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Record W2003548421 · doi:10.4212/cjhp.v62i5.826

Best Possible Medication History for Hemodialysis Patients Obtained by a Pharmacy Technician

2009· article· en· W2003548421 on OpenAlexaffvenue
Marianna Leung, Joanne Jung, Wynnie Lau, Mercedeh Kiaii, Beverly Jung

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2009
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of British ColumbiaVancouver Coastal HealthProvidence Health Care
Fundersnot available
KeywordsTechnicianPharmacy technicianPharmacistMedicinePharmacyHemodialysisMedical emergencyFamily medicineEmergency medicineSurgery

Abstract

fetched live from OpenAlex

Background: Outpatients undergoing hemodialysis are at high risk for adverse drug events. Limited resources make it challenging for pharmacists to routinely obtain a best possible medication history (BPMH).Objectives: The primary objective was to determine whether, for patients undergoing hemodialysis, a pharmacy technician has the skills to obtain a BPMH that would allow a pharmacist to identify drug-related problems. The secondary objectives were to determine the number and types of medication discrepancies and drug-related problems identified and the time required by the technician to complete the BPMH.Methods: All patients treated in the hemodialysis unit during the study period were included, except for those who required an interpreter or were unable to participate in an in-person interview. A single technician was taught how to interview patients according to a structured format. For each patient, the technician’s BMPH was verified by a pharmacist. The agreement rate between technician and pharmacists was determined, along with the number and types of discrepancies and drugrelated problems identified.Results: The technician interviewed 99 patients. Of the 1334 medication orders reviewed, the technician and pharmacists agreed on all but 15 (agreement rate 98.9%). A total of 358 medication discrepancies were noted for 93 patients (3.8 discrepancies per patient). Of these, 210 (59%) were undocumented intentional discrepancies, and 148 (41%) were unintentional discrepancies (most commonly errors of commission). Of the 135 drug-related problems identified, the majority involved dosing problems or nonadherence. The technician required an average of 17 min for each interview.Conclusion: An adequately trained technician was capable of interviewing patients to create a BPMH. A variety of medication discrepancies and drug-related problems were identified. Generation of a BPMH by a technician is a useful approach allowing pharmacists to identify drugrelated problems.RÉSUMÉ Contexte : Les patients externes sous hémodialyse sont à risque élevé d’événements indésirables liés aux médicaments. Avec les ressources limitées, il est difficile pour les pharmaciens d’obtenir systématiquement le meilleur schéma thérapeutique possible (MSTP).Objectifs : Le principal objectif était de déterminer si, pour les patients sous hémodialyse, un technicien en pharmacie possède les compétences pour obtenir un MSTP qui permettrait au pharmacien d’identifier les problèmes reliés à la pharmacothérapie. Les objectifs secondaires étaient de déterminer le nombre et le type de différences relativement aux médicaments ainsi que les problèmes reliés à la pharmacothérapie identifiés et le temps requis par le technicien pour compléter le MSTP.Méthodes : Tous les patients de l’unité d’hémodialyse au moment de l’étude ont été admis à celle-ci, à l’exception de ceux qui avaient besoin d’un interprète ou qui étaient incapables de participer à une entrevue en personne. Un seul technicien a été formé pour ménager une entrevue structurée avec les patients. Pour chaque patient, les MSTP obtenus par le technicien ont été vérifiés par un pharmacien. Le taux de correspondance entre les renseignements recueillis par le technicien et ceux validés par les pharmaciens a été déterminé et le nombre ainsi que les types de différences et les problèmes reliés à la pharmacothérapie identifiés ont été répertoriés.Résultats : Le technicien a interviewé 99 patients. Des 1334 ordonnances de médicament analysées, le technicien et les pharmaciens étaient en désaccord pour 15 d’entre elles, soit un taux de concordance de 98,9 %. On a relevé 358 différences relativement aux médicaments chez 93 patients (3,8 différences par patient). De ces dernières, 210 (59 %) étaient des différences intentionnelles non consignées et 148 (41%), des différences non intentionnelles (le plus souvent des erreurs de commission). Des 135 problèmes reliés à la pharmacothérapie identifiés, la plupart concernaient des problèmes de posologie ou de non-observance. Le technicien passait en moyenne 17 minutes par entrevue.Conclusion : Un technicien adéquatement formé était en mesure d’interviewer les patients pour créer un MSTP. Une variété de différences relativement aux médicaments et de problèmes reliés à la pharmacothérapie ont été identifiés. La création d’un MSTP par un technicien est une approche utile permettant aux pharmaciens d’identifier les problèmes reliés à la pharmacothérapie.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.064
GPT teacher head0.357
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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Citations43
Published2009
Admission routes2
Has abstractyes

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