Can the integration of new rules into a clinical decision support system reduce the incidence of acute kidney injury and hyperkalemia among hospitalized older adults: a protocol for a stepped-wedge, cluster-randomized trial (DETECT-IP).

Background: Clinical decision support systems (CDSSs) enable the automated, real-time detection of situations associated with a risk of adverse drug events (ADEs). However, the effectiveness of CDSS in reducing ADEs has yet to be demonstrated. We have chosen to focus on the detection of ADE such as hyperkalemia and/or acute kidney injury (AKI), which are common among hospitalized older adults.

Translation of the REMEDI[e]S (Review of potentially inappropriate MEDIcation pr[e]scribing in Seniors) explicit criteria into seminatural language for use in prescription support systems: A multidisciplinary consensus.

Background: By recovering data in an ordered manner and at the right time, clinical decision support systems (CDSSs) are designed to help healthcare professionals make decisions that improve patient care.

Objectives: The aim of the present study was to translate the REMEDI[e]s tool's explicit criteria, France's first reference list of potentially inappropriate drugs for the elderly, into seminatural language, in order to implement these criteria as alert rules and then enable their computer coding in a CDSS.

Methods: This work was carried out at Lille University Hospital by a team of clinical pharmacists with expertise in the use of pharmaceutical decision support systems, in collaboration with the authors of the REMEDI[e]s tool.

[Use of a pharmaceutical decision support system in the valuation of hospital stays: Evaluation through 3 examples in collaboration with the department of medical information].

Pharmacy decision support systems (PDSS) help clinical pharmacists to prevent and detect adverse drug events. The coding of hospital stays by the department of medical information (DMI) requires expertise, as it determines hospital revenues and the epidemiological data transmitted via the French national hospital database. The aim was to study the interest and feasibility of using a PDSS, in collaboration with the DMI, to help with the coding of hospital stays.

Pharmaceutical Decision Support System Using Machine Learning to Analyze and Limit Drug-Related Problems in Hospitals.

The health product circuit corresponds to the chain of steps that a medicine goes through in hospital, from prescription to administration. The safety and regulation of all the stages of this circuit are major issues to ensure the safety and protect the well-being of hospitalized patients. In this paper we present an automatic system for analyzing prescriptions using Artificial Intelligence (AI) and Machine Learning (ML), with the aim of ensuring patient safety by limiting the risk of prescription errors or drug iatrogeny.

Acute stroke in patients taking an oral anticoagulant: impact of clinical pharmacist's intervention on pharmacovigilance reporting.

Introduction: Ischemic or hemorrhagic stroke can occur to patients treated with oral anticoagulants (OAC), through lack of effectiveness or overdosing.

Objective: To evaluate the impact of clinical pharmacist's intervention on pharmacovigilance (PV) reporting for OAC-treated patients hospitalized for stroke.

Methods: Monocentric prospective study in which a clinical pharmacist's intervention was performed in a stroke unit, with a focus on patients treated by OAC prior admission.