title: Clinical decision support for drug related events: Moving towards better prevention.
abstract: Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.
authors.affiliation: Sandra L Kane-Gill, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, United States.
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Kane-Gill
authors.affiliation: Sandra L Kane-Gill, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, United States.
authors.forename: Archita
authors.initials: A
authors.lastname: Achanta
authors.affiliation: Sandra L Kane-Gill, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, United States.
authors.forename: John A
authors.initials: JA
authors.lastname: Kellum
authors.affiliation: Sandra L Kane-Gill, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, United States.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Handler
keywords: Adverse drug event; Clinical; Clinical decision support systems; Clinical pharmacy information systems; Critical care; Decision support systems; Drug-related side effects and adverse reactions; Intensive care units; Medication errors; Patient safety