10th annual utah's health services research conference - a data model for representation and...

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.

Overview

• Data Characterization• Data Characterization Model• Architecture• Quality Analysis Framework• Examples• Dimensions• Preliminary Conceptual Model

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.

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.

Architecture

Query Tool

MetadataRepository

VIRGOQuality Analysis

ADAPT

ADAPT

ADAPT

ADAPT

Counts&Data

Secur

ity

Security

TerminologyServer

DataSources

Quality & Analytics Framework

Quality Service

Quality Analysis Repository

Data SourcesAdapters

TerminologyServices

MetadataServices

Example – Categorical Variable - Completeness

• Completeness – Extent which data are not missing and is of sufficient breadth and depth.

Data Sources

Quality Service

OpenFurther

An Example – Continuous Variables.

• Serum creatinine level on a random sample of synthetic data.

• Example Dimensions that fall under: Anomaly, Correctness, Accuracy.

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.

DQ - Dimensions

Conceptual Model

• Dimensions computable conceptual model

Thank You!