly journals A Blockchain-Based Spatial Data Trading Framework

2021 ◽  
Author(s):  
liu hui ◽  
WeiPeng Tai ◽  
Yaofei Wang ◽  
Wang Shenling
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Security And Privacy ◽  
Distributed Data ◽  
Distributed Data Storage ◽  
Related Data ◽  
Data Market ◽  
Spatial Big Data ◽  
Execution Environment ◽  
Trusted Execution Environment

Abstract With the increasing utilization of space related data, the demand for spatial big data sharing and trading is growing rapidly, which promotes the emergence of spatial data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. Blockchain is a decentralized distributed data storage technology, which uses the traceability and unforgeability to confirm and record each transaction, can solve the disadvantages of the centralized data market, however, it also introduces the problems of security and privacy. To address this issue, we propose a blockchain-based spatial data trading framework with Trusted Execution Environment to provide a trusted decentralized platform, including data storage, data query, data pricing and security computing. Based on this framework, a spatial data trading demonstration system was implemented and its feasibility and security were verified.

scholarly journals Geocomputation techniques for spatial analysis: are they relevant to health data?

2001 ◽  
Vol 17 (5) ◽  
pp. 1059-1071 ◽  
Author(s):  
Gilberto Câmara ◽  
Antônio Miguel Vieira Monteiro
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Spatial Data ◽  
Heuristic Search ◽  
Health Data ◽  
Frame Of Reference ◽  
Spatial Data Analysis ◽  
Related Data ◽  
Health Related ◽  
Computationally Intensive

Geocomputation is an emerging field of research that advocates the use of computationally intensive techniques such as neural networks, heuristic search, and cellular automata for spatial data analysis. Since increasing amounts of health-related data are collected within a geographical frame of reference, geocomputational methods show increasing potential for health data analysis. This paper presents a brief survey of the geocomputational field, including some typical applications and references for further reading.

scholarly journals E -Patient Deidentified Record Integration using Cloud Computing

2020 ◽  
Vol 9 (2) ◽  
pp. 157-160
Keyword(s):  
Public Health ◽  
Cloud Computing ◽  
Data Storage ◽  
Modern Architecture ◽  
Public Health Care ◽  
Health Record ◽  
Related Data ◽  
Patient Health ◽  
Health Related ◽  
Care Problems

With the development of science and technology, the design of modern architecture is becoming more and more attractive. Now a days the medical fields become more wide development in machinery the same way the data storage also developed higher . The main reason for proposing the paper is to store the patient data into the cloud. The patient can access the data from anywhere at any time. . The delivering of public health solutions can lead to increased efficiency in health related data. Many nations across the globe have launched aggressive stimulus programs aimed at solving public health care problems in efficient way .This paper proposed for maintain the patient health record in cloud computing.

Security and Privacy for Electronic Healthcare Records Using AI in Blockchain

2021 ◽  
pp. 90-102
Author(s):  
Ramani Selvanambi ◽  
Samarth Bhutani ◽  
Komal Veauli
Keyword(s):  
Data Privacy ◽  
Correct Diagnosis ◽  
Medical Data ◽  
Medical Advice ◽  
Data Availability ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Related Data ◽  
Healthcare Data ◽  
Health Related

In yesteryears, the healthcare data related to each patient was limited. It was stored and controlled by the hospital authorities and was seldom regulated. With the increase in awareness and technology, the amount of medical data per person has increased exponentially. All this data is essential for the correct diagnosis of the patient. The patients also want access to their data to seek medical advice from different doctors. This raises several challenges like security, privacy, data regulation, etc. As health-related data are privacy-sensitive, the increase in data stored increases the risk of data exposure. Data availability and privacy are essential in healthcare. The availability of correct information is critical for the treatment of the patient. Information not easily accessed by the patients also complicates seeking medical advice from different hospitals. However, if data is easily accessible to everyone, it makes privacy and security difficult. Blockchains to store and secure data will not only ensure data privacy but will also provide a common method of data regulation.

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