Stevens Marthe, Wehrens Rik, de Bont Antoinette
Department of Health Care Governance, Erasmus School of Health Policy & Management, P.O. Box 1738, 3000, DR, Rotterdam, the Netherlands.
Soc Sci Med. 2020 Aug;258:113116. doi: 10.1016/j.socscimed.2020.113116. Epub 2020 Jun 17.
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.
数据科学和精神病学有着不同的认知文化,它们在数据驱动的倡议(如大数据、机器学习)中汇聚在一起。关于这些倡议的文献似乎要么淡化要么过度强调这两个领域之间的认知差异。在本文中,我们研究数据科学和精神病学认知文化的趋同与分歧。这种方法更有可能捕捉到文化在哪些方面以及如何存在差异,并深入了解来自这两个领域的从业者如何找到尽管存在差异但仍能合作的方式。我们引入了“认知美德”的概念,以便从人种志角度关注认知差异,还引入了“交易区”的概念,以专注于差异是如何协商的。这就引出了我们的研究问题:在基于医院的数据驱动倡议中,数据科学和精神病学从业者如何协商认知差异?我们的研究结果基于一项人种志研究,在该研究中,我们观察了荷兰一家精神病医院科室基于机器学习技术开发患者预后预测模型的过程(2017年9月至2018年2月)。许多认知美德需要协商,例如数据纳入的完整性或选择性。这些差异在当地和临时进行交易,受到共同的认知美德(如系统方法)、边界对象和社会化过程的刺激。当美德差异过大、差异因讲故事而扩大且各方没有时间或能力了解对方时,交易就变得困难。在讨论中,我们认为我们的综合理论框架为研究不同从业者之间的合作如何进行以及何处可以改进提供了一种新方法。我们呼吁公开认知差异,因为这使得就数据驱动倡议的附加价值及其在医疗保健中可以发挥的作用进行有根据的讨论成为可能。