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Big Bad Data: Law, Public Health, and Biomedical Databases

Overview of attention for article published in The Journal of Law, Medicine & Ethics, January 2021
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Title
Big Bad Data: Law, Public Health, and Biomedical Databases
Published in
The Journal of Law, Medicine & Ethics, January 2021
DOI 10.1111/jlme.12040
Pubmed ID
Authors

Sharona Hoffman, Andy Podgurski

Abstract

The accelerating adoption of electronic health record (EHR) systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic "big data," in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
United Kingdom 2 1%
Canada 2 1%
Finland 1 <1%
Netherlands 1 <1%
United States 1 <1%
Unknown 158 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 19%
Student > Master 32 19%
Researcher 31 18%
Student > Postgraduate 10 6%
Student > Doctoral Student 8 5%
Other 26 15%
Unknown 29 17%
Readers by discipline Count As %
Medicine and Dentistry 31 18%
Computer Science 28 17%
Social Sciences 16 10%
Business, Management and Accounting 11 7%
Nursing and Health Professions 10 6%
Other 33 20%
Unknown 39 23%