Pharmaceutical algorithms set in a real time clinical decision support targeting high-alert medications applied to pharmaceutical analysis

Background: Pharmaceutical analysis of the prescription has to prop up the quality of patients' medication management in a context of medication's risk acculturation. But this activity remains highly variable. Medication-related clinical decision support may succeed in reducing adverse drug events and healthcare costs. Purpose: This study aims to present AVICENNE as a real time medication-related clinical decision support (rt-CDS) applied to pharmaceutical analysis and its ability to detect Drug related problems (DRP) consecutively resolved by pharmacists. Basic procedures A Medication-related rt-CDS is created by integrating the software PharmaClass (R) (Keenturtle), 5 health data streams on the patient and Pharmaceutical algorithms (PA). PA are created by modeling the pharmaceutical experiment about DRP and the thread of their criticality. They are partially encoded as computerized rules in Pharmaclass (R) allowing alerts' issue. An observational prospective study is conducted during 9-months among 1000 beds in 2 health facilities. The first step is to identify alerts as DRP; their resolution follows with clear guidelines worked out for the phar-maceutical analysis.A basis on predictive positive values (PPV) of the PA is being built today helping to know the performance of DRP detection and resolution. Main findings 71 PA are encoded as rules into Pharmaclass (R): 40 targeted serious adverse drug events. 1508 alerts are analyzed by pharmacists. Among them 921 DRPs were characterized and 540 pharmaceutical interventions transmitted of which 219 were accepted by prescribers. Three PPV are defined depending on software, pharmacist and patient. Principal conclusion Clinical pharmacy societies should host, share and update a national corpus of PA and exploit its educational interest.