10th Annual Utah's Health Services Research Conference - A Data Model for Representation and Storage...
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Transcript of 10th Annual Utah's Health Services Research Conference - A Data Model for Representation and Storage...
![Page 1: 10th Annual Utah's Health Services Research Conference - A Data Model for Representation and Storage of Biomedical Data Quality. By: Naresh Sundar Rajan](https://reader036.fdocuments.in/reader036/viewer/2022081519/55c456a7bb61eb27678b45d9/html5/thumbnails/1.jpg)
A Data Model for Representation and Storage of Biomedical Data Quality
Naresh Sundar Rajan MS,
Biomedical Informatics Core (BMIC), Department of Biomedical Informatics,
University of Utah.
This work has been supported in part by the National Center for Research Resources award UL1RR025764
and the Agency for Healthcare Research and Quality award HS019862.
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Overview
• Data Characterization• Data Characterization Model• Architecture• Quality Analysis Framework• Examples• Dimensions• Preliminary Conceptual Model
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Data Characterization
• Clinical research studies such as Health Services Research (HSR), Comparative Effectiveness Research (CER) etc., rely on EHR data.
• EHR data is prone to data quality issues• Need for systematic and generalizable
methods to characterize data.
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Data Characterization Model
• Clinical studies might require the federation and integration of data from multiple data sources to create large cohorts.
• Addressing multi-source data characterization requires a common representations that support the semantic and syntactic differences in data sources.
• In order to make this representation computable, these quality assessments need to be stored in a data model that comprises all the dimensions of data quality.
![Page 5: 10th Annual Utah's Health Services Research Conference - A Data Model for Representation and Storage of Biomedical Data Quality. By: Naresh Sundar Rajan](https://reader036.fdocuments.in/reader036/viewer/2022081519/55c456a7bb61eb27678b45d9/html5/thumbnails/5.jpg)
Architecture
Query Tool
MetadataRepository
VIRGOQuality Analysis
ADAPT
ADAPT
ADAPT
ADAPT
Counts&Data
Secur
ity
Security
TerminologyServer
DataSources
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Quality & Analytics Framework
Quality Service
Quality Analysis Repository
Data SourcesAdapters
TerminologyServices
MetadataServices
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Example – Categorical Variable - Completeness
• Completeness – Extent which data are not missing and is of sufficient breadth and depth.
Data Sources
Quality Service
OpenFurther
![Page 8: 10th Annual Utah's Health Services Research Conference - A Data Model for Representation and Storage of Biomedical Data Quality. By: Naresh Sundar Rajan](https://reader036.fdocuments.in/reader036/viewer/2022081519/55c456a7bb61eb27678b45d9/html5/thumbnails/8.jpg)
An Example – Continuous Variables.
• Serum creatinine level on a random sample of synthetic data.
• Example Dimensions that fall under: Anomaly, Correctness, Accuracy.
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Data Quality (DQ) Dimensions
– Literature survey for various data quality dimensions and concepts.• >1100 research articles reviewed.• Dimensions/Concepts extracted manually by
reviewing.
– Issues such as Accuracy, Completeness, Timeliness, Believability, Objectivity, Volume of data, and etc.
– About 50 dimensions extracted from literature.
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DQ - Dimensions
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Conceptual Model
• Dimensions computable conceptual model
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Thank You!