Data Tracking in Search of Workflows

Møller, Naja L. Holten; Bjørn, Pernille; Villumsen, Jonas Christoffer; Hancock, Tine C. Hansen; Aritake, Toshimitsu; Tani, Shigeyuki

doi:10.1145/2998181.2998296

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…For example, Langstrup 30 describes the collection of PROM (Patient-Reported Outcome Measures) data by patients; Hult et al 26 describe the work of nurses adapting to different patient literacies; Chorev 24 chronicles the work of clinicians to make sense of ambiguous data; and Bjørnstad and Ellingsen 32 call attention to the invisible work of creating meaningful integration of information systems. Widespread adoption of digital Information Infrastructures for healthcare increases the capacity to produce, store, and analyze data, 43 and widespread availability of data tools mean that increasing expectations are developing for the types and depth of biomedical and organizational research that can be done using second-order data. Such managerial and administrative data work presents novel data practices both for professionals and for management and administrative staff.…”

Section: Implications Of Healthcare Data Workmentioning

confidence: 99%

Data work in healthcare: An Introduction

Bossen

Pine

Cabitza

et al. 2019

Health Informatics J

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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.

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Section: Implications Of Healthcare Data Workmentioning

confidence: 99%

Data work in healthcare: An Introduction

Bossen

Pine

Cabitza

et al. 2019

Health Informatics J

View full text Add to dashboard Cite

show abstract

“…By creating space for agency and personal autonomy, systemic and programmatic interventions become more effective. For example, among homeless care providers in the U.S., there is evidence that outcomes improve when people can influence programs of care and set their own priorities [55,64].…”

Section: Professional Discretion In Caseworkmentioning

confidence: 99%

Assembling the Case

Møller

Fitzpatrick

Dantec

2019

Proc. ACM Hum.-Comput. Interact.

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This study examines caseworkers' and citizens' interactions when assembling resource development applications for citizens with serious health and personal issues. As with other types of welfare schemes, the application serves as a mechanism of both support and control. From our study, we illustrate how an increased reliance on data is transforming the citizen-caseworker interaction in social welfare. We characterize this transformation as 'datafication': a phenomenon where the increased reliance on data for decision-support across contexts of data production makes it challenging for individual citizens to contest or correct data-born accounts of their situation. Our contribution is two-fold: first, we empirically characterize the citizen-caseworker interaction in the application process. Second, we discuss how citizens' private resourcing complements the formal application process and provides them with strategies to give authority to their case and exercise personal autonomy. The private resourcing practices we observed show how integrating supplementary accounts from citizens into the systems that caseworkers rely on could make citizens' experiences and social context legible. This in turn has policy and technology design implications as public services increasingly introduce data-driven modes of case management.

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“…Conceptually, I draw on the computer-supported cooperative work (CSCW) field, which has a long tradition of exploring how work is actually conducted at micro level 9,10 (for recent empirical examples, refer previous studies 11,12 ). I supplement this with the notion of qualculation from the science and technology studies (STS) field 13,14 in order to highlight how the situated work of calculation and judgment is required to interpret and use information from one context to the next.…”

Section: Introductionmentioning

confidence: 99%

The qualculative dimension of healthcare data interoperability

Pine

2019

Health Informatics J

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Research on interoperability and information exchange between information technology systems touts the use of secondary data for a variety of purposes, including research, management, quality improvement, and accountability. However, many studies have pointed out that this is difficult to achieve in practice. Hence, this article aims to examine the causes for this by reporting an ethnographic study of the data work performed by medical records coders and birth certificate clerks working in a hospital system to uncover the practices of creating administrative data (e.g. secondary data). The article illustrates that clerks and coders use situated qualitative judgments of the accuracy and authority of different primary medical accounts. Coders and clerks also employ their understandings of the importance of different future uses of data as they make crucial decisions about how much discretion to exercise in producing accurate data and how much effort to put toward clarifying problematic medical data. These findings suggest that information technology systems designed for interoperability and secondary data also need to be designed in ways that support the qualculative practices of data workers in order to succeed, including making future uses of data clear to data workers and finding ways to minimize conflicting data before data workers encounter it.

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Data Tracking in Search of Workflows

Cited by 15 publications

References 25 publications

Data work in healthcare: An Introduction

Data work in healthcare: An Introduction

Assembling the Case

The qualculative dimension of healthcare data interoperability

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