HCC: Small: Creating a Data-Driven World: Situated Practices of Collecting, Curating, Manipulating, and Deploying Data in Healthcare

该项目将研究情境实践、人类假设和组织惯例，这些实践、假设和组织惯例将“小数据”转化为可挖掘的“大数据”存储，用于测量和指标。 目前，我们对大数据的起源以及收集、管理、操纵和部署这些巨大信息资源的知识是有限的。我们更不了解这些活动的社会和文化影响，特别是在非学术背景下。越来越多的学者敦促对大数据科学的方法、分析假设和潜在偏见进行批判性的质疑。在我们能够理解它们的社会和政治影响之前，需要对大数据集的组装和操作所处的实践进行细致入微的理解，特别是如果我们要根据对此类数据集操作的分析来评估科学结果的质量。该研究将通过医疗保健中产科数据生产的多站点民族志进行，这是一个大数据和相关指标既重要又存在问题的领域。 首先，它将研究创建形成数据集的大量信息的情景实践和生活经验。其次，它将跟踪结果如何通过自动化的措施和算法出现，并影响它们应该反映的环境。这项研究跨越了数据的生命周期。它将调查信息是如何由从业人员，职员和编码员收集，并转化为所谓的“干净”数据的本地存储库，以供性能改进专家操作。然后，它将跟踪信息如何在全州范围的数据中心中进一步传输和细化，并由主要的质量改进组织部署。最后，研究将跟踪汇总的数据回到当地医院本身，并评估数据可视化和绩效指标如何影响当地决策和医院运作。该项目的更广泛影响包括近期和长期效益。在短期内，这项研究将有利于个人和组织努力解决有关如何组织本地资源以生产和部署大数据服务于管理和绩效改进目标的问题。从长远来看，这项研究将产生基本的概念模型，有助于创建关于创建和使用大数据的社会，道德和政治影响的设计建议和实践指南。

This project will study the situated practices, human assumptions, and organizational routines that transform "little data" into mineable stores of "big data" harnessed for measures and metrics. Currently, our knowledge about the origins of big data and what goes into collecting, curating, manipulating, and deploying these huge information resources is limited. We know even less about the social and cultural implications of these activities, particularly in non-academic contexts. A growing group of scholars urge critical interrogation of the methods, analytical assumptions, and underlying biases of big data science. A nuanced understanding of the situated practices through which big datasets are assembled and manipulated is required before we can comprehend their social and political implications, particularly if we are to evaluate the quality of the scientific results based on analysis on the manipulation of such datasets. The research will be carried out through a multi-sited ethnography of obstetrical data production in healthcare, an area where big data and associated metrics are both important and problematic. First, it will examine the situated practice and lived experience of creating the massive amounts of information that come to form the datasets. Second, it will trace how the results emerge through automatized measures and algorithms and affect the very environments they are supposed to reflect. This research spans the lifecycle of data. It will investigate how information is collected by practitioners, clerks, and coders and transformed into local repositories of supposedly "clean" data to be manipulated by performance improvement specialists. It will then trace how information is transferred and refined further in a statewide data center and deployed by a major quality improvement organization. Finally, the research will follow the aggregated data back to the local hospitals themselves and assess how data visualizations and performance measures affect local decisions and hospital functioning. The broader impacts of this project include both near and long-term benefits. In the short term, this research will benefit the individuals and organizations struggling with questions about how to organize local resources to produce and deploy big data in service of management and performance improvement goals. In the long term, this research will generate foundational conceptual models that help to create design recommendations and practice guidelines regarding the social, ethical, and political implications of creating and using big data.