Development and Evaluation of a Clinical Decision Support System to Improve Medication Safety

Clinical decision support systems (CDSSs) are a good strategy for preventing medication errors and reducing the incidence and severity of adverse drug events (ADEs). However, these systems are not very effective and are subject to multiple limitations that prevent their implementation in clinical practice.

The objective of this study was to evaluate the effectiveness of an advanced CDSS, HIGEA, which generates alerts based on predefined clinical rules to identify patients at risk of an ADE.

A multidisciplinary team defined the system and the clinical rules focusing on medication errors commonly encountered in clinical practice. Four intervention programs were defined: (1) dose adjustment in renal impairment; (2) adjustment of anticoagulation/antiplatelet therapy; (3) detection of biochemical/hematologic toxicities; and (4) therapeutic drug monitoring. We performed a 6-month observational prospective study to analyze the effectiveness of these clinical rules by calculating the positive predictive value (PPV).

The team defined 211 clinical rules. During the study period, HIGEA generated 1,086 alerts (8.9 alerts per working day), which were reviewed by pharmacists. Fifty-one percent (554/1,086) of alerts generated an intervention to prevent a possible ADE; of these, 66% (368/554) required a documented modification to therapy owing to a real prescription error intercepted. The intervention program that induced the highest number of modifications to therapy was the dose adjustment in renal impairment program (PPV = 0.51), followed by the adjustment of anticoagulation/antiplatelet therapy program (PPV = 0.24). The percentage of accepted interventions was similar in surgical units (68%), medical units (67%), and critical care units (63%).

Our study offers evidence that HIGEA is highly effective in preventing potential ADEs at the prescription stage.