لخّصلي

In France, 4.5% of hospitalizations are caused by healthcare-related adverse events, and 1.6% by preventable medication
adverse events. In addition, 20% of serious adverse events
during hospitalization are related to medications errors for
which 40% of those errors are preventable [1]. Medica-tion errors or drug related problems (DRP), defned as any preventable event that may lead to an inappropriate medica-tion use or patient harm, still remain a clinical and economic
burden.
The annual cost of non-optimized medication ther-apy resulting in treatment failure or a new medical problem was estimated to reach between $495 and $672 billion US dollars, representing 16% of total US health care expenses in
2016 [2]. Secondary care (hospital and specialists) adverse
drug events (ADE) were found to lead to longer hospital
stay, costing £14.8 million sterling pounds ($19.1 million US
dollars) and causing or contributing to 1081 deaths annually
in England [3].
In hospital setting, medication orders and administrations
were found to be the most common steps responsible for
preventable adverse events. McCarter et al. ofered solu-
tions to reduce medication errors by improving education,
implementing new safety systems and technology, and per-
form clinical pharmacist interventions [4]. In this respect,
computerized physician orders and medication reviews by
pharmacists proved to be key elements in patients’ hos-
pital care and have shown to be efective and essential to
reduce medication errors [5, 6].
In France, the exhaustive
medication review of all hospitalized patients’ medication
orders is recommended and more and more encouraged as
a quality and safety requirement. Every hospital is liable
to provide a sufcient quality management of patient care
including medication review of orders, and the number of
medication review achieved is a criterion among others to
evaluate the quality and safety of hospital care [7].
Unfor-tunately, exhaustive medication review by pharmacists for all hospitalized patients remains a challenging goal to reach in French acute care hospitals. The high frequency of
medication orders changes and the lack of time or trained
pharmacists dedicated to this activity are the most common
obstacles.
One way of improving the efciency of clinical pharma-cists is to specifcally target and review prescriptions with a potential risk of serious medication error and rule out prescriptions with no or low risk of error. To improve this
approach, a study recently identifed variables (age, renal
function, number of prescribed drugs) which could help pri-oritize prescriptions in need of a pharmacist-led medication review [8].
With this in mind, a digital tool was recently developed,
combining machine learning with Artifcial Intelligence (AI)
and rule-based expert system to help prioritize medication
reviews [9]. This hybrid model using knowledge-driven
(expert system) and date-driven approaches (machine learn-ing) has shown to be an accurate tool at intercepting poten-tial prescription errors leading to DRPs. The accuracy of this hybrid decision support algorithm showed a sensitivity
of 0.81 (95% CI, 0.78–0.84) and a precision 0.75 (95% CI,
0.70–0.80), that outperformed classic prescription order
analysis tools [9]. Currently used in practice, this tool helps
pharmacists to prioritize their medication review activity by
distinguishing low and high risk prescriptions. Nevertheless,
it seems essential to be able to test the efectiveness of the
tool in real life conditions.