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2019 | OriginalPaper | Buchkapitel

8. Citizens’ Cooperation in the Reuse of Their Personal Data: The Case of Data Cooperatives in Healthcare

verfasst von : Joan Rodon Mòdol

Erschienen in: Collaboration in the Digital Age

Verlag: Springer International Publishing

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Abstract

The advent of big data analytics is creating new opportunities for the reuse of personal health data which are economically and socially desirable. Yet the way the reuse of personal data is organized impacts not only the types of benefits but also the way those benefits are distributed among subjects, holders and consumers of data and society in general. This paper analyses and compares three dominant approaches to organizing personal data reuse reported in the literature: healthcare system-centric, for-profit data-driven, and individual-centric. This analysis reveals a challenge of existing approaches: they do not guarantee that data subjects have a say over how their personal data is reused while at the same time ensuring that data consumers see value in reusing that data. This chapter suggest that an emergent organizational form that addresses this challenge is that of data cooperatives (DCs). DCs represent a new logic of cooperation of data subjects who voluntary pool their personal data and participate in the governance of its reuse. To illustrate the working of DCs, this chapter conducts a case study of a DC in healthcare.

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Vorheriges Kapitel A Coaching Style of Management and the Affective Structuration of Workplace Relations

Nächstes Kapitel Cooperatives in the Age of Sharing

Nur mit Berechtigung zugänglich

“An identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person” (Article 4 (1)).

For a taxonomy of data reuse see Custers and Ursic (2016).

The Council of the EU defines personalized medicine as “a medical model using characterization of individuals' phenotypes and genotypes (e.g. molecular profiling, medical imaging, lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention.” (EU 2017). Related terms that are also used: precision, stratified, or individualized medicine.

Günther et al. (2017) use the term interconnectivity to refer to “the possibility to synthesize data” from various data streams. Given the technological connotation of the term interconnectivity we choose the term linkage.

We distinguish two types of outcomes of PHD reuse: knowledge outcomes (e.g., findings from the research), and economic outcomes (e.g., generated by data consumers).

Those infrastructures adopt either a centralized (i.e., a single, shared repository for the data) or a federated (i.e., data is distributed, remaining at their source, and is accessed on demand when needed) model.

e.g., 23andMe, FitBit, Microsoft HealthVault, Google, Apple.

e.g., IQVia, Symphony Health.

Each data-driven organization extracts and controls PHD around the service it provides, but does not have access to other subsets of PHD tied to services provided by other data-driven organizations.

e.g., Digi.me (www.digi.me), Blue Button (bluebuttonconnector.healthit.gov), Datacoup (www.datacoup.com), CitizenMe (www.citizenme.com), Meeco (www.meeco.me), and Dime (www.dataisme.com).

Of course this paper does not presuppose that all for-profit digital platforms are extractive.

http://www.datenundgesundheit.ch/?lang=en.

Ernst Hafen is the co-founder and president of the Board of MiData.coop, co-founder of the Data and Health association.

Cooperatives are the largest private employer in Switzerland. Particularly, the two largest consumer co-operatives (Migros and Coop) are responsible for 8% of Switzerland’s GDP (ICA, 2017).

Digital self-determination broadly refers to the ability to shape who we are and protect our own identity (by for instance, controlling our personal data) in the digital world.

The prototype source course is available at https://github.com/amarfurt/hdc, https://github.com/i4mi/midata.js, https://github.com/SebastianHaag/midata.coop.

In 2017 MiData was working with partners in Germany (Charité and Berlin Institute of Health), the Netherlands (Medical Delta, TNO) and in collaboration with INDEPTH-Network.org in Ethiopia and Vietnam.

User must comply with the Swiss Data Protection Act with regard to data from other users.

Newsletter #5, February 2 2017.

FHIR API stands for Fast Healthcare Interoperability Resources API and is an API for exchanging electronic Health records. https://test.midata.coop/fhir.

This includes many data types—for instance, the data types used in the use cases are: steps, weight, well-being, diary notes, cognition tests, hand-eye coordination, blood lab results, glycemic index, patient/physician communication, anxiety level, craving scale, therapies, lungs functions, glucose level, stress level, electro dermal activity, citizen self-reported drug consumption, genome genotyping, sensory perception, food intake, nutrition, metabolomics, sleep behavior and electrophysiology, and patient feedback.

Health Level Seven International.

Anonymized form refers to data that cannot without disproportionate effort, be traced to specific person [WSrc11].

Ulrich Genick is responsible for citizen science and a founding member of MiData.

We do not foresee that DCs will substitute existing PHD reuse approaches, particularly the healthcare system-centric and for-profit data-driven approaches; rather, DCs will co-exist with them.

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Titel: Citizens’ Cooperation in the Reuse of Their Personal Data: The Case of Data Cooperatives in Healthcare
verfasst von: Joan Rodon Mòdol
Verlag: Springer International Publishing
Buch: Collaboration in the Digital Age
Print ISBN: 978-3-319-94486-9

Electronic ISBN: 978-3-319-94487-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-94487-6_8

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