Sharing health-related data: a privacy test?

Dyke, Stephanie O.M.; Dove, Edward S.; Knoppers, Bartha Maria

doi:10.1038/npjgenmed.2016.24

Cited by 38 publications

(30 citation statements)

References 26 publications

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“…While in many situations this distinction will be sufficient, the MME Consent Policy acknowledges that it may not always be: “ Clinical judgment should be used to assess the potential for re‐identification and possible harm depending on the level of phenotypic detail provided ”. Ongoing research into data sensitivity aims to provide greater clarity in this area and to further develop useful guidance such as the Data Sharing Privacy Test to distinguish levels of data sensitivity (Dyke et al., ).…”

Section: Discussionmentioning

confidence: 99%

“…The second level of matchmaking involves sharing more detailed genotypic and/or phenotypic data that may be unique or sensitive. The risk of re‐identification and potential harm can be lowered through pooling sources of data and by removing some phenotype information based on its sensitivity (Dyke et al., ; The Beacon Project. Beacon:A protocol for federated genomic data sharing, submitted).…”

Section: Mme “Tiered” Consent Policymentioning

confidence: 99%

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“Matching” consent to purpose: The example of the Matchmaker Exchange

et al. 2017

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The Matchmaker Exchange (MME) connects rare disease clinicians and researchers to facilitate the sharing of data from undiagnosed patients for the purpose of novel gene discovery. Such sharing raises the odds that two or more similar patients with candidate genes in common may be found, thereby allowing their condition to be more readily studied and understood. Consent considerations for data sharing in MME included both the ethical and legal differences between clinical and research settings and the level of privacy risk involved in sharing varying amounts of rare disease patient data to enable patient matches. In this Commentary, we discuss these consent considerations and the resulting MME Consent Policy as they may be relevant to other international data sharing initiatives.

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Section: Discussionmentioning

confidence: 99%

Section: Mme “Tiered” Consent Policymentioning

confidence: 99%

“Matching” consent to purpose: The example of the Matchmaker Exchange

et al. 2017

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“…Their 'capacity' to contribute to the reidentification of subjects increases their potential sensitivity. Although there has been a great deal of discussion over which data are in the sensitive category, there is little examination of the different levels of sensitivity within the personal data category [9]. The two main ethical and legal issues in data protectionautonomy and privacy are interrelated concepts as encapsulated in the concept of informational self-determination.…”

Section: Other Definitions Includementioning

confidence: 99%

What Are Data? A Categorization of the Data Sensitivity Spectrum

Rumbold

Pierscionek

2018

Big Data Research

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The definition of data might at first glance seem prosaic, but formulating a definitive and useful definition is surprisingly difficult. This question is important because of the protection given to data in law and ethics. Healthcare data are universally considered sensitive (and confidential), so it might seem that the categorisation of less sensitive data is relatively unimportant for medical data research. This paper will explore the arguments that this is not necessarily the case and the relevance of recognizing this. The categorization of data and information requires re-evaluation in the age of Big Data in order to ensure that the appropriate protections are given to different types of data. The aggregation of large amounts of data requires an assessment of the harms and benefits that pertain to large datasets linked together, rather than simply assessing each datum or dataset in isolation. Big Data produce new data via inferences, and this must be recognized in ethical assessments. We propose a schema for a granular assessment of data categories. The use of schemata such as this will assist decision-making by providing research ethics committees and information governance bodies with guidance about the relative sensitivities of data. This will ensure that appropriate and proportionate safeguards are provided for data research subjects and reduce inconsistency in decision making.

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“…Thus there is a balancing act for any project that is best achieved through a human rights approach. There are different levels of sensitivity even within the category of healthcare data [67]. Differential privacy is particularly helpful in this regard [68].…”

Section: Main Textmentioning

confidence: 99%

A critique of the regulation of data science in healthcare research in the European Union

Rumbold

Pierscionek

2017

BMC Med Ethics

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The EU offers a suitable milieu for the comparison and harmonisation of healthcare across different languages, cultures, and jurisdictions (albeit with a supranational legal framework), which could provide improvements in healthcare standards across the bloc. There are specific ethico-legal issues with the use of data in healthcare research that mandate a different approach from other forms of research. The use of healthcare data over a long period of time is similar to the use of tissue in biobanks. There is a low risk to subjects but it is impossible to gain specific informed consent given the future possibilities for research. Large amounts of data on a subject present a finite risk of re-identification. Consequently, there is a balancing act between this risk and retaining sufficient utility of the data. Anonymising methods need to take into account the circumstances of data sharing to enable an appropriate balance in all cases. There are ethical and policy advantages to exceeding the legal requirements and thereby securing the social licence for research. This process would require the examination and comparison of data protection laws across the trading bloc to produce an ethico-legal framework compatible with the requirements of all member states. Seven EU jurisdictions are given consideration in this critique.

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Sharing health-related data: a privacy test?

Cited by 38 publications

References 26 publications

“Matching” consent to purpose: The example of the Matchmaker Exchange

“Matching” consent to purpose: The example of the Matchmaker Exchange

What Are Data? A Categorization of the Data Sensitivity Spectrum

A critique of the regulation of data science in healthcare research in the European Union

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