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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Detecting Systemic Data Quality Issues in Electronic Health Records.

Casey N Ta1, Chunhua Weng1

  • 1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary

This study introduces a novel method for assessing electronic health record data quality by analyzing temporal patterns in clinical concepts. Findings reveal system-wide factors influencing data quality, crucial for reliable clinical research.

Keywords:
Cluster AnalysisData AccuracyElectronic Health Records

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Area of Science:

  • Health Informatics
  • Clinical Data Science
  • Biostatistics

Background:

  • Electronic health records (EHRs) are vital for clinical research but present data quality challenges due to observational collection and healthcare process biases.
  • Assessing and understanding these data quality issues is critical for the validity of EHR-derived clinical research findings.

Purpose of the Study:

  • To develop and apply a novel methodology for assessing data quality in EHRs.
  • To identify and characterize temporal patterns in clinical concept frequencies to detect data quality issues.
  • To investigate the influence of system-wide factors on EHR data quality.

Main Methods:

  • Developed a methodology for data quality assessment using domain-level aggregate statistics and concept-level temporal frequencies.
  • Normalized and applied K-means clustering to annual concept frequencies to detect common temporal patterns.
  • Applied the methodology to the Columbia University Irving Medical Center Observational Medical Outcomes Partnership database.

Main Results:

  • Generated domain-aggregate and cluster plots revealing diverse temporal patterns in clinical concepts (conditions, drugs, procedures).
  • Analysis of the condition domain identified patterns indicative of data quality issues.
  • These patterns suggest that system-wide factors significantly impact individual concept frequencies in EHR data.

Conclusions:

  • The proposed methodology effectively visualizes and identifies data quality issues in EHRs by analyzing temporal concept patterns.
  • System-wide factors demonstrably influence the quality and frequency of clinical data within EHRs.
  • This approach aids in improving the reliability of secondary EHR data analysis for clinical research.