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Human-Data Interaction in Healthcare

Human-Data Interaction in Healthcare

Federico Cabitza, Angela Locoro
Copyright: © 2020 |Pages: 20
ISBN13: 9781799812043|ISBN10: 1799812049|EISBN13: 9781799812050
DOI: 10.4018/978-1-7998-1204-3.ch058
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MLA

Cabitza, Federico, and Angela Locoro. "Human-Data Interaction in Healthcare." Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1148-1167. https://doi.org/10.4018/978-1-7998-1204-3.ch058

APA

Cabitza, F. & Locoro, A. (2020). Human-Data Interaction in Healthcare. In I. Management Association (Ed.), Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications (pp. 1148-1167). IGI Global. https://doi.org/10.4018/978-1-7998-1204-3.ch058

Chicago

Cabitza, Federico, and Angela Locoro. "Human-Data Interaction in Healthcare." In Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1148-1167. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1204-3.ch058

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Abstract

In this chapter, we focus on an emerging strand of IT-oriented research, namely Human-Data Interaction (HDI) and on how this can be applied to healthcare. HDI regards both how humans create and use data by means of interactive systems, which can both assist and constrain them and the operational level of data work, which is both work on data and by data. Healthcare is a challenging arena where to test the potential of HDI towards a new, user-centered perspective on how to support and assess “data work”. This is especially true in current times where data are becoming increasingly big and many tools are available for the lay people, including doctors and nurses, to interact with health-related data. This chapter is a contribution in the direction of considering health-related data through the lens of HDI, and of framing data visualization tools in this strand of research. The intended aim is to let the subtler peculiarities among different kind of data and of their use emerge and be addressed adequately. Our point is that doing so can promote the design of more usable tools that can support data work from a user-centered and data quality perspective and the evidence-based validation of these tools.

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