Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions | Zendy

Zhang Yili | Zendy; Güneş Koru | Zendy

Open Access

Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions

Author(s) -

Zhang Yili,

Güneş Koru

Publication year - 2019

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocz201

Subject(s) - medicaid , data quality , health care , computer science , quality (philosophy) , data science , taxonomy (biology) , syntax , data collection , artificial intelligence , operations management , engineering , statistics , political science , metric (unit) , philosophy , botany , mathematics , epistemology , law , biology

Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States.

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