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INITIATE: An Intelligent Adaptive Alert Environment

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Exposure to a large volume of alerts generated by medical Alert Generating Systems (AGS) such as drug-drug interaction softwares or clinical decision support systems over-whelms users and causes alert fatigue in them. Some of alert fatigue effects are ignoring crucial alerts and longer response times. A common approach to avoid alert fatigue is to devise mechanisms in AGS to stop them from generating alerts that are deemed irrelevant. In this paper, we present a novel framework called INITIATE: an INtellIgent adapTIve AlerT Environment to avoid alert fatigue by managing alerts generated by one or more AGS. We have identified and categories the lifecycle of different alerts and have developed alert management logic as per the alerts' lifecycle. Our framework incorporates an ontology that represents the alert management strategy and an alert management engine that executes this strategy. Our alert management framework offers the following features: (1) Adaptability based on users' feedback; (2) Personalization and aggregation of messages; and (3) Connection to Electronic Medical Records by implementing a HL7 Clinical Document Architecture parser.
Title: INITIATE: An Intelligent Adaptive Alert Environment
Description:
Exposure to a large volume of alerts generated by medical Alert Generating Systems (AGS) such as drug-drug interaction softwares or clinical decision support systems over-whelms users and causes alert fatigue in them.
Some of alert fatigue effects are ignoring crucial alerts and longer response times.
A common approach to avoid alert fatigue is to devise mechanisms in AGS to stop them from generating alerts that are deemed irrelevant.
In this paper, we present a novel framework called INITIATE: an INtellIgent adapTIve AlerT Environment to avoid alert fatigue by managing alerts generated by one or more AGS.
We have identified and categories the lifecycle of different alerts and have developed alert management logic as per the alerts' lifecycle.
Our framework incorporates an ontology that represents the alert management strategy and an alert management engine that executes this strategy.
Our alert management framework offers the following features: (1) Adaptability based on users' feedback; (2) Personalization and aggregation of messages; and (3) Connection to Electronic Medical Records by implementing a HL7 Clinical Document Architecture parser.

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