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Record W3121055669 · doi:10.4212/cjhp.v74i1.3036

Correlation between Medication Administration–Related Errors in Patients with Parkinson Disease and Timing of Pharmacy-Led Best Possible Medication Histories

2021· article· en· W3121055669 on OpenAlexaffvenue
Emily Cowley, Michael R. Miller, Charles Yin, Lynne Kelly

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsLondon Health Sciences CentreWestern UniversityChildren’s Health Research InstituteAlberta Health Services
Fundersnot available
KeywordsMedicinePharmacyPharmacistPharmacy technicianClinical pharmacyRegimenEmergency medicinePediatricsFamily medicineInternal medicine

Abstract

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Background: Poor prescribing and incomplete medication administration have been linked to increased lengths of hospitalization for patients with Parkinson disease. The Institute for Safe Medication Practices (ISMP) has recommended that patients with Parkinson disease receive a pharmacy consultation within 2 h of admission to hospital. Objectives: To examine whether the time for a pharmacy team member to obtain a best possible medication history (BPMH) was associated with administration-related medication errors. The primary outcome was the proportion of doses with a medication error during a patient’s admission in relation to the time to completion of the initial BPMH by a registered pharmacist (RPh) or registered pharmacy technician (RPhT). The secondary objective was to compare the proportion of doses with a medication error in relation to whether the BPMH was completed by an RPh or an RPhT. Methods: This retrospective chart review involved patients with Parkinson disease who were admitted to the medicine services at London Health Sciences Centre from September 30, 2014, to September 30, 2018. Patients were included if they had Parkinson disease and a medication regimen that included levodopa-carbidopa. For all patients, an RPhT or RPh conducted the initial BPMH or updated the BPMH. Pearson correlation analysis was used to determine whether a correlation existed between administration-related errors and completion of the BPMH by a pharmacy staff member. Results: A total of 84 patients with 104 admissions were included. There was no significant correlation between the time to completion of the initial BPMH by a pharmacy team member and the proportion of doses with medication errors (p = 0.32). Although RPhTs completed the BPMHs more quickly than RPhs (p < 0.001), there was no significant difference between pharmacy team members in terms of the proportion of doses with medication errors (p = 0.86). Conclusions: Completing a BPMH within 2 h of a patient’s admission, as per the ISMP recommendation, is unlikely to affect administration-related medication errors, given that no correlation was identified. Expediting BPMH without addressing other factors is insufficient, and initiatives are required to improve the medication administration process. RÉSUMÉ Contexte : La mauvaise prescription et l’administration incomplète de médicaments ont été liées à une augmentation de la durée du séjour à l’hôpital des patients atteints de la maladie de Parkinson. L’Institute for Safe Medication Practices (ISMP) a recommandé que les patients atteints de la maladie de Parkinson obtiennent une consultation en pharmacie dans les 2 heures après leur admission à l’hôpital. Objectifs : Vérifier si le temps d’attente pour l’obtention, par un membre de l’équipe de la pharmacie, du meilleur schéma thérapeutique possible (MSTP) était associé ou non à des erreurs de médication liées à l’administration. Le résultat principal portait sur la proportion des doses comportant une erreur de médication lors de l’admission d’un patient par rapport au temps nécessaire à un pharmacien ou à un technicien en pharmacie autorisés pour réaliser le MSTP initial. L’objectif secondaire visait à comparer la proportion des doses comportant une erreur de médication entre un MSTP réalisé par un pharmacien autorisé et un MSTP réalisé par un technicien en pharmacie autorisé. Méthodes : Cet examen rétrospectif des dossiers impliquait des patients atteints de la maladie de Parkinson ayant été admis aux services de médecine au London Health Sciences Centre entre le 30 septembre 2014 et le 30 septembre 2018. Les patients pouvaient participer à l’étude s’ils avaient la maladie de Parkinson et qu’ils suivaient un traitement médicamenteux comprenant du lévodopa-carbidopa. Un pharmacien autorisé ou un technicien en pharmacie autorisé réalisait ou actualisait le MSTP initial de tous les patients. La corrélation de Pearson a servi à déterminer s’il existait une corrélation entre les erreurs liées à l’administration et la réalisation du MSTP par un membre du personnel de la pharmacie. Résultats : Au total, 84 patients correspondant à 104 admissions ont été inclus dans l’étude. Il n’y avait aucune corrélation importante entre le moment de la réalisation du MSTP initial par un membre du personnel de la pharmacie et la proportion des doses comportant des erreurs de médication (p = 0,32). Bien que les techniciens en pharmacie autorisés aient terminé plus rapidement leur MSTP que les pharmaciens autorisés (p < 0,001), aucune différence importante n’a été notée entre les membres du personnel de la pharmacie en termes de proportion des doses et d’erreur de médication (p = 0,86). Conclusions : Il est peu probable que la réalisation d’un MSTP dans les 2 heures après l’admission d’un patient, conformément à la recommandation de l’ISMP, ait une influence sur les erreurs de médication liées à l’administration, vu qu’aucune corrélation n’a été décelée. Précipiter la réalisation du MSTP sans aborder les autres facteurs ne suffit pas et des actions sont nécessaires pour améliorer le processus d’administration des médicaments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.343
Teacher spread0.293 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations2
Published2021
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