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.

Keywords: Drug-related side effects and adverse reactions, Decision support systems, Clinical, Medication errors, Patient safety, Clinical pharmacy information systems, Intensive care units, Critical care, Adverse drug event, Clinical decision support systems

Core tip: Drug related events in the intensive care unit are associated with higher medical costs and dire patient outcomes. Clinical decision support (CDS) systems are the most important component to aid in adverse drug event (ADE) surveillance and improve in medication safety. Institutions are increasing the use of CDS systems for event detection and CDS systems that combine patient factors with laboratory values, drug information and biomarkers are key to effective ADE prevention.