Implementation of a clinical decision support system for the optimization of antidiabetic drug orders by pharmacists.

AIMS: The objective of the study was to describe the impact of a clinical decision support system (CDSS) on antidiabetic drug management by clinical pharmacists for hospitalized patients with T2DM. METHODS: We performed a retrospective, single-centre study in a teaching hospital, where clinical pharmacists analysed prescriptions and issued pharmacist interventions (PIs) through a computerized physician order entry (CPOE) system. A CDSS was integrated into the pharmacists' workflow in July 2019. We analysed PIs during 2 periods of interest: one before the introduction of the CDSS (from November 2018 to April 2019, PIs issued through the CPOE alone) and one afterwards (from November 2020 to April 2021, PIs issued through the CPOE and/or the CDSS). The study covered nondiabetology wards as endocrinology, diabetes and metabolism departments were not computerized at the time of the study. RESULTS: There were 203 PIs related to antidiabetic drugs in period 1 and 319 in period 2 (a 57.5% increase). Sixty-four of the 319 PIs were generated by the CDSS. Noncompliance/contraindication was the main problem identified by the CDSS (41 PIs, 68.4%), and 57.8% led to discontinuation of the drug. Most of the PIs issued through the CDSS corresponded to orders that had not been flagged up by clinical pharmacists using the CPOE. Conversely, most alerts about indications that were not being treated were detected by the clinical pharmacists using the CPOE and not by the CDSS. CONCLUSION: Use of CDSS by clinical pharmacists improved antidiabetic drug management for hospitalized patients with T2DM. The CDSS might add value to diabetes care in nondiabetology wards by decreasing the frequency of potentially inappropriate prescriptions and adverse drug reactions.

[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]. / Utilisation d'un système d'aide à la décision pharmaceutique dans la valorisation des séjours hospitaliers : évaluation à travers 3 exemples en collaboration avec le département de l'information médicale.

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. Over 5 months, three rules were implemented in the PDSS to detect gout, Parkinson's disease and oro-pharyngeal candidiasis. The PDSS alerts were analyzed by a pharmacy resident and then forwarded to the DMI, who analyzed the stays to see whether or not the coding for the disease corresponding to the alert was present. The absence of coding was evaluated and tracked, along with the resulting change in severity and valuation. Three hundred and ninety-nine alerts from the PDSS were analyzed and sent to the DMI, representing 211 stays and 309 uniform hospital standardized discharge abstract (UHSDA) in the fields of medicine, surgery and obstetrics. Two hundred and eight (67.3%) UHSDA did not have the coding corresponding to the alert. For the majority of these UHSDAs, apart from diagnostic precision, there was no impact on the valuation of stays. For 4 UHSDAs, the addition of the diagnosis code led to an increase in the value of the stay and the severity of the homogeneous patient groups. The total revaluation corresponding to this modification was €5416. The use of PDSS has helped in the precision of diagnosis coding and the valuation of stays. This result must be weighed against the time invested in analyzing alerts and associated coding. An improvement in disease detection and data processing is needed to be feasible in practice, given the more than 227,600 RSS performed per year at our facility.

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. Our study is made in collaboration with Lille University Hospital (LUH). We exploited the MIMIC-III (Medical Information Mart for Intensive Care) a large, single-center database containing information corresponding to patients admitted to critical care units at a large tertiary care hospital.

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. A total of 3 multi-professional consensus meetings were required to discuss the construction of each rule in seminatural language and the coding choices. RESULTS: All REMEDIES criteria (n=104) were translated into seminatural language. This study is the first to have translated the 104 REMEDI[e]s explicit criteria into seminatural language. CONCLUSIONS: One of the study's strengths relates to the close collaboration between the authors of the REMEDI[e]s tool and experts in CDSS programming rules; this ensured the exactitude of the seminatural language translations and limited (mis)interpretations.

Detection of Drug-Related Problems through a Clinical Decision Support System Used by a Clinical Pharmacy Team.

Clinical decision support systems (CDSSs) are intended to detect drug-related problems in real time and might be of value in healthcare institutions with a clinical pharmacy team. The objective was to report the detection of drug-related problems through a CDSS used by an existing clinical pharmacy team over 22 months. It was a retrospective single-center study. A CDSS was integrated in the clinical pharmacy team in July 2019. The investigating clinical pharmacists evaluated the pharmaceutical relevance and physician acceptance rates for critical alerts (i.e., alerts for drug-related problems arising during on-call periods) and noncritical alerts (i.e., prevention alerts arising during the pharmacist's normal work day) from the CDSS. Of the 3612 alerts triggered, 1554 (43.0%) were critical, and 594 of these 1554 (38.2%) prompted a pharmacist intervention. Of the 2058 (57.0%) noncritical alerts, 475 of these 2058 (23.1%) prompted a pharmacist intervention. About two-thirds of the total pharmacist interventions (PI) were accepted by physicians; the proportion was 71.2% for critical alerts (i.e., 19 critical alerts per month vs. 12.5 noncritical alerts per month). Some alerts were pharmaceutically irrelevant-mainly due to poor performance by the CDSS. Our results suggest that a CDSS is a useful decision-support tool for a hospital pharmacist's clinical practice. It can help to prioritize drug-related problems by distinguishing critical and noncritical alerts. However, building an appropriate organizational structure around the CDSS is important for correct operation.

Improving the usability and usefulness of computerized decision support systems for medication review by clinical pharmacists: A convergent, parallel evaluation.

BACKGROUND: Computerized decision support systems (CDSSs) help hospital-based clinical pharmacists to perform medication reviews and so are promising tools for improving medication safety. However, their poor usability can reduce effectiveness and acceptability. OBJECTIVES: To evaluate the usability and perceived usefulness of a CDSS for medication review by hospital-based pharmacists and to draw up guidelines on improving its usability. METHODS: We performed a convergent, parallel evaluation. Firstly, three researchers conducted a heuristic evaluation of the CDSS. Secondly, clinical pharmacists who use the CDSS filled out the Usefulness, Satisfaction and Ease of Use (USE) questionnaire. Lastly, semi-structured interviews with the pharmacists enabled us to understand their opinions and experiences. The results of the heuristic evaluation were used to identify potential improvements in the CDSS. We performed a statistical analysis of the USE questionnaire data. Interviews were analyzed based on the unified theory of acceptance and use of technology (UTAUT), together with a task-technology fit model. The results generated by these three approaches were compared in order to determine convergences and divergences, identify challenges related to the usability and usefulness of the CDSS, and draw up guidelines for its improvement. RESULTS: Forty-seven usability problems were discovered; they variously concerned the graphical user interface, the pharmacists' needs, and the medication review model implemented in the CDSS. Only the "usefulness" dimension of the USE was not scored positively. All the UTAUT dimensions and the task-technology fit dimension emerged in the interviews. Cross-comparisons of the results from the three approaches led to the identification of four challenges and the corresponding formulation of 23 guidelines. CONCLUSIONS: The guidelines developed here should help to improve the design and acceptability of CDSSs. Hence, CDSSs will be able to assist clinical pharmacists more fully with their medication reviews and help to further improve patient safety.

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. A PV report was made with all data collected for cases of stroke suspected to be related to OAC therapy. Data provided by pharmacist were compared with data initially available in the patient's electronic medical records. PV reports with pharmacist intervention were compared to those without. RESULTS: During the study period, 48 patients were included in the study: 43 (89.6%) ischemic strokes with an embolic or unknown etiology, four hemorrhage strokes (8.33%), and one medication error (2.08%). A clinical pharmacist intervention was performed for 19 patients (39.6%) and provided significant additional data in all of them (100%). The information was related to adherence to treatment for 17 cases (89.5%), OAC's initial prescription date for 11 cases (57.9%) and identifying event(s) that could have interfered with the efficacy of the OAC in five cases (26.3%). For patients with pharmacist intervention, PV reports were significantly more informative in terms of date's introduction of anticoagulant, adherence to treatment, reference to weight change or concomitant event. CONCLUSIONS: clinical pharmacist's intervention with patients taking oral anticoagulants and hospitalized for acute stroke contributes to collect high-quality data for pharmacovigilance reporting.

Integration of Explicit Criteria in a Clinical Decision Support System Through Evaluation of Acute Kidney Injury Events.

In Clinical Decision Support System (CDSS), relevance of alerts is essential to limit alert fatigue and risk of overriding relevant alerts by health professionals. Detection of acute kidney injury (AKI) situations is of great importance in clinical practice and could improve quality of care. Nevertheless, to our knowledge, no explicit rule has been created to detect AKI situations in CDSS. The objective of the study was to implement an AKI detection rule based on KDIGO criteria in a CDSS and to optimize this rule to increase its relevance in clinical pharmacy use. Two explicit rules were implemented in a CDSS (basic AKI rule and improved AKI rule), based on KDIGO criteria. Only the improved rule was optimized by a group of experts during the two-month study period. The CDSS provided 1,125 alerts on AKI situations (i.e. 643 were triggered for the basic AKI rule and 482 for the improved AKI rule). As the study proceeds, the pharmaceutically and medically relevance of alerts from the improved AKI rule increased. A ten-fold increase was shown for the improved AKI rule compared to the basic AKI rule. The study highlights the usefulness of a multidisciplinary review to enhance explicit rules integrated in CDSS. The improved AKI is able to detect AKI situations and can improve workflow of health professionals.

Context-Sensitive Implementation of Clinical Pharmacy Missions Within an Academic Hospital.

Clinical pharmacy activities contribute to improve patient safety. Yet, the work system's characteristics influence how clinical pharmacy activities are performed and conversely clinical pharmacy causes that work system to evolve. This exploratory study aims to identify the different ways in which clinical pharmacy activities are performed in different units of a large academic hospital. Interviews and observations have been performed to identify in each ward the clinical pharmacy activities implemented and how they are carried out.

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.

Defining Potentially Inappropriate Prescriptions for Hypoglycaemic Agents to Improve Computerised Decision Support: A Study Protocol.

In France, around 5% of the general population are taking drug treatments for diabetes mellitus (mainly type 2 diabetes mellitus, T2DM). Although the management of T2DM has become more complex, most of these patients are managed by their general practitioner and not a diabetologist for their antidiabetics treatments; this increases the risk of potentially inappropriate prescriptions (PIPs) of hypoglycaemic agents (HAs). Inappropriate prescribing can be assessed by approaches that are implicit (expert judgement based) or explicit (criterion based). In a mixed, multistep process, we first systematically reviewed the published definitions of PIPs for HAs in patients with T2DM. The results will be used to create the first list of explicit definitions. Next, we will complete the definitions identified in the systematic review by conducting a qualitative study with two focus groups of experts in the prescription of HAs. Lastly, a Delphi survey will then be used to build consensus among participants; the results will be validated in consensus meetings. We developed a method for determining explicit definitions of PIPs for HAs in patients with T2DM. The resulting explicit definitions could be easily integrated into computerised decision support tools for the automated detection of PIPs.