Health Data Marketplace - Challenges, Possibilities, and the Path Forward

Wearable devices continuously produce physiological data that can provide individuals critical information about their daily routine or fitness level in combination with their smartphones without requiring manual calculations or maintaining log-books. Real-time participant-generated data can enable large scale observational studies of health conditions, provide better insights into medical conditions of individuals and streamline clinical trial processes in medical research. However, privacy is a major concern for health data and there can be a lack of trust among different parties in the health data collection process. In addition, individuals often do not have sufficient control over the sharing of their data from the wearable devices. The lack of control, trust and privacy are key barriers to research participants being prepared to share their personal data from wearable devices. In this work, we propose a trust model to overcome the trust deficit among different parties. Then, we present a reference system architecture, rooted on the developed trust model, that provides incentive for individuals to securely share their health data through a data marketplace. By encouraging individuals to share their real-time health data, researchers will have access to large data sets at low cost.

BackgroundWearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers.ObjectiveWe aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace.MethodsWe used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system.ResultsWe designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study.ConclusionsA decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.

In this work, we demonstrate Sterling, a decentralized marketplace for private data. Sterling enables privacy-preserving distribution and use of data by using privacy-preserving smart contracts which run on a permissionless blockchain. The privacy-preserving smart contracts, written by data providers and consumers, immutably and irrevocably represent the interests of their creators. In particular, we provide a mechanism for data providers to control the use of their data through automatic verification of data consumer contracts, allowing providers to express constraints such as pricing and differential privacy. Through smart contracts and trusted execution environments, Sterling enables privacy-preserving analytics and machine learning over private data in an efficient manner. The resulting economy ensures that the interests of all parties are aligned. For the demonstration, we highlight the use of Sterling for training machine learning models on individuals' health data. In doing so, we showcase novel approaches to automatically appraising training data, verifying and enforcing model privacy properties, and efficiently training private models on the blockchain using trusted hardware.

During the COVID-19 pandemic, we witnessed how sharing of biological and biomedical data facilitated researchers, medical practitioners, and policymakers to tackle the pandemic on a global scale. Despite the growing use of electronic health records (EHRs) by medical practitioners and wearable digital gadgets by individuals, 80% of health and medical data remain unused, adding little value to the work of researchers and medical practitioners. Legislative constraints related to health data sharing, centralized siloed design of traditional data management systems, and most importantly, lack of incentivization models are thought to be the underpinning bottlenecks for sharing health data.With the advent of the General Data Protection Regulation (GDPR) of the European Union (EU) and the development of technologies like blockchain and distributed ledger technologies (DLTs), it is now possible to create a new paradigm of data sharing by changing the incentivization model from current authoritative or altruistic form to a shared economic model where financial incentivization will be the main driver for data sharing. This can be achieved by setting up a digital health data marketplace (DHDM).Here, we review papers that proposed technical models or implemented frameworks that use blockchain-like technologies for health data. We seek to understand and compare different technical challenges associated with implementing and optimizing the DHDM operation outlined in these articles. We also examine legal limitations in the context of the EU and other countries such as the USA to accommodate any compliance requirement for such a marketplace. Last but not least, we review papers that investigated the short-, medium-, and long-term socioeconomic impact of such a marketplace on a wide range of stakeholders.

BackgroundIntegrating pervasive computing with blockchain’s ability to store privacy-protected mobile health (mHealth) data while providing Health Insurance Portability and Accountability Act (HIPAA) compliance is a challenge. Patients use a multitude of devices, apps, and services to collect and store mHealth data. We present the design of an internet of things (IoT)–based configurable blockchain with different mHealth apps on iOS and Android, which collect the same user’s data. We discuss the advantages of using such a blockchain architecture and demonstrate 2 things: the ease with which users can retain full control of their pervasive mHealth data and the ease with which HIPAA compliance can be accomplished by providers who choose to access user data.ObjectiveThe purpose of this paper is to design, evaluate, and test IoT-based mHealth data using wearable devices and an efficient, configurable blockchain, which has been designed and implemented from the first principles to store such data. The purpose of this paper is also to demonstrate the privacy-preserving and HIPAA-compliant nature of pervasive computing-based personalized health care systems that provide users with total control of their own data.MethodsThis paper followed the methodical design science approach adapted in information systems, wherein we evaluated prior designs, proposed enhancements with a blockchain design pattern published by the same authors, and used the design to support IoT transactions. We prototyped both the blockchain and IoT-based mHealth apps in different devices and tested all use cases that formed the design goals for such a system. Specifically, we validated the design goals for our system using the HIPAA checklist for businesses and proved the compliance of our architecture for mHealth data on pervasive computing devices.ResultsBlockchain-based personalized health care systems provide several advantages over traditional systems. They provide and support extreme privacy protection, provide the ability to share personalized data and delete data upon request, and support the ability to analyze such data.ConclusionsWe conclude that blockchains, specifically the consensus, hasher, storer, miner architecture presented in this paper, with configurable modules and software as a service model, provide many advantages for patients using pervasive devices that store mHealth data on the blockchain. Among them is the ability to store, retrieve, and modify ones generated health care data with a single private key across devices. These data are transparent, stored perennially, and provide patients with privacy and pseudoanonymity, in addition to very strong encryption for data access. Firms and device manufacturers would benefit from such an approach wherein they relinquish user data control while giving users the ability to select and offer their own mHealth data on data marketplaces. We show that such an architecture complies with the stringent requirements of HIPAA for patient data access.

