A 21st Century Embarrassment of Riches: The Balance Between Health Data Access, Usage, and Sharing

Objectives: To provide an introduction to the 2018 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial provides an overview and introduction to the 2018 IMIA Yearbook which special topic is: “Between access and privacy: Challenges in sharing health data”. The special topic editors and section are discussed, and the new section of the 2018 Yearbook, Cancer Informatics, is introduced. Changes in the Yearbook editorial team are also described. Results: With the exponential burgeoning of health-related data, and attendant demands for sharing and using these data, the special topic for 2018 is noteworthy for its timeliness. Data sharing brings responsibility for preservation of data privacy, and for this, patient perspectives are of paramount importance in understanding how patients view their health data and how their privacy should be protected. Conclusion: With the increase in availability of health-related data from many different sources and contexts, there is an urgent need for informaticians to become aware of their role in maintaining the balance between data sharing and privacy.

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

10.2196/preprints.13583 ◽

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.

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Health data sharing and use explored

Impact ◽

10.21820/23987073.2021.8.4 ◽

2021 ◽

Vol 2021 (8) ◽

pp. 4-5

Author(s):

Lucy Annette

Keyword(s):

Data Sharing ◽

Data Protection ◽

Positive Impact ◽

Digital Health ◽

Health Data ◽

Full Potential ◽

Health And Wellness ◽

Related Data ◽

Health Related ◽

The Right

Three expert roundtables took place as part of DigitalHealthEurope (DHE), with discussions surrounding health data sharing and use. In the first roundtable, the implementation of GDPR was explored and the experts delved into possible remaining challenges associated with understanding the way in which health related data may be used. Legal issues and the importance of data protection and citizen protection were discussed, as was the need for more human resources regarding data protection, which could be rectified by the provision of education in this area. The introduction of a new EU body responsible for data legislative needs was an idea that was put forward. Next, the law as an enabler of data use was discussed, along with the protection of citizens and data. It was highlighted that in order for the full potential of digital health to be realised, data literacy and skills are paramount. The experts also discussed how data can be used to protect citizens, without compromising a right to privacy, as well as the importance of generating the right data to ensure that it can be used to protect citizens' health and wellness. A further topic of discussion was how the development of a range of skills among data stakeholders would lead to the better use of data and that this would have a positive impact on health and wellness.

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

10.2196/preprints.30468 ◽

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.

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

Journal of Medical Internet Research ◽

10.2196/16879 ◽

2020 ◽

Vol 22 (5) ◽

pp. e16879 ◽

Cited By ~ 1

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.

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The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677917 ◽

2019 ◽

Vol 28 (01) ◽

pp. 195-202 ◽

Cited By ~ 6

Author(s):

Marc Cuggia ◽

Stéphanie Combes

Keyword(s):

Data Sharing ◽

Medical Informatics ◽

Data Privacy ◽

Data Use ◽

Health Data ◽

The European Union ◽

Global Approach ◽

Data Governance ◽

Academic Hospitals ◽

Access Data

Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

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Automating Privacy Compliance Using Policy Integrated Blockchain

Cryptography ◽

10.3390/cryptography3010007 ◽

2019 ◽

Vol 3 (1) ◽

pp. 7 ◽

Cited By ~ 1

Author(s):

Karuna Pande Joshi ◽

Agniva Banerjee

Keyword(s):

Data Sharing ◽

Data Privacy ◽

Personal Data ◽

Data Access ◽

Information Management System ◽

Knowledge Graph ◽

Efficient System ◽

Essential Requirement ◽

Blockchain Technology ◽

Rich Data

An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.

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A UKSeRP for SAIL: striking a balance

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.254 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Kerina Jones ◽

David Ford ◽

Caroline Brooks

Keyword(s):

Data Linkage ◽

Data Access ◽

Remote Access ◽

Data Types ◽

Study Approach ◽

Related Data ◽

Access System ◽

Data User ◽

Health Related ◽

The Uk

ABSTRACT ObjectivesWhilst the current expansion of health-related big data and data linkage research are exciting developments with great potential, they bring a major challenge. This is how to strike an appropriate balance between making the data accessible for beneficial uses, whilst respecting the rights of individuals, the duty of confidentiality and protecting the privacy of person-level data, without undue burden to research. ApproachUsing a case study approach, we describe how the UK Secure Research Platform (UKSeRP) for the Secure Anonymised Information Linkage (SAIL) databank addresses this challenge. We outline the principles, features and operating model of the SAIL UKSeRP, and how we are addressing the challenges of making health-related data safely accessible to increasing numbers of research users within a secure environment. ResultsThe SAIL UKSeRP has four basic principles to ensure that it is able to meet the needs of the growing data user community, and these are to: A) operate a remote access system that provides secure data access to approved data users; B) host an environment that provides a powerful platform for data analysis activities; (C) have a robust mechanism for the safe transfer of approved files in and out of the system; and (D) ensure that the system is efficient and scalable to accommodate a growing data user base. Subject to independent Information Governance approval and within a robust, proportionate Governance framework, the SAIL UKSeRP provides data users with a familiar Windows interface and their usual toolsets to access anonymously-linked datasets for research and evaluation. ConclusionThe SAIL UKSeRP represents a powerful analytical environment within a privacy-protecting safe haven and secure remote access system which has been designed to be scalable and adaptable to meet the needs of the rapidly growing data linkage community. Further challenges lie ahead as the landscape develops and emerging data types become more available. UKSeRP technology is available and customisable for other use cases within the UK and international jurisdictions, to operate within their respective governance frameworks.

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Decentralized Personal Health Records with Interacting Physician Apps and Intelligent Agents on Blockchain and Smart Contract Technologies (Preprint)

10.2196/preprints.26230 ◽

2020 ◽

Author(s):

Hyeong-Joon Kim ◽

Hye Hyun Kim ◽

Hosuk Ku ◽

Kyung Don Yoo ◽

Suehyun Lee ◽

...

Keyword(s):

Data Sharing ◽

Intelligent Agents ◽

Data Privacy ◽

Data Integrity ◽

Health Data ◽

Smart Devices ◽

Data Element ◽

Element Level ◽

Smart Contract ◽

Transaction Manager

BACKGROUND The Health Avatar Platform (HAP) provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (IoA3) for data privacy and participatory medicine. However, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. OBJECTIVE The introduction of blockchain and smart contract (SC) technologies to the HAP legacy platform with a clinical metadata registry (MDR) remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. METHODS HAP decentralizes patient data in appropriate locations with no central storage, i.e., on patients’ smartphones and on physicians’ smart devices. We implemented an Ethereum-based hash chain for all transactions and SC-based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into three SCs, in this case a health data transaction manager, a transaction status manager, and an API transaction manager. The actual decentralized health data are managed in off-chain manner on their appropriate smart devices and authenticated by hashed metadata on-chain. RESULTS Metadata of each data transaction are captured in a HAP blockchain node by the SCs. We provide workflow diagrams each of the three use cases of data push (from a physician app or an intelligent Agents to a patient Avatar), data pull (requested to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash chained metadata support data element-level verification of the data integrity in subsequent transactions. SCs can incentivize transactions for data sharing and intelligent digital healthcare services. CONCLUSIONS HAP and IoA3 provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions by SCs.

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

10.2196/preprints.8777 ◽

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.

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

10.2196/preprints.16879 ◽

2019 ◽

Cited By ~ 1

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.

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