Representing clinical guidelines in GLIF: individual and collaborative expertise.

OBJECTIVE

An evaluation of the cognitive processes used in the translation of a clinical guideline from text into an encoded form so that it can be shared among medical institutions.

DESIGN

A comparative study at three sites regarding the generation of individual and collaborative representations of a guideline for the management of encephalopathy using the GuideLine Interchange Format (GLIF) developed by members of the InterMed Collaboratory.

MEASUREMENTS

Using theories and methods of cognitive science, the study involves a detailed analysis of the cognitive processes used in generating representations in GLIF. The resulting process-outcome measures are used to compare subjects with various types of computer science or clinical expertise and from different institutions.

RESULTS

Consistent with prior studies of text comprehension and expertise, the variability in strategies was found to be dependent on the degree of prior experience and knowledge of the domain. Differing both in content and structure, the representations developed by physicians were found to have additional information and organization not explicitly stated in the guidelines, reflecting the physicians' understanding of the underlying pathophysiology. The computer scientists developed more literal representations of the guidelines; addition were mostly limited to specifications mandated by the logic of GLIF itself. Collaboration between physicians and computer scientists resulted in consistent representations that were more than the sum of the separate parts, in that both domain-specific knowledge of medicine and generic knowledge of guideline structure were seamlessly integrated.

CONCLUSION

Because of the variable construction of guideline representations, understanding the processes and limitations involved in their generation is important in developing strategies to construct shared representations that are both accurate and efficient. The encoded guidelines developed by teams that include both clinicians and experts in computer-based representations are preferable to those developed by individuals of either type working alone.