Mette Heringa1, Annet van der Heide2, Annemieke Floor-Schreudering3, Peter A G M De Smet4, Marcel L Bouvy5. 1. SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands; Health Base Foundation, Papiermolen 36, 3994 DK Houten, The Netherlands. Electronic address: m.heringa@sirstevenshof.nl. 2. Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands. Electronic address: a.vanderheide@students.uu.nl. 3. SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands. Electronic address: a.floor@sirstevenshof.nl. 4. Departments of Clinical Pharmacy and IQ Healthcare, University Medical Centre St Radboud, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands. Electronic address: pdesmet@knmp.nl. 5. SIR Institute for Pharmacy Practice and Policy, Theda Mansholtstraat 5b, 2331 JE Leiden, The Netherlands; Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands. Electronic address: m.l.bouvy@uu.nl.
Abstract
OBJECTIVE: Drug interaction alerts (drug-drug and drug-disease interaction alerts) for chronic medications substantially contribute to alert fatigue in primary care. The aim of this study was to determine which events require (re)assessment of a drug interaction and whether using these events as triggers in clinical decision support systems (CDSSs) would affect the alert rate. METHODS: Two random 5% data samples from the CDSSs of 123 community pharmacies were used: dataset 1 and 2. The top 10 of most frequent drug interaction alerts not involving laboratory values were selected. To reach consensus on events that should trigger alerts (e.g. first time dispensing, dose modification) for these drug interactions, a two-step consensus process was used. An expert panel of community pharmacists participated in an online survey and a subsequent consensus meeting. A CDSS with alerts based on the consensus was simulated in both datasets. RESULTS: Dataset 1 and 2 together contained 1,672,169 prescriptions which led to 591,073 alerts. Consensus on events requiring alerts was reached for the ten selected drug interactions. The simulation showed a reduction of the alert rate of 93.0% for the ten selected drug interactions (comparable for dataset 1 and 2), corresponding with a 28.3% decrease of the overall drug interaction alert rate. CONCLUSION: By consensus-based better specification of the events that trigger drug interaction alerts in primary care, the alert rate for these drug interactions was reduced by over 90%. This promising approach deserves further investigation to assess its consequences and applicability in daily practice.
OBJECTIVE: Drug interaction alerts (drug-drug and drug-disease interaction alerts) for chronic medications substantially contribute to alert fatigue in primary care. The aim of this study was to determine which events require (re)assessment of a drug interaction and whether using these events as triggers in clinical decision support systems (CDSSs) would affect the alert rate. METHODS: Two random 5% data samples from the CDSSs of 123 community pharmacies were used: dataset 1 and 2. The top 10 of most frequent drug interaction alerts not involving laboratory values were selected. To reach consensus on events that should trigger alerts (e.g. first time dispensing, dose modification) for these drug interactions, a two-step consensus process was used. An expert panel of community pharmacists participated in an online survey and a subsequent consensus meeting. A CDSS with alerts based on the consensus was simulated in both datasets. RESULTS: Dataset 1 and 2 together contained 1,672,169 prescriptions which led to 591,073 alerts. Consensus on events requiring alerts was reached for the ten selected drug interactions. The simulation showed a reduction of the alert rate of 93.0% for the ten selected drug interactions (comparable for dataset 1 and 2), corresponding with a 28.3% decrease of the overall drug interaction alert rate. CONCLUSION: By consensus-based better specification of the events that trigger drug interaction alerts in primary care, the alert rate for these drug interactions was reduced by over 90%. This promising approach deserves further investigation to assess its consequences and applicability in daily practice.
Authors: Helene G van der Meer; Hans Wouters; Martina Teichert; Fabiënne Griens; Jugoslav Pavlovic; Lisa G Pont; Katja Taxis Journal: Ther Adv Drug Saf Date: 2018-10-30
Authors: Arthur T M Wasylewicz; Britt W M van de Burgt; Thomas Manten; Marieke Kerskes; Wilma N Compagner; Erik H M Korsten; Toine C G Egberts; Rene J E Grouls Journal: Clin Pharmacol Ther Date: 2022-06-27 Impact factor: 6.903