Why healthcare cannot—and should not—become data-driven, despite the many promises of intensified data sourcing. In contemporary healthcare, everybody seems to want more data, of higher quality, on more people, and to use this data for a wider range of purposes. In theory, such pervasive data collection should lead to a healthcare system in which data can quickly, efficiently, and unambiguously be interpreted and provide better care for patients, more efficient administration, enhanced options for research, and accelerated economic growth. In practice, however, data are difficult to interpret and the many purposes often undermine one another. In this book, anthropologist and STS scholar Klaus Hoeyer offers an in-depth look at the paradoxes surrounding healthcare data. Focusing on Denmark, a world leader in healthcare data infrastructures, Hoeyer shares the perspectives of different stakeholders, from epidemiologists to hospital managers, from patients to physicians, analyzing the social dynamics set in motion by data intensification and calling special attention to that which cannot be easily coded in a database. He illustrates how data can be at once helpful, overwhelming, and sometimes disastrous through concrete examples. The COVID-19 pandemic serves as a special closing case study that shows how these data paradoxes carry weighty political implications. By revealing the diverse and sometimes contradictory practices spawned by intensified data sourcing, Data Paradoxes raises vital questions about how we might better use healthcare data.
Author(s): Klaus Hoeyer
Series: Infrastructures Series
Edition: 1
Publisher: The MIT Press
Year: 2023
Language: English
Commentary: TruePDF
Pages: 325
Tags: Medical Informatics: Denmark: Case Studies; Medicine: Denmark: Data Processing: Case Studies; Medical Policy: Denmark: Case Studies
Cover
Half title
Series title
Title
Copyright
Contents
Preface
Introduction | Data Politics
1 | Data Promises
2 | Data Living
3 | Data Work
4 | Data Experiences
5 | Data Wisdom
6 | Data Pandemic
Conclusion | Data Paradoxes
Notes
References
Index