Datafication and accountability in public health: Introduction to a special issue

Hoeyer, Klaus; Bauer, Susanne; Pickersgill, Martyn

doi:10.1177/0306312719860202

Cited by 49 publications

(40 citation statements)

References 89 publications

(92 reference statements)

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“…The Danish story of mobilizing massive investments in a new research area could be told as a classic narrative of the formation and facilitation of political alliances (Czarniawska, 2005; Vincent, 2014). To raise funds you need to raise expectations (Brown and Michael, 2003), and it is hardly surprising that the boundaries between scientific evidence and political processes become blurry in the articulation of such arguments (Hoeyer et al, 2019; Weiss, 1986). In relation to personalized medicine, I nevertheless find it striking how groups of researchers, clinicians and policymakers coalesce in their pursuit of future sources of evidence, while demonstrating tenuous relationships with existing evidence.…”

Section: Resultsmentioning

confidence: 99%

Data as promise: Reconfiguring Danish public health through personalized medicine

Hoeyer

2019

Soc Stud Sci

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‘Personalized medicine’ might sound like the very antithesis of population science and public health, with the individual taking the place of the population. However, in practice, personalized medicine generates heavy investments in the population sciences – particularly in data-sourcing initiatives. Intensified data sourcing implies new roles and responsibilities for patients and health professionals, who become responsible not only for data contributions, but also for responding to new uses of data in personalized prevention, drawing upon detailed mapping of risk distribution in the population. Although this population-based ‘personalization’ of prevention and treatment is said to be about making the health services ‘data-driven’, the policies and plans themselves use existing data and evidence in a very selective manner. It is as if data-driven decision-making is a promise for an unspecified future, not a demand on its planning in the present. I therefore suggest interrogating how ‘promissory data’ interact with ideas about accountability in public health policies, and also with the data initiatives that the promises bring about. Intensified data collection might not just be interesting for what it allows authorities to do and know, but also for how its promises of future evidence can be used to postpone action and sidestep uncomfortable knowledge in the present.

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Section: Resultsmentioning

confidence: 99%

Data as promise: Reconfiguring Danish public health through personalized medicine

Hoeyer

2019

Soc Stud Sci

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“…Recombining and adjusting these figures, they decided to add four simple coefficients to the original risk-predicting algorithm from Framingham. It is in this ongoing calibration of the risk prediction that versions of accountability are negotiated and become visible and that the ‘accountability assemblage’ of risk prediction can be opened up for debate (Hoeyer et al, 2019; Hogle, 2019).…”

Section: ‘Adapting Framingham’: the Arriba Score In The Makingmentioning

confidence: 99%

Following the algorithm: How epidemiological risk-scores do accountability

Amelang

Bauer

2019

Soc Stud Sci

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Epidemiological risk scores are calculative devices that mediate and enact versions of accountability in public health and preventive medicine. This article focuses on practices of accountability by following a cardiovascular risk score widely used in medical counselling in Germany. We follow the risk score in the making, in action, and in circulation to explore how the score performs in doctor-patient relations, how it recombines epidemiological results, and how it shapes knowledge production and healthcare provision. In this way, we follow the risk score’s various trajectories – from its development at the intersection of epidemiology, general medicine and software engineering, to its usage in general practitioners’ offices, and its validation infrastructures. Exploring the translations from population to individual and back that are at work in the risk score and in the primary prevention of cardiovascular disease, we examine how versions and distributions of accountability are invoked and practiced as the score is developed and put to use. The case of a simple risk score used in everyday counselling brings into relief some key shifts in configurations of accountability with emerging versions of ‘health by the algorithm’. While there is an increasing authority of algorithmic tools in the fabric of clinical encounters, risk scores are interwoven with local specificities of the healthcare system and continue to be in the making.

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“…Big-data analysis has been heavily politicised due to numerous cases that show how this research can disrupt privacy and be exploited by for discriminatory purposes, human rights abuses, surveillance, or political gain (e.g., Klimburg-Witjes & Wentland, 2021;Saetnan et al, 2018). Similarly, the datafication of medicine and health raises questions about the potential misuse of big data due to the highly sensitive nature of medical and genetic data (Hoeyer et al, 2019;Rothstein, 2015;Ruckenstein & Schüll, 2017). Although the security of big data is discussed in diverse fields, discussion of biobanking as a specific area of bigdata research is minimal.…”

Section: Introductionmentioning

confidence: 99%

Anticipatory Governance in Biobanking: Security and Risk Management in Digital Health

Rychnovská

2021

Sci Eng Ethics

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Although big-data research has met with multiple controversies in diverse fields, political and security implications of big data in life sciences have received less attention. This paper explores how threats and risks are anticipated and acted on in biobanking, which builds research repositories for biomedical samples and data. Focusing on the biggest harmonisation cluster of biomedical research in Europe, BBMRI-ERIC, the paper analyses different logics of risk in the anticipatory discourse on biobanking. Based on document analysis, interviews with ELSI experts, and field research, three types of framing of risk are reconstructed: data security, privacy, and data misuse. The paper finds that these logics downplay the broader social and political context and reflects on the limits of the practices of anticipatory governance in biobanking. It argues that this regime of governance can make it difficult for biobanks to address possible future challenges, such as access to biomedical data by authorities, pressures for integrating biobank data with other type of personal data, or their use for profiling beyond medical purposes. To address potential controversies and societal implications related to the use of big data in health research and medicine, the paper suggests to expand the vocabulary and practices of anticipatory governance, in the biobanking community and beyond.

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Datafication and accountability in public health: Introduction to a special issue

Cited by 49 publications

References 89 publications

Data as promise: Reconfiguring Danish public health through personalized medicine

Data as promise: Reconfiguring Danish public health through personalized medicine

Following the algorithm: How epidemiological risk-scores do accountability

Anticipatory Governance in Biobanking: Security and Risk Management in Digital Health

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