The perils of data misreporting

Authors:
Daniel P. Lorence
Affiliations:
Pennsylvania State University, University Park, PA
Venue:
Communications of the ACM - Blueprint for the future of high-performance networking
Year:
2003

Citing 4
Cited 2

Anchoring data quality dimensions in ontological foundations

Communications of the ACM
Data quality in context

Communications of the ACM
Examining data quality

Communications of the ACM
A product perspective on total data quality management

Communications of the ACM

Impact of the Union and Difference Operations on the Quality of Information Products

Information Systems Research
Using Data Mining Techniques to Discover Bias Patterns in Missing Data

Journal of Data and Information Quality (JDIQ)

Quantified Score

Hi-index	0.00

Visualization

Abstract

With physicians, information managers, and payers all influencing health care data classification, data can end up as an injured party. This hurts all of us, as reimbursement levels and error analysis increasingly rely on coded and classified data.