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. 2019 May 6:2019:630-638.
eCollection 2019.

A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures

Steven G Johnson  1 , Lisiane Pruinelli  1   2 , Alexander Hoff  3 , Vipin Kumar  3 , György J Simon  1 , Michael Steinbach  3 , Bonnie L Westra  1   2
Affiliations

A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures

Steven G Johnson et al. AMIA Jt Summits Transl Sci Proc. .

Abstract

The ability to assess data quality is essential for secondary use of EHR data and an automated Healthcare Data Quality Framework (HDQF) can be used as a tool to support a healthcare organization's data quality initiatives. Use of a general purpose HDQF provides a method to assess and visualize data quality to quickly identify areas for improvement. The value of the approach is illustrated for two analytics use cases: 1) predictive models and 2) clinical quality measures. The results show that data quality issues can be efficiently identified and visualized. The automated HDQF is much less time consuming than a manual approach to data quality and the framework can be rerun repeatedly on additional datasets without much effort.

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Figures

Figure 1:
Figure 1:
Radar Graph of Data Quality Measures for Two Variables
Figure 2:
Figure 2:
Domain Consistency Trends for Key CAUTI Variables
See this image and copyright information in PMC

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