Introduction to the Special Issue: Digital Inequalities and Discrimination in the Big Data Era

Monetisation of digital health data through a GDPR-compliant and blockchain enabled digital health data marketplace: A proposal to enhance patient's engagement with health data repositories

Recent advances in wearable technology and the rapid adoption of wearable devices have led to a paradigm shift in the way health related data is generated, stored, and consumed. There are increased opportunities for setting up innovative wearable data markets where real-time, user-generated health data can be easily bought and sold. However, there are several challenges associated with safeguarding the consent, security, and privacy of all market participants, particularly the data owners. In this paper, we discuss some of these challenges and propose a conceptual framework and reference architecture for wearable data marketplaces that builds on top of blockchain technology, and facilitates and incentivises trustworthy data sharing. In particular, we propose an approach whereby user-defined data-sharing policies translate to executable smart contracts to facilitate the data exchange between the data subject and the data consumer. We illustrate how the proposed approach works through an use case scenario.

Precision Medicine in Inflammatory Bowel Diseases: Challenges and Considerations for the Path Forward

The rise in wearable technologies that track health data is evolving at an increasing pace linking personal devices such as mobile devices and smartwatches for remote healthcare. This transformation is creating new opportunities for designers to improve user experience with wearable devices for healthcare. An essential dimension of user experience is associated with optimising human-centred design principles to develop targeted interventions to enhance user interaction with a product. Those principles, including affordances in a product, system or service, resulting in improved engagement, making wearable devices more desirable. An exploratory study was conducted and demonstrated a constructive relationship between the product language and understanding the role of gender to facilitate user engagement in the context of smartwatches. The results of our study suggest that the inclusion of gender has substantial potential to optimise user engagement with wearable devices and concludes with a path forwards for design research to promote gender awareness in product language for health promotion.KeywordsGenderHuman-centred designProduct languageRemote healthcareSmartwatches

Data access committees (DACs) are critical players in the data sharing ecosystem. DACs review requests for access to data held in one or more repositories and where specific constraints determine how the data may be used and by whom. Our team surveyed DAC members affiliated with genomic data repositories worldwide to understand standard processes and procedures, operational metrics, bottlenecks, and efficiencies, as well as their perspectives on possible improvements to quality review. We found that DAC operations and systemic issues were common across repositories globally. In general, DAC members endeavored to achieve an appropriate balance of review efficiency, quality, and compliance. Our results suggest a similarly proportionate path forward that helps DACs pursue mutual improvements to efficiency and compliance without sacrificing review quality.

Big tech, big data and the new world of digital health

With the advent of the General Data Protection Regulation (GDPR) of the EU and the development of technologies like blockchain and distributed ledger technologies (DLT), it is now possible to create a new paradigm with a shared economic model where financial Incentivization will be the main driver for data sharing. This can be achieved by setting up a digital health data marketplace (DHDM). Speakers on s podcast authored a paper in BTHY journal entitled “From Sharing to Selling: Challenges and Opportunities of Establishing a Digital Health Data Marketplace Using Blockchain Technologies,” https://doi.org/10.30953/bhty.v5.184 Their ongoing work is discussed for the DHDM operation outlined along with current developments and future work. Questions addressed are below: What inspired the authors to explore the use of blockchain technology in healthcare data sharing and monetization? What are the key challenges and opportunities identified in establishing a digital health data marketplace using blockchain technologies? What are the potential socioeconomic impacts of a digital health data marketplace on patients, healthcare providers, and researchers, and how do you see the economics of health data developing into dynamic systems that will reflect in the processes of care delivery and management? What are the next steps for the research? Are there ongoing projects or collaborations the audience can expect to be excited about?

Personal genomic data and the related health data are valuable resources for both public-funded research, and for-profit entities in development of new drugs, therapies, and diagnostic tests. In order to access to large datasets, pharmaceutical and biotech companies have developed partnerships with public and private entities such as direct-to-consumer genetic testing companies to buy genomic and health related databases collected from research participants and customers. Although individuals mainly support data sharing for research purposes, the for-profit nature of such data sharing raises some questions regarding the rights of the data subjects and fairness in sharing benefits. In response, a new generation of sequencing and data sharing startups such as Nebula Genomics, LunaDNA, and EncrypGen are emerging which aim for leaving the data control in the hands of each individual customer. In particular, such so-called “DNA data marketplaces” allow individuals to receive various types of monetary incentives to sequence their genome and share it with interested commercial parties. This paper aims to provide an exploratory and critical review of the ethical challenges related to establishing such marketplaces for genomic and health data sharing. In the view of the growing number of startups developing such marketplaces, a thorough analysis of the relevant ethical concerns is timely and needed.

A Taxonomy Framework for Health Data Marketplace Business Model