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Improving Appropriate Prescribing For Geriatric Patients Using a Clinical Decision Support System
Improving Appropriate Prescribing For Geriatric Patients Using a Clinical Decision Support System
Auteurs
Floor van den Hanenberg, Valentina D Poetsema, Carolina JPW Keijsers, Jeroen JMA Hendrikx, Jos van Campen, Michiel C Meulendijk, Jelle Tichelaar, Michiel A. van Agtmael
ABSTRACT
Purpose: Polypharmacy is a known risk factor for potentially inappropriate prescribing. Recently there is an increasing interest in clinical decision support systems (CDSS) to improve prescribing. The objective of this study was to evaluate the impact of a CDSS, with the START-STOPP criteria as main content in the setting of a geriatric ward. Endpoints were 1) appropriateness of prescribing and 2) acceptance rate of recommendations.
Methods: This prospective study comparing the use of a CDSS with usual care involved patients admitted to geriatric wards in two teaching hospitals in the Netherlands. Patients were included from January to May 2017. The medications of 64 patients in the first six weeks was assessed according to the current standard, whereas the medications of 61 patients in the second six weeks were also assessed by using a CDSS. Medication appropriateness was assessed with the Medication Appropriateness Index (MAI).
Results: The medications of 125 patients (median age 83 years) were reviewed. In both the usual care group and the intervention group MAI scores decreased significantly from admission to discharge (within group analyses, p<0.001). This effect was significantly larger in the intervention group (p<0.05). MAI scores at discharge in the usual care group and the intervention group were respectively 9.95±6.70 and 7.26±5.07. The CDSS generated 193 recommendations, of which 71 concerned START criteria, 45 STOPP criteria, and 77 potential interactions. Overall, 31.6% of the recommendations were accepted.
Conclusion: This study shows that a CDSS to improve prescribing has additional value in the setting of a geriatric ward. Almost one third of the software-generated recommendations were interpreted as clinically relevant and accepted, on average one per patient.