• Eva K Lee, Tsung-Lin Wu, Tal Senior, and James Jose.
• Center for Operations Research in Medicine and HealthCare, Atlanta, GA ; NSF I/UCRC Center for Health Organization Transformation, Atlanta, GA ; Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA.
• AMIA Annu Symp Proc. 2014 Jan 1; 2014: 845-54.

AbstractWith the adoption of electronic medical records (EMRs), drug safety alerts are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, even with proper tuning of the EMR alert parameters, the volume of unfiltered alerts can be overwhelming to users. In this paper, we design an adaptive decision support tool in which past cognitive overriding decisions of users are learned, adapted and used for filtering actions to be performed on current alerts. The filters are designed and learned based on a moving time window, number of alerts, overriding rates, and monthly overriding fluctuations. Using alerts from two separate years to derive filters and test performance, predictive accuracy rates of 91.3%-100% are achieved. The moving time window works better than a static training approach. It allows continuous learning and capturing of the most recent decision characteristics and seasonal variations in drug usage. The decision support system facilitates filtering of non-essential alerts and adaptively learns critical alerts and highlights them prominently to catch providers' attention. The tool can be plugged into an existing EMR system as an add-on, allowing real-time decision support to users without interfering with existing EMR functionalities. By automatically filtering the alerts, the decision support tool mitigates alert fatigue and allows users to focus resources on potentially vital alerts, thus reducing the occurrence of adverse drug events.

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