Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools' respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools' respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (RAUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P's RAUC: 90.0%, 44.2% and 75.0% for RAUC ≤ 0.5; RAUC 0.5-2 and RAUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the RAUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

Medication Review: Human Factors Study Aiming at Helping an Acute Geriatric Unit to Sustain and Systematize the Process.

BACKGROUND: Medication Review (MRev) has been implemented in many hospitals to improve patient safety and well-being. However, it seems sometimes difficult to implement, maintain and systematize this process, especially when key-elements are absent. This study focuses on the analysis of a MRev process implemented in an Acute Geriatric Unit (AGU) which, at the time of the study, had no Computerized Physician Order Entry (CPOE) and no sufficient staff to - normally - support the process. OBJECTIVE: This study describes the MRev process as existing in the AGU with a particular focus on the preparatory MRev meeting phase and presents our recommendations to maintain and optimize it. METHODS: Human Factor experts have collected and analyzed data during MRev process by interviews, shadowing observations and video recording from April to October 2014 at Lille University Hospital. RESULTS: MRev process consists of three phases (meeting preparation, MRev meeting and patient discharge) and includes seven main tasks for which actors, documented supports, outcomes and difficulties are identified. Although allocating a fulltime pharmacist for the AGU would solve several problems, the main realistic recommendations concern training for junior and senior actors according to their roles and the improvement of some tasks processes. CONCLUSION: Despite less than optimal conditions as compared to those recommended by the literature, the observed AGU performs an efficient review based on well designed tools and processes.