Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.
Objectives Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, their prevention and resolution. The objective of this...
| Publicado en: | European Journal of Hospital Pharmacy Vol. 32; no. 4; pp. 324 - 329 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BMJ Publishing Group
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187586542&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187586542 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20479956 FD0Y jtl: European Journal of Hospital Pharmacy issn: 20479956 maglogo: N pubinfo: dt: Jul2025 vid: 32 iid: 4 pid: 8280 pub: BMJ Publishing Group artinfo: ui: 187586542 187586542 187586542 10.1136/ejhpharm-2024-004139 187586542 ppf: 324 ppct: 5 formats: tig: atl: Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital. aug: au: Alkanj, Ahmad Godet, Julien Johns, Erin Gourieux, Benedicte Michel, Bruno affil: Laboratoire de Pharmacologie et Toxicologie NeuroCardiovasculaire UR7296, Strasbourg, France sug: subj: Deep Learning France Neural Networks (Computer) Medication Errors Classification Adverse Drug Event Classification Pharmacists Prescriptions, Drug Medication Review Human Descriptive Research Retrospective Design Academic Medical Centers France Software Algorithms Descriptive Statistics Nonexperimental Studies Data Analysis Software ab: Objectives Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, their prevention and resolution. The objective of this study was to exploit a newly developed deep neural network classifier to identify drug-related problems from pharmaceutical interventions and perform a large retrospective descriptive analysis of them in a French university hospital over a 3-year period. Methods Data were collected from prescription support software from 2018 to 2020. A classifier running in Python 3.8 and using Keras library was then used to automatically categorise drug-related problems from pharmaceutical interventions according to the coding of the French Society of Clinical Pharmacy. Results 2 930 656 prescription lines were analysed for a total of 119 689 patients. Among these prescription lines, 1 53 335 (5.2%) resulted in pharmaceutical interventions (n=48 202 patients; 40.2%). Pharmaceutical interventions were predominantly observed in patients aged 65 years or older (n=26 141 patients out of 53 186; 49.1%) and in patients taking five or more medications (44 702 patients out of 93 419; 47.8%). The most frequently identified types of drug-related problems associated with pharmaceutical interventions were 'Nonconformity to guidelines or contra-indication' (n=88 523; 57.7%), 'Overdosage' (16 975; 11.1%) and 'Improper administration' (13 898; 9.1%). The most frequently encountered drugs were: paracetamol (n=10 585; 6.9%), esomeprazole (6031; 3.9%), hydrochlorothiazide (2951; 1.9%), enoxaparin (2191; 1.4%), tramadol (1879; 1.2%), calcium (2073; 1.3%), perindopril (1950; 1.2%), amlodipine (1716; 1.1%), simvastatin (1560; 1.0%) and insulin (1019; 0.7%). Conclusions The deep neural network classifier used met the challenge of automatically classifying drug-related problems from pharmaceutical interventions from a large database without mobilising significant human resources. The use of such a classifier can lead to alerting caregivers about certain risky practices in prescription and administration, and triggering actions to improve patients' therapeutic outcomes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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