R N Shiffman1. 1. Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA. richard-shiffman@qm.yale.edu
Abstract
OBJECTIVE: To develop a knowledge representation model for clinical practice guidelines that is linguistically adequate, comprehensible, reusable, and maintainable. DESIGN: 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. CONCLUSION: Augmented decision tables can serve as a unifying knowledge representation for developers and implementers of clinical practice guidelines.
OBJECTIVE: To develop a knowledge representation model for clinical practice guidelines that is linguistically adequate, comprehensible, reusable, and maintainable. DESIGN: 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. CONCLUSION: Augmented decision tables can serve as a unifying knowledge representation for developers and implementers of clinical practice guidelines.
Authors: Richard N Shiffman; George Michel; Abdelwaheb Essaihi; Elizabeth Thornquist Journal: J Am Med Inform Assoc Date: 2004-06-07 Impact factor: 4.497