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Using routinely collected data for clinical research.

C Safran1

  • 1Charles A. Dana Research Institute, Boston, MA.

Statistics in Medicine
|April 1, 1991
PubMed
Summary
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Mining large clinical databases offers a powerful alternative for health services research when prospective data collection is not feasible. The ClinQuery program aids in exploring these valuable datasets, revealing insights from clinical data alone or combined with diagnoses.

Area of Science:

  • Health services research
  • Clinical informatics
  • Data mining

Background:

  • Prospective data collection in randomized controlled trials is often infeasible.
  • Hospitals are accumulating large clinical databases.
  • These databases represent a valuable resource for research.

Purpose of the Study:

  • To introduce ClinQuery, a computer program designed for exploring and analyzing large clinical databases.
  • To demonstrate the utility of clinical data in health services research.
  • To compare the effectiveness of using coded data versus clinical data.

Main Methods:

  • Development of the ClinQuery computer program for data exploration.
  • Analysis of clinical databases to assess data accuracy and utility.

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  • Comparison of health services research outcomes using different data sources.

    • Main Results:

    • Clinical data mining is a powerful tool for health services research.
    • Coded data can be inaccurate, making alternative clinical data preferable in some instances.
    • A combination of clinical data and coded discharge diagnoses is optimal in other cases.

    Conclusions:

    • The ClinQuery program facilitates the exploration and analysis of clinical databases.
    • Clinical data, either alone or combined with coded diagnoses, is essential for robust health services research.
    • Leveraging existing clinical databases is a viable and powerful research strategy.