Clinical risk assessment of modelled situations in a pharmaceutical decision support system: a modified e-Delphi exploratory study.

BACKGROUND: Pharmaceutical decision support systems (PDSSs) use reasoning software to match patient data to modelled situations likely to cause drug-related problems (DRPs) or adverse drug events. To aid decision-making, modelled situations must be linked to well-defined systemic clinical risks. AIM: To obtain expert consensus on the level of clinical risk for patients associated with each modelled situation that could be addressed using a PDSS. METHOD: A two-round e-Delphi survey was conducted from February to April 2022, involving 20 experts from four French-speaking countries. Participants had to rate modelled situations on two five-point Likert scales, assessing the likelihood of clinical consequences and their severity. The degree of consensus was determined as the proportion of participants providing risk scores in line with the median. The combined median scores for likelihood and severity provided the level of risk according to the Clinical Risk Situation for Patients (CRiSP) scale, formalized via validated tools. RESULTS: The expert panel achieved consensus (≥ 75% agreement) on 48 out of 52 modelled clinical situations. Among these, 45 were categorized as high or extreme risk. The most common DRP identified was overdosing, accounting for 22% of cases. Furthermore, DRPs involving cardiovascular, psychiatric, and endocrinological drug classes were prevalent, constituting 45, 13, and 9% of cases, respectively. CONCLUSION: Through consensus, our study identified 45 modelled clinical situations associated with high or extreme risks. This study highlights the interest of using PDSSs to prevent harm in patients and, on a large scale, document the impact of the pharmacist in preventing, intercepting and managing iatrogenic drug risk.

Enhancing pharmaceutical decision support system: evaluating antithrombotic-focused algorithms for addressing drug-related problems.

OBJECTIVES: To evaluate the efficacy of integrating antithrombotic-focused pharmaceutical algorithms (PAs) into a pharmaceutical decision support system (PDSS) for detecting drug-related problems (DRPs) and facilitating pharmaceutical interventions. METHODS: A set of 26 PAs (12.4%) out of a total of 210 were created to model patient situations involving antithrombotics, and their contributions were compared with the entire PDSS system.The observational prospective study was conducted between November 2019 and June 2023 in two health facilities with 1700 beds. Pharmacists, who followed a DRP resolution strategy to support human supervision, analysed alerts generated by these encoded PAs. They registered their interventions and the acceptance by physicians. RESULTS: From 3290 alerts analysed targeting antithrombotics, the pharmacists issued 1170 interventions of which 676 (57.8%) were accepted by physicians. With the 184 other PAs, from 9484 alerts the pharmacists issued 3341 interventions of which 1785 were accepted (53.4%).Results indicate that the detection of DRPs related to antithrombotics usage represents a high proportion of those detected by the PDSS, highlighting the importance of incorporating tailored PA elements at the modelling stage. CONCLUSIONS: The system evolves alongside the physiological changes associated to the patient situations, adapts the alerts and complements the current care. Therefore, we recommend that all PDSS should integrate specific algorithms targeting DRPs associated with antithrombotics to enhance pharmaceutical interventions and improve patient safety.

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® (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® 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 pharmaceutical 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®: 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.

The clinical impact of medication reconciliation on admission to a French hospital: a prospective observational study.

OBJECTIVE: This study was designed to assess the clinical impact of medication reconciliation using two criteria: the number of inpatients who had experienced at least one medication error; the severity of the potential harm associated with these detected errors. METHOD: The study was a prospective observational one. The eligible population included patients aged 65 and over subjected to medication reconciliation at admission. The potential severity of medication errors was evaluated independently by the physician in charge of the patient and by the pharmacist involved in the medication reconciliation process. Severity assessment took account of the drug(s) involved in the error, the type of medication error, and the patient's clinical and biological data. RESULTS: From January 2011 to September 2012, 1799 medication errors were recorded among the 1670 patients subjected to medication reconciliation who were hospitalised from the emergency department. At least one medication error occurred for 744 (44.6%) of these patients. There were 87 medication errors associated with potentially major severity (5.6%). These concerned 67 patients (4.2%). The most prevalent error was omission. Cardiovascular and anticoagulant drugs were the drugs most frequently involved in these serious medication errors. Arrhythmia, haemorrhage, thrombosis, hyperglycaemia and hypoglycaemia were identified as the most likely harms that could have occurred. CONCLUSIONS: The detection of cases of serious potential harm shows the clinical impact of medication reconciliation. It would be interesting to perform a multicentred assessment using indicators such as the number of inpatients experiencing at least one serious medication error. This could help to promote medication reconciliation as essential for patient safety.

[A Woman Experienced Severe Thrombocytopenia When Treated With Fluvastatin]. / Thrombopénie sévère chez une patiente traitée par fluvastatine.

A 62-year-old woman treated with fluvastatin experienced three separate thrombocytopenic illnesses, severe on two occasions associated with nadir platelet count of 57 000/µL and 75 000/µL. The hospital pharmacist replaced fluvastatin by pravastatin during three stays. Platelet count has increased some days after this substitution. These results suggest that fluvastatin could be involved in these thrombocytopenic episodes.