Pieter Cornu1, Stephane Steurbaut2, Kristof Gentens3, Rudi Van de Velde3, Alain G Dupont2. 1. Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium. Electronic address: Pieter.Cornu@vub.ac.be. 2. Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium. 3. Department of Medical Informatics, UZ Brussel, 1090 Brussels, Belgium.
Abstract
OBJECTIVES: Clinical decision support (CDS) systems are frequently used to reduce unwanted drug-drug interactions (DDIs) but often result in alert fatigue. The main objective of this study was to investigate whether a newly developed context-specific DDI alerting system would improve alert acceptance. METHODS: A controlled pre-post intervention study was conducted in 4 departments in a university hospital. After a 7-month pre-intervention period, the new system was activated in the intervention departments, while the old system remained activated in the control departments. Post-intervention data was collected for a 7-month period. RESULTS: A significant increase of the overall acceptance rate was observed between the pre- and post-intervention period (2.2% versus 52.4%; p<0.001) for the intervention departments and between the intervention and control departments (2.5% versus 52.4%; p<0.001) in the post-intervention period. There were no significant differences in acceptance rates between the pre- and post-intervention period in the control departments and also not between the control and intervention departments in the pre-intervention period. CONCLUSIONS: The improvement was probably related to several optimization strategies including the customization of the severity classification, the creation of individual screening intervals, the inclusion of context factors for risk assessment, the new alert design and the creation of a follow-up system. The marked increase in alert acceptance looks promising and should be further evaluated after hospital wide implementation. System aspects that require further optimization were identified and will be developed. Further research is warranted to develop context-aware algorithms for complex class-class interactions.
OBJECTIVES: Clinical decision support (CDS) systems are frequently used to reduce unwanted drug-drug interactions (DDIs) but often result in alert fatigue. The main objective of this study was to investigate whether a newly developed context-specific DDI alerting system would improve alert acceptance. METHODS: A controlled pre-post intervention study was conducted in 4 departments in a university hospital. After a 7-month pre-intervention period, the new system was activated in the intervention departments, while the old system remained activated in the control departments. Post-intervention data was collected for a 7-month period. RESULTS: A significant increase of the overall acceptance rate was observed between the pre- and post-intervention period (2.2% versus 52.4%; p<0.001) for the intervention departments and between the intervention and control departments (2.5% versus 52.4%; p<0.001) in the post-intervention period. There were no significant differences in acceptance rates between the pre- and post-intervention period in the control departments and also not between the control and intervention departments in the pre-intervention period. CONCLUSIONS: The improvement was probably related to several optimization strategies including the customization of the severity classification, the creation of individual screening intervals, the inclusion of context factors for risk assessment, the new alert design and the creation of a follow-up system. The marked increase in alert acceptance looks promising and should be further evaluated after hospital wide implementation. System aspects that require further optimization were identified and will be developed. Further research is warranted to develop context-aware algorithms for complex class-class interactions.
Authors: Laura Légat; Sven Van Laere; Marc Nyssen; Stephane Steurbaut; Alain G Dupont; Pieter Cornu Journal: J Med Internet Res Date: 2018-09-07 Impact factor: 5.428