Analytics in action: users and predictive data in the neonatal intensive care unit

Abstract

As the data phenomenon has grown, researchers have increasingly recognized the need to investigate algorithmic and data-driven technology, practices, and culture. While a number of studies have investigated the process of designing algorithms or examined the contours and consequences of algorithms themselves, more research is needed that details the ways in which users of data-driven technology make sense of algorithmic output and the conditions under which these processes unfold. This paper explores how users derive knowledge from predictive algorithms in medical contexts. Drawing upon interviews and observations of a neonatal intensive care unit, I examine how clinicians use data-driven predictive algorithms designed to forecast the onset of infection. Although the developers intend the technology to function as an early warning system, clinicians do not formulate knowledge from the technology solely as intended. Instead, I find that clinicians engage in a set of interpretive processes that I call ‘conditioned reading,’ and ‘accumulative reading.’ I suggest that these processes are possible due to the particular conditions of the medical context, including the institutional entrenchment of evidence-based medicine. I conclude by arguing that fully theorizing the social implications of data analytics will require researchers to investigate the role of institutional contexts.

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