Arden Syntax Clinical Foundation Framework for Event Monitoring in Intensive Care Units: Report on a Pilot Study.

The creation of clinical decision support systems has received a strong impulse over the last years, but their integration into a clinical routine has lagged behind, partly due to a lack of interoperability and trust by physicians. We report on the implementation of a clinical foundation framework in Arden Syntax, comprising knowledge units for (a) preprocessing raw clinical data, (b) the determination of single clinical concepts, and (c) more complex medical knowledge, which can be modeled through the composition and configuration of knowledge units in this framework. Thus, it can be tailored to clinical institutions or patients' caregivers. In the present version, we integrated knowledge units for several infection-related clinical concepts into the framework and developed a clinical event monitoring system over the framework that employs three different scenarios for monitoring clinical signs of bloodstream infection. The clinical event monitoring system was tested using data from intensive care units at Vienna General Hospital, Austria.

Context-Sensitive Clinical Alert Packages Written in Arden Syntax.

An increasing body of raw patient data is generated on each day of a patient's stay at a hospital. It is of paramount importance that critical patient information be extracted from these large data volumes and presented to the patient's clinical caregivers as early as possible. Contemporary clinical alert systems attempt to provide this service with moderate success. The efficacy of the systems is limited by the fact that they are too general to fit specific patient populations or healthcare institutions. In this study we present an extendable alerting framework implemented in Arden Syntax, which can be configured to the needs and preferences of healthcare institutions and individual patient caregivers. We illustrate the potential of this alerting framework via an alert package that analyzes hematological laboratory results with data from intensive care units at the Vienna General Hospital, Austria. The results show the effectiveness of this alert package and its ability to generate key alerts while avoiding over-alerting.