TY - CONF
T1 - Rule templates and linked knowledge sources for rule-based information quality assessment in healthcare
AU - Wang, Zhan
AU - Zozus, Meredith Nahm
N1 - Funding Information:
The research in healthcare IQA described here is motivated by (1) recent increases in national attention towards secondary use of healthcare data for research through broad programs such as the National Institutes of Health (NIH) funded Healthcare Systems Research Collaboratory the NIH funded Clinical and Translational Science Awards and the Patient-Centered Outcomes Research Institute funded through the Affordable Care Act, (2) national emphasis on use of healthcare data for organizational performance assessment and improvement, i.e., Accountable Care Organizations, (3) almost ubiquitous availability of rich healthcare data in most institutions, and (4) lack of methods for IQA, specifically assessment of data accuracy, demonstrated effective in healthcare. We seek to ultimately demonstrate and evaluate rule-based data cleaning in healthcare.
Publisher Copyright:
© 2017 MIT Information Quality Program. All rights reserved.
PY - 2017
Y1 - 2017
N2 - Measuring and managing information quality in healthcare has remained largely uncharted territory with few notable exceptions. A rules-based approach to data error identification was explored through compilation of over 6,000 data quality rules used with healthcare data. The rules were categorized based on topic and logic yielding twenty rule templates and associated knowledge tables used by the rule templates. Knowledge sources for the knowledge tables were sought and identified for eleven of the twenty rule templates and have to be created for the remaining nine. This work provides a framework with which data quality rules can be organized and shared as rule templates and knowledge tables. While there is significant additional work to be done in this area, the exploration of the rule template and associated knowledge tables approach here shows the approach to be possible and scalable.
AB - Measuring and managing information quality in healthcare has remained largely uncharted territory with few notable exceptions. A rules-based approach to data error identification was explored through compilation of over 6,000 data quality rules used with healthcare data. The rules were categorized based on topic and logic yielding twenty rule templates and associated knowledge tables used by the rule templates. Knowledge sources for the knowledge tables were sought and identified for eleven of the twenty rule templates and have to be created for the remaining nine. This work provides a framework with which data quality rules can be organized and shared as rule templates and knowledge tables. While there is significant additional work to be done in this area, the exploration of the rule template and associated knowledge tables approach here shows the approach to be possible and scalable.
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M3 - Paper
AN - SCOPUS:85084164352
T2 - 22nd MIT International Conference on Information Quality, ICIQ 2017
Y2 - 6 October 2017 through 7 October 2017
ER -