יובל שחר

Clinicians can benefit from automated support to guideline (GL) application at the point of care. However, several conceptual dimensions should be considered for a realistic application: 1) The representation of the knowledge might be through structured text (semi-formal), or specified in a machinecomprehensible language (formal); 2) The availability of electronic patient data might be partial or full; 3) GL-based recommendations might be triggered by a human-initiated (synchronous) session, or data-driven (asynchronous). In addition, several requirements must be fulfilled, such as an evaluation of the GL application engine by a GL simulation engine. Finally, to apply multiple GLs, by multiple users, in multiple settings, the GL-application engine should be designed as an enterprise architecture that can plug into any Electronic Medical Record (EMR). We present an architecture fulfilling these desiderata, describe application examples with different conceptual dimensions and requirements, using our new GL-application engine, PICARD, discuss lessons learned, and briefly describe a clinical evaluation of the current framework in the domain of pre-eclampsia/toxemia of pregnancy.