A method of electronic health data quality assessment: Enabling data provenance

2017 ◽  
Author(s):  
Yuling Sun ◽  
Tun Lu ◽  
Ning Gu
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Data ◽  
Data Provenance ◽  
Data Quality Assessment ◽  
Electronic Health

scholarly journals A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research

Medical Care ◽  
2012 ◽  
Vol 50 ◽  
pp. S21-S29 ◽  
Author(s):  
Michael G. Kahn ◽  
Marsha A. Raebel ◽  
Jason M. Glanz ◽  
Karen Riedlinger ◽  
John F. Steiner
Keyword(s):  
Clinical Research ◽  
Electronic Health Record ◽  
Data Quality ◽  
Quality Assessment ◽  
Single Site ◽  
Health Record ◽  
Data Quality Assessment ◽  
Electronic Health

Robust estimation of infant feeding indicators by data quality assessment of longitudinal Electronic Health Records from birth up to 18 months of life

2021 ◽  
pp. 106147
Author(s):  
García-de-León-Chocano Ricardo ◽  
Sáez Carlos ◽  
Muñoz-Soler Verónica ◽  
Oliver-Roig Antonio ◽  
García-de-León-González Ricardo ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Data Quality ◽  
Quality Assessment ◽  
Infant Feeding ◽  
Robust Estimation ◽  
Health Records ◽  
Data Quality Assessment ◽  
Electronic Health

TAQIH, a tool for tabular data quality assessment and improvement in the context of health data

2019 ◽  
Vol 181 ◽  
pp. 104824 ◽  
Author(s):  
Roberto Álvarez Sánchez ◽  
Andoni Beristain Iraola ◽  
Gorka Epelde Unanue ◽  
Paul Carlin
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Data ◽  
Tabular Data ◽  
Data Quality Assessment ◽  
Quality Assessment And Improvement

scholarly journals Fuzzy-Provenance Architecture for Effort Metric Data Quality Assessment

2009 ◽  
Author(s):  
Rita Cristina Galarraga Berardi ◽  
Duncan Dubugras Alcoba Ruiz
Keyword(s):  
Fuzzy Logic ◽  
Data Quality ◽  
Quality Assessment ◽  
Software Metrics ◽  
Data Provenance ◽  
Logical Inference ◽  
Software Companies ◽  
Data Quality Assessment

Software companies rely on stored metric data in order to track and manage their projects, through analyzing, monitoring and estimating software metrics. If managers cannot believe the metrics data, the product that is being developed is fated to fail. Currently, the assessment of software effort is subjective and derived mainly through managers’ assumptions, which is fundamentally an error-prone process. We present an architecture for assessing data quality of software effort metric based on data provenance associated with a mechanism of logical inference (fuzzy logic). The contribution is to provide an assessment to search evident reasons for a low quality in order to ensure that the metrics can be used with sufficient reliability.

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