Lower alert rates by clustering of related drug interaction alerts.
We aimed to investigate to what extent clustering of related drug interaction alerts (drug-drug and drug-disease interaction alerts) would decrease the alert rate in clinical decision support systems (CDSSs).



We conducted a retrospective analysis of drug interaction alerts generated by CDSSs in community pharmacies. Frequently generated combinations of alerts were analyzed for associations in a 5% random data sample (dataset 1). Alert combinations with similar management recommendations were defined as clusters. The alert rate was assessed by simulating a CDSS generating 1 alert per cluster per patient instead of separate alerts. The simulation was performed in dataset 1 and replicated in another 5% data sample (dataset 2).



Data were extracted from the CDSSs of 123 community pharmacies. Dataset 1 consisted of 841 572 dispensed prescriptions and 298 261 drug interaction alerts. Dataset 2 was comparable. Twenty-two frequently occurring alert combinations were identified. Analysis of these associated alert combinations for similar management recommendations resulted in 3 clusters (related to renal function, electrolytes, diabetes, and cardiovascular diseases). Using the clusters in alert generation reduced the alert rate within these clusters by 53-70%. The overall number of drug interaction alerts was reduced by 11% in dataset 1 and by 12% in dataset 2. This corresponds to a decrease of 21 alerts per pharmacy per day.



Using clusters of drug interaction alerts with similar management recommendations in CDSSs can substantially decrease the overall alert rate. Further research is needed to establish the applicability of this concept in daily practice.
SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands m.heringa@sirstevenshof.nl.|Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands.|Health Base Foundation, Houten, the Netherlands.
Mette
M
Heringa
Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands.
Hidde
H
Siderius
SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands.|Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands.
Annemieke
A
Floor-Schreudering
Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, Nijmegen, the Netherlands.
Peter A G M
PA
de Smet
SIR Institute for Pharmacy Practice and Policy, Leiden, the Netherlands.|Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, the Netherlands.
Marcel L
ML
Bouvy
D016000:Cluster Analysis; D066264:Datasets as Topic; D004347:Drug Interactions; D006801:Humans; D007256:Information Systems; D050316:Medical Order Entry Systems; D010594:Pharmacies; D012189:Retrospective Studies; D012308:Risk Management
clinical decision support systems; clinical risk management; drug therapy alerts; drug-drug interactions; pharmacy information systems