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Record W2995630287 · doi:10.4212/cjhp.v72i6.2941

Evaluation of a Novel Audit Tool for Medication Reconciliation at Hospital Discharge

2019· article· en· W2995630287 on OpenAlexaffvenueabout
Anne Holbrook, H Bannerman, Amna Ahmed, J Tiger Liu, Sue Troyan, Alice Watt

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2019
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAuditMedicineDocumentationQuality managementHospital dischargeMedical emergencyMedication ReconciliationFamily medicineClinical pharmacyMEDLINEEmergency medicinePharmacistNursingPharmacyService (business)Intensive care medicineBusiness

Abstract

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ABSTRACTBackground: Discharge medication reconciliation (MedRec) is designed to reduce medication errors and inform patients and key postdischarge providers, but it has been difficult to implement routinely in Canadian hospitