"Garbage in, garbage out"

Johansen, Monika Alise; Scholl, Jeremiah; Hasvold, Per; Ellingsen, Gunnar; Bellika, Johan Gustav

doi:10.1145/1460563.1460646

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…However, the amount of valuable resources, with their potential to improve the clinical decisions and quality of healthcare settings, compounds the data quality issues specified in Table 1. Our findings contrast with prior notions blaming poor data quality on data entry (Grimes, 2010;Johansen et al, 2008). Challenges in healthcare data quality are much more complex, with the main issues summarized in Table 1.…”

Section: Rq1contrasting

confidence: 99%

A Review of the State of the Art of Data Quality in Healthcare

Liu

Talaei-Khoei

Peng

2023

Journal of Global Information Management

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Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.

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Section: Rq1contrasting

confidence: 99%

A Review of the State of the Art of Data Quality in Healthcare

Liu

Talaei-Khoei

Peng

2023

Journal of Global Information Management

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“…Disease surveillance denotes a practice of ongoing data collection, analysis and dissemination for the purposes of implementing and evaluating public health policies and practices [31]. Some work in disease surveillance in CSCW has been on developing systems using data sources such as mobile data to predict flu dynamics [17], evaluating the potential of public resources such as Wikipedia texts to measure global disease [37], or studying sociotechnical issues related to data collection in hospitals for surveillance purposes [25]. There is also extensive CSCW research on health care, which is focused mostly on clinical work and how it is transformed by computers [20].…”

Section: Related Workmentioning

confidence: 99%

“…This type of work is mainly concerned with the act of data collection, which differs from passive disease surveillance (c.f. [25]) which makes use of data obtained in hospitals or health facilities from health-seeking patients.…”

Section: Related Workmentioning

confidence: 99%

Data Bites Man

Sanches

Brown

2018

Proc. ACM Hum.-Comput. Interact.

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Malaria surveillance is a practice concerned with collection and analysis of data. This paper presents an ethnographical account of an international team of researchers producing data about malaria in the Zanzibar archipelago. We show that malaria is increasingly an electronic entity, inextricably interwoven with the practices of data workers using ICT tools. The use of mobile ICT tools enables new data production practices that include situated coordination mechanisms such that 1) more people can be surveyed, including individuals not suffering from malaria but possibly carrying the parasite, 2) greater geographical areas can be covered, and 3) more data can be validated and included in malaria statistics. As electronic data, malaria builds and mobilizes diverse human, organizational, and infrastructural worlds around it, who must now be dedicated to its production, management, and care. We discuss implications for design of digital data collection tools that support the work of teams surveying malaria, as well as implications for disease surveillance methods and practices.

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“…If data is to be repurposed from existing systems, there must be clear understanding of its structure as well as the data management practices used to maintain the data [10]. Deviation from intended data management practices, particularly those hardened into systems, can make repurposing data difficult as information capture no longer adheres to expected norms.…”

Section: Data Lifecycle In Humanitarian Responsementioning

confidence: 99%

Collaborative Analytics and Brokering in Digital Humanitarian Response

Hellmann

Maitland

Tapia

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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During large scale humanitarian crises, relief practitioners identify data used for decision making and coordination, as critical to their operations. Implicit in this need is the required capabilities for analyzing data. Given the rapidly evolving systems of collaborative data management and analysis in digital humanitarian efforts, information scientists and practitioners alike are keen to understand the role of data analytics in response operations. Through a case study of a digital humanitarian collaborative effort, we examine the processes for big and small data analytics, specifically focusing on data development, sharing, and collaborative analytics. Informed by theories of articulation work and collaborative analytics, we analyze data from indepth interviews with digital humanitarians. Our findings identify key practices and processes for collaborative analytics in resource constrained environments, particularly the role of brokering, and in turn generate design recommendation for collaborative analytic platforms.

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"Garbage in, garbage out"

Cited by 10 publications

References 28 publications

A Review of the State of the Art of Data Quality in Healthcare

A Review of the State of the Art of Data Quality in Healthcare

Data Bites Man

Collaborative Analytics and Brokering in Digital Humanitarian Response

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