INITIATE: An Intelligent Adaptive Alert Environment

Area of Science:

• Medical Informatics
• Human-Computer Interaction
• Clinical Decision Support

Background:

• Medical Alert Generating Systems (AGS) produce high alert volumes, leading to user overwhelm and alert fatigue.
• Alert fatigue results in critical alerts being ignored and increased response times, impacting patient safety.
• Current methods to mitigate alert fatigue focus on reducing irrelevant alert generation within AGS.

Purpose of the Study:

• To introduce INITIATE (an INtellIgent adapTIve AlerT Environment), a novel framework designed to combat alert fatigue.
• To manage alerts from single or multiple AGS by categorizing alert lifecycles and applying tailored management logic.
• To enhance user experience and system efficiency by providing adaptive and personalized alert management.

Main Methods:

• Developed a framework (INITIATE) that categorizes alert lifecycles and implements alert management logic.
• Incorporated an ontology to represent alert management strategies.
• Integrated an alert management engine to execute these strategies, featuring user feedback adaptability, message personalization/aggregation, and HL7 Clinical Document Architecture parsing for EMR integration.

Main Results:

• The INITIATE framework provides adaptive alert management based on user feedback.
• It enables personalization and aggregation of alert messages for better user comprehension.
• The framework facilitates seamless connection to Electronic Medical Records via HL7 CDA parsing.

Conclusions:

• INITIATE offers a comprehensive solution to mitigate alert fatigue in clinical settings.
• The framework's adaptability and integration capabilities enhance the effectiveness of AGS.
• By managing alert lifecycles and user preferences, INITIATE aims to improve clinical workflow and patient safety.