Abstract
Data science and psychiatry have diverse epistemic cultures that come together in data-driven initiatives (e.g., big data, machine learning). The literature on these initiatives seems to either downplay or overemphasize epistemic differences between the fields. In this paper, we study the convergence and divergence of the epistemic cultures of data science and psychiatry. This approach is more likely to capture where and how the cultures differ and gives insights into how practitioners from both fields find ways to work together despite their differences. We introduce the notions of "epistemic virtues" to focus on epistemic differences ethnographically, and "trading zones" to concentrate on how differences are negotiated. This leads us to the following research question: how are epistemic differences negotiated by data science and psychiatry practitioners in a hospital-based data-driven initiative? Our results are based on an ethnographic study in which we observed a Dutch psychiatric hospital department developing prediction models of patient outcomes based on machine learning techniques (September 2017 - February 2018). Many epistemic virtues needed to be negotiated, such as completeness or selectivity in data inclusion. These differences were traded locally and temporarily, stimulated by shared epistemic virtues (such as a systematic approach), boundary objects and socialization processes. Trading became difficult when virtues were too diverse, differences were enlarged by storytelling and parties did not have the time or capacity to learn about the other. In the discussion, we argue that our combined theoretical framework offers a fresh way to study how cooperation between diverse practitioners goes and where it can be improved. We make a call for bringing epistemic differences into the open as this makes a grounded discussion possible about the added value of data-driven initiatives and the role they can play in healthcare.
Highlights
Data-driven initiatives aim to analyse large volumes of data from varied sources to improve healthcare delivery by predicting future health risks and treatment responses (Mayer-Schönberger and Cukier, 2014; Murdoch and Detsky, 2013)
We focus on the negotiation of epistemic differences and while language and objects can help to facilitate exchange, much is still unknown about the role of epistemic virtues in trading zones
After identifying the literature on trading zones and epistemic virtues as crucially relevant for the analysis of the empirical material, we developed the analysis further through posing such questions; (1) what is traded here, by whom and where? (2) Which epistemic virtues are in play? (3) What does this say about the respective epistemic cultures?
Summary
Data-driven initiatives aim to analyse large volumes of data from varied sources to improve healthcare delivery by predicting future health risks and treatment responses (Mayer-Schönberger and Cukier, 2014; Murdoch and Detsky, 2013) Examples of such initiatives are machine learning applications that analyse medical images (Sample, 2018) or predict readmissions on intensive care units (PacMed, 2019) and artificial intelligence systems that assist in the diagnosis of cancer (Somsashekhar et al, 2017). Epistemic practices guide how members of a field propose, communicate, evaluate and legitimize knowledge These epistemic practices are part of particular epistemic cultures (Knorr Cetina, 1981) that can be described as sets of specific norms, values, beliefs and traditions, that are “bonded through affinity, necessity and historical incidence” (Knorr Cetina, 1999:1). This means that epistemic cultures are known for specific activities for reasoning and establishing evidence, thereby determining what and how we know in communities
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