Representation of clinical practice guidelines in conventional and augmented decision tables.
OBJECTIVE
To develop a knowledge representation model for clinical practice guidelines that is linguistically adequate, comprehensible, reusable, and maintainable.


METHODS
Decision tables provide the basic framework for the proposed knowledge representation model. Guideline logic is represented as rules in conventional decision tables. These tables are augmented by layers where collateral information is recorded in slots beneath the logic.


RESULTS
Decision tables organize rules into cohesive rule sets wherein complex logic is clarified. Decision table rule sets may be verified to assure completeness and consistency. Optimization and display of rule sets as sequential decision trees may enhance the comprehensibility of the logic. The modularity of the rule formats may facilitate maintenance. The augmentation layers provide links to descriptive language, information sources, decision variable characteristics, costs and expected values of policies, and evidence sources and quality.


CONCLUSIONS
Augmented decision tables can serve as a unifying knowledge representation for developers and implementers of clinical practice guidelines.
Shiffman
R N
RN
Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA. richard-shiffman@qm.yale.edu
D003661:Decision Support Techniques; D003663:Decision Trees; D006801:Humans; D008128:Logic; D008962:Models, Theoretical; D017410:Practice Guidelines as Topic