Guidelines and Workflow Models

Implementing clinical guidelines as decision-support systems that provide patient-specific recommendations during clinical encounters increases the chances of affecting clinicians' behavior and achieving the benefits of guidelines. The road to achieving widespread use of such clinical decision support (CDS) systems is long and difficult. Along this road, there are successes and failures, and many future directions that can be taken to reach this goal. This chapter reviews the current state of the art in guideline-based decision support research and considers the likely future directions that can be taken to reach the ultimate goal. Clinical guidelines aim to improve quality of care, decrease unjustified practice variations, and save costs. For guidelines to affect clinicians' behavior, they should provide patient-specific decision support during patient encounters. Specifying guidelines in computer-interpretable guideline (CIG) formalisms, which could provide automatic inference based on patient data, may achieve this goal. Several methodologies have been developed to support the transition from narrative guidelines into CIG implementations. They include methodologies for marking up narrative guideline elements to assess a guideline's quality and completeness, and map it to CIG formalisms. Many CIG formalisms exist, differing in their goals, computation model, the elements used to structure guideline knowledge, and the degree to which they support workflow integration.