Towards a realistic clinical-guidelines application framework: desiderata, applications, and lessons learned

Authors:
Erez Shalom;Iliya Fridman;Yuval Shahar;Avner Hatsek;Eitan Lunenfeld
Affiliations:
Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel;Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel;Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel;Medical Informatics Research Center, Department of Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva, Israel;Soroka Medical Center, Department of OB/GYN, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer-Sheva, Israel
Venue:
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
Year:
2012

Citing 12
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Abstract

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 machine-comprehensible 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.