Development of a computerised alert system, ADEAS, to identify patients at risk for an adverse drug event

• Published in: Quality & safety in health care Vol. 19; no. 6; p. e35
• Main Authors: Rommers, M K, Zegers, M H, De Clercq, P A, Bouvy, M L, de Meijer, P H E M, Teepe-Twiss, I M, Guchelaar, H-J
• Format: Journal Article
• Language: English
• Published: England 01-12-2010
• Subjects: Decision Support Systems, Clinical; Drug-Related Side Effects and Adverse Reactions - prevention & control; Hospital Information Systems; Humans; Medical Order Entry Systems; Netherlands; Risk Assessment; Netherlands
• ISSN: 1475-3901
• DOI: 10.1136/qshc.2009.033704

Adverse drug events (ADEs) are frequent and pose an important risk for patients treated with drugs. Fortunately, a substantial part of ADEs is preventable, and computerised physician order entry with a sophisticated clinical decision support system may be used to reach this goal.
To develop a new automated system that could improve the quality of medication surveillance. The system should focus on detecting patients at risk for an ADE by combining data from the hospital information system and computerised physician order entry (drug prescription data, drug-drug interaction alerts, clinical chemical laboratory parameters, demographic features), using clinical rules.
The clinical rules were formulated in a multidisciplinary team, based on seven risk categories. The new system was composed in a guideline-based decision support framework consisting of both a guideline development module and a decision support module. A total of 121 clinical rules were built into the system. Validation of the system and a proof of principle test were performed.
The adverse drug event alerting system (ADEAS) was developed and validated successfully. The proof of principle test showed that ADEAS has potential clinical usefulness. ADEAS generated alerts and detected additional potential risk situations, which were not generated by the conventional medication surveillance.
We developed a pharmacy decision support system ADEAS that focuses on the detection of situations prone to lead to an ADE and might help clinicians to take timely corrective interventions and thereby can prevent patient harm.