Niccolò Tempini

E-government and organizational change: Reappraising the role of ICT and bureaucracy in public service delivery

Abstract There is a substantial literature on e-government that discusses information and communication technology (ICT) as an instrument for reducing the role of bureaucracy in government organizations. The purpose of this paper is to offer a critical discussion of this literature and to provide a complementary argument which favors the use of ICT in the public sector to support the operations of bureaucratic organizations. Based on the findings of a case study – of the Venice Municipality in Italy – the paper discusses how ICT can be used to support rather than eliminate bureaucracy. Using the concepts of e-bureaucracy and functional simplification and closure, the paper proposes evidence and support for the argument that bureaucracy should be preserved and enhanced where e-government policies are concerned. Functional simplification and closure are very valuable concepts for explaining why this should be a viable approach.

Patient Data as Medical Facts: Social Media Practices as a Foundation for Medical Knowledge Creation

This paper investigates a web-based, medical research network that relies on patient self-reporting to collect and analyze data on the health status of patients, mostly suffering from severe conditions. The network organizes patient participation in ways that break with the strong expert culture of medical research. Patient data entry is largely unsupervised. It relies on a data architecture that encodes medical knowledge and medical categories, yet remains open to capturing details of patient life that have as a rule remained outside the purview of medical research. The network thus casts the pursuit of medical knowledge in a web-based context, marked by the pivotal importance of patient experience captured in the form of patient data. The originality of the network owes much to the innovative amalgamation of networking and computational functionalities built into a potent social media platform. The arrangements the network epitomizes could be seen as a harbinger of new models of organizing medical knowledge creation and medical work in the digital age, and a complement or alternative to established models of medical research.

Everything counts in large amounts: a critical realist case study on data-based production

Contemporary digital ecosystems produce vast amounts of data every day. The data are often no more than microscopic log entries generated by the elements of an information infrastructure or system. Although such records may represent a variety of things outside the system, their powers go beyond the capacity to carry semantic content. In this article, we harness critical realism to explain how such data come to matter in specific business operations. We analyse the production of an advertising audience from data tokens extracted from a telecommunications network. The research is based on an intensive case study of a mobile network operator that tries to turn its subscribers into an advertising audience. We identify three mechanisms that shape data-based production and three properties that characterize the underlying pool of data. The findings advance the understanding of many organizational settings that are centred on data processing.

Governing PatientsLikeMe: Information Production and Research Through an Open, Distributed, and Data-Based Social Media Network

Many organizations develop social media networks with the aim of engaging a wide range of social groups in the production of information that fuels their processes. This effort appears to crucially depend on complex data structures that allow the organization to connect and collect data from a myriad of local contexts and actors. One such organization, PatientsLikeMe, is developing a platform with the aim of connecting patients with one another while collecting self-reported medical data, which it uses for scientific and commercial medical research. Here the question of how technology and the underlying data structures shape the kind of information and medical evidence that can be produced through social media-based arrangements comes powerfully to the fore. In this observational case study, I introduce the concepts of information cultivation and social denomination to explicate how the development of such a data collection architecture requires a continuous exercise of balancing between the conflicting demands of patient engagement, necessary for collecting data in scale, and data semantic context, necessary for effective capture of health phenomena in informative and specific data. The study extends the understanding of the context-embeddedness of information phenomena and discusses some of the social consequences of social media models for knowledge making.

Emerging ethical issues regarding digital health data. On the World Medical Association Draft Declaration on Ethical Considerations Regarding Health Databases and Biobanks

Throughout many parts of the world, biomedical research ethics is based on a core body of well-established norms, rules, and principles, including the Declaration of Helsinki, the Nuremberg Code, the Belmont Report, and the International Ethical Guidelines for Biomedical Research Involving Human Subjects (1). The overarching goal of these codifications is to protect people against harms arising from research, and from researchers experimenting on them without their knowledge and permission.

Till data do us part: Understanding data-based value creation in data-intensive infrastructures

Abstract Much of the literature on value creation in social media-based infrastructures has largely neglected the pivotal role of data and their processes. This paper tries to move beyond this limitation and discusses data-based value creation in data-intensive infrastructures, such as social media, by focusing on processes of data generation, use and reuse, and on infrastructure development activities. Building on current debates in value theory, the paper develops a multidimensional value framework to interrogate the data collected in an embedded ethnographical case study of the development of PatientsLikeMe , a social media network for patients. It asks when, and where, value is created from the data, and what kinds of value are created from them, as they move through the data infrastructure; and how infrastructure evolution relates to, and shapes, existing data-based value creation practices. The findings show that infrastructure development can have unpredictable consequences for data-based value creation, shaping shared practices in complex ways and through a web of interdependent situations. The paper argues for an understanding of infrastructural innovation that accounts for the situational interdependencies of data use and reuse. Uniquely positioned, the paper demonstrates the importance of research that looks critically into processes of data use in infrastructures to keep abreast of the social consequences of developments in big data and data analytics aimed at exploiting all kinds of digital traces for multiple purposes.

Concealment and Discovery: The Role of Information Security in Biomedical Data Re-Use

This paper analyses the role of information security (IS) in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in material, social and regulatory landscapes of research. We consider data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region, and the Medical and Environmental Data Mash-up Infrastructure, a project bringing together researchers to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts, and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalized in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the reusability of biomedical data, while encouraging exploratory analyses of secondary data sources.

Where health and environment meet: the use of invariant parameters in big data analysis

The use of big data to investigate the spread of infectious diseases or the impact of the built environment on human wellbeing goes beyond the realm of traditional approaches to epidemiology, and includes a large variety of data objects produced by research communities with different methods and goals. This paper addresses the conditions under which researchers link, search and interpret such diverse data by focusing on “data mash-ups”—that is the linking of data from epidemiology, biomedicine, climate and environmental science, which is typically achieved by holding one or more basic parameters, such as geolocation, as invariant. We argue that this strategy works best when epidemiologists interpret localisation procedures through an idiographic perspective that recognises their context-dependence and supports a critical evaluation of the epistemic value of geolocation data whenever they are used for new research purposes. Approaching invariants as strategic constructs can foster data linkage and re-use, and support carefully-targeted predictions in ways that can meaningfully inform public health. At the same time, it explicitly signals the limitations in the scope and applicability of the original datasets incorporated into big data collections, and thus the situated nature of data linkage exercises and their predictive power.

Genomics and big data in biomedicine

Authors gratefully acknowledge funding by the European Research Council, grant number 335925 (DATA_SCIENCE), and the helpful comments of editors to an earlier draft.