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Digital family histories for data mining.

Overview of attention for article published in Perspectives in Health Information Management, October 2013
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Title
Digital family histories for data mining.
Published in
Perspectives in Health Information Management, October 2013
Pubmed ID
Authors

Robert Hoyt, Steven Linnville, Hui-Min Chung, Brent Hutfless, Courtney Rice

Abstract

As we move closer to ubiquitous electronic health records (EHRs), genetic, familial, and clinical information will need to be incorporated into EHRs as structured data that can be used for data mining and clinical decision support. While the Human Genome Project has produced new and exciting genomic data, the cost to sequence the human personal genome is high, and significant controversies regarding how to interpret genomic data exist. Many experts feel that the family history is a surrogate marker for genetic information and should be part of any paper-based or electronic health record. A digital family history is now part of the Meaningful Use Stage 2 menu objectives for EHR reimbursement, projected for 2014. In this study, a secure online family history questionnaire was designed to collect data on a unique cohort of Vietnam-era repatriated male veterans and a comparison group in order to compare participant and family disease rates on common medical disorders with a genetic component. This article describes our approach to create the digital questionnaire and the results of analyzing family history data on 319 male participants.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
Turkey 1 3%
Germany 1 3%
Switzerland 1 3%
Unknown 34 87%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 23%
Student > Bachelor 5 13%
Researcher 4 10%
Student > Ph. D. Student 4 10%
Student > Postgraduate 3 8%
Other 8 21%
Unknown 6 15%
Readers by discipline Count As %
Medicine and Dentistry 7 18%
Computer Science 6 15%
Agricultural and Biological Sciences 3 8%
Social Sciences 3 8%
Psychology 3 8%
Other 7 18%
Unknown 10 26%