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Record W2969543049 · doi:10.4212/cjhp.v72i4.2915

Hospital Pharmacists’ Perceptions and Decision-Making Related to Drug-Drug Interactions

2019· article· en· W2969543049 on OpenAlexaffvenue
Harkaryn Bagri, Karen Dahri, Michael Legal

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsSt. Paul's HospitalVancouver General HospitalUniversity of British ColumbiaSurrey Memorial Hospital
Fundersnot available
KeywordsDrugPerceptionMedicineFamily medicinePsychologyPharmacology

Abstract

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ABSTRACTBackground: Pharmacists often overlook drug interaction alerts because of limitations in clinical decision support (CDS) software systems intended to detect evidence-based, clinically significant drug-drug inter-actions (DDIs). Alert fatigue, which occurs when pharmacists become desensitized to an overload of DDIs, may also contribute.Objectives: To gain a better understanding of how pharmacists assess common DDIs and the extent to which computerized drug alerts affect their decision-making, as background for initiatives to overcome alert fatigue and improve detection of DDIs.Methods: This qualitative study used focus group methodology. A structured focus group was planned at each of 3 large tertiary hospitals. Pharmacists were invited to participate if their jobs included patient care and/or dispensary responsibilities. The focus group discussions were audio-recorded and subsequently transcribed, analyzed, and coded into themes using NVivo software. Four main categories of themes were identified: perceived challenges, pharmacists’ assessment of DDIs, barriers to responding to alerts, and proposed solutions.Results: The participants (n = 24) described a large discrepancy among CDS software systems in terms of the severity of specific DDIs, which made it difficult to view these systems as reliable sources. The participants agreed that alert fatigue is present and contributes to DDIs being overlooked. However, lack of patient information to make an initial assessment, as well as the constant need for multitasking, prevents pharmacists from focusing on the evaluation of DDIs. Conclusions: Although alert fatigue was reported to be a common factor responsible for pharmacists missing DDIs, other barriers also exist. Participants suggested ways to limit DDI alerts to those that are clinically relevant. Having a collaborative team of pharmacists periodically review the DDIs embedded in the CDS system, incorporating a colour-code system, and removing duplicate entries were discussed as ways to improve system efficiency.RÉSUMÉContexte : Les pharmaciens ignorent souvent les alertes d’interactions médicamenteuses à cause des limites des logiciels d’aide à la décision clinique (ADC) conçus pour détecter les interactions médicamenteuses (IM) factuelles et significatives d’un point de vue clinique. La fatigue liée aux alarmes (alert fatigue), qui survient lorsque les pharmaciens sont désensibilisés à cause d’une surcharge d’IM, peut aussi contribuer à cette situation.Objectifs : Mieux comprendre comment les pharmaciens évaluent les IM courantes et dans quelle mesure les alertes médicamenteuses affectent leur prise de décision, dans le cadre de la mise en œuvre d’initiatives visant à surmonter la fatigue liée aux alarmes et à mieux détecter les IM.Méthodes : La méthodologie de cette étude qualitative se basait sur les groupes de discussion. Un groupe de discussion structuré était prévu dans chacun des trois grands hôpitaux tertiaires. Les pharmaciens étaient invités à participer si leur travail comprenait des soins offerts aux patients ou des responsabilités dans la distribution de médicaments. Les discussions dans les groupes ont fait l’objet d’un enregistrement audio avant d’être retranscrites, analysées et codées selon les thèmes à l’aide du logiciel NVivo. Quatre catégories de thèmes principaux ont été établies : les défis perçus, l’évaluation des IM par les pharmaciens, les obstacles à lever pour répondre aux alertes et les solutions proposées.Résultats : Les participants (n = 24) ont mentionné un écart important dans les définitions de la gravité [severity] d’IM spécifiques données par les logiciels d’ADC, de sorte qu’il était difficile de se fier à ces systèmes. Les participants ont indiqué que la fatigue liée aux alarmes existait bel et bien et qu’elle contribuait au manque de prise en compte des IM. Cependant, le manque d’information sur les patients pour faire l’évaluation initiale, ainsi que le besoin constant d’effectuer plusieurs tâches à la fois, empêche les pharmaciens de se concentrer sur l’évaluation des IM. Conclusions : Bien que la fatigue liée aux alarmes empêche fréquemment les pharmaciens de remarquer les IM, il existe d’autres obstacles. Les participants ont proposé de limiter les alertes d’IM à celles pertinentes d’un point de vue clinique. Les solutions examinées pour améliorer l’efficacité du système ont porté sur la formation d’une équipe collaborative de pharmaciens qui examine périodiquement les IM intégrés dans le système ADC, l’incorporation d’un système de codes de couleur et l’élimination des entrées dupliquées.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.411
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations22
Published2019
Admission routes2
Has abstractyes

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