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Data quality in a DRG-based information system

C Colin1, R Ecochard, F Delahaye

  • 1Medical Information Center of the Hospices Civils de Lyon, Hôtel Dieu, France.

International Journal for Quality in Health Care : Journal of the International Society for Quality in Health Care
|September 1, 1994
PubMed
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This study assessed medical data quality in a major hospital, finding significant errors in personal data, abstracting, and coding. Regular quality control is crucial for maintaining database coherence and healthcare team participation.

Area of Science:

  • Medical Informatics
  • Health Data Quality
  • Hospital Information Systems

Background:

  • The
  • Hospices Civils de Lyon
  • Edouard Herriot Hospital
  • maintains a critical medical information system.
  • Evaluating data quality is essential for accurate patient records and healthcare management.

Purpose of the Study:

  • To evaluate the quality of medical data collected from a large French hospital.
  • To identify error rates in patient personal data, abstracting, coding, and data entry.
  • To assess data quality for hospital morbidity description and Diagnosis-Related Group (DRG) case-mix.

Main Methods:

  • A random sample of 593 discharge abstracts from 12 hospital wards was analyzed.

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  • Quality control included checking patient data, abstract completeness, abstracting quality, coding accuracy, and data entry.
  • Error rates were calculated for concordance with medical records and for DRG classification impact.

    • Main Results:

    • Personal data errors occurred at a 4.4% rate, primarily in names and dates of birth.
    • Abstracting medical information had error rates of 11.5% (overall) and 7.5% (DRG-modifying).
    • Coding diagnoses/procedures showed 11.4% (overall) and 1.3% (DRG-modifying) errors; data entry errors were 2% (overall) and 0.2% (DRG-modifying).

    Conclusions:

    • The study highlights significant data quality issues in various aspects of the hospital information system.
    • Regular quality control is imperative to ensure database coherence and reflect healthcare team participation.
    • Addressing identified errors is crucial for reliable hospital morbidity reporting and accurate DRG case-mix.