LAUSR

Healthcare organizations across the globe are currently grappling to implement tools and practices to transform data from “refuse to riches,” a movement propelled by mass adoption of electronic health records (EHRs), sensors, and servers that can hold an ever-expanding volume of digital data.1 Allegedly, “By digitizing, combining and effectively using big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits.”2 The potential is “. . . to improve care, save lives and lower costs.”2 As a consequence, organizations are struggling under massive institutional pressures to make healthcare “data-driven” against the messy reality of creating, managing, analyzing, and using data for management, decision-making, accountability, and medical research.3 However, data do not sit in ready repository, fully formed, and easily harvestable.4 Data must be created through various forms of situated work. Even when data is a byproduct—“exhaust data”— from other processes, data has to be filtered, analyzed, and interpreted.5 While scholars have acknowledged the situated and effortful nature of data production along with the inherent subjectivities of data, these practices have been little investigated.