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[Continuous improvement in anesthesiological quality documentation]

A Junger1, C Veit, T Klöss

  • 1Abteilung für Anaesthesiologie und operative Intensivmedizin, Allgemeines Krankenhaus Harburg.

Anasthesiologie, Intensivmedizin, Notfallmedizin, Schmerztherapie : AINS
|December 23, 1998
PubMed
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The American Society of Anesthesiologists (ASA) classification effectively identifies patient risk groups in anesthesia quality benchmarking. Adding specific risk factors provides minimal additional information, suggesting a streamlined dataset is possible.

Area of Science:

  • Anesthesiology
  • Quality Improvement
  • Health Informatics

Context:

  • Quality benchmarking projects in anesthesia often rely on the American Society of Anesthesiologists (ASA) classification to distinguish patient risk groups.
  • The DGAI core dataset has been used since 1992 to document anesthesias in Hamburg hospitals, enabling large-scale data analysis.

Purpose:

  • To critically examine the parallel description of patient risk using both ASA classification and specific risk parameters.
  • To test the hypothesis that documenting both parameter groups in quality benchmarking yields no significant information gain.
  • To explore the possibility of reducing the core dataset and documentation workload.

Summary:

  • A study analyzed 257,878 elective anesthesias, comparing the predictive power of ASA classification with specific risk assessments for adverse events (grade 3-5).

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  • Results indicated that, with one exception, no special risk assessment demonstrated superior predictive power for adverse event incidence compared to ASA classification.
  • This finding held true even for high-lethality adverse events like decompensated cardiac insufficiency, myocardial infarction, pulmonary embolism, and cardiac arrest.

    • Impact:

    • Abandoning the documentation of specific risk factors as predictors in the core dataset may be feasible without substantial information loss.
    • Reducing data in quality assurance to a core set of meaningful parameters can increase method acceptance and result validity.
    • Streamlining data collection can significantly reduce the documentation workload for healthcare professionals.