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Related Experiment Videos

Representing clinical guidelines in GLIF: individual and collaborative expertise

V L Patel1, V G Allen, J F Arocha

  • 1Centre for Medical Education, McGill University, Montreal, PQ, Canada. patel@hebb.psychl.mcgill.ca.

Journal of the American Medical Informatics Association : JAMIA
|October 7, 1998
PubMed
Summary

Translating clinical guidelines into encoded formats requires understanding cognitive processes. Collaborative teams of physicians and computer scientists create more accurate and efficient shared representations than individuals working alone.

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Area of Science:

  • Cognitive Science
  • Medical Informatics
  • Health Services Research

Background:

  • Clinical guidelines are essential for standardizing patient care.
  • Sharing guidelines across institutions requires standardized, encoded formats.
  • Understanding the cognitive processes involved in guideline encoding is crucial for effective implementation.

Purpose of the Study:

  • To evaluate the cognitive processes used when translating clinical guidelines into encoded formats for inter-institutional sharing.
  • To compare the representations generated by individuals with different expertise (clinical vs. computer science) and collaborative teams.

Main Methods:

  • A comparative study was conducted at three sites.
  • The GuideLine Interchange Format (GLIF) was used for encoding.

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  • Cognitive science theories and methods were applied to analyze the generation of GLIF representations.
  • Process-outcome measures compared subjects with varying expertise and institutional backgrounds.

    • Main Results:

    • Representation strategies varied based on prior experience and domain knowledge.
    • Physicians' representations included implicit pathophysiological knowledge, differing from literal guideline text.
    • Computer scientists' representations were more literal, adhering to GLIF logic.
    • Collaborative efforts integrated medical and guideline structure knowledge, yielding superior, consistent representations.

    Conclusions:

    • Variability in guideline representation necessitates understanding generation processes for efficient, accurate shared formats.
    • Teams comprising both clinicians and computer-based representation experts produce preferable encoded guidelines over individual efforts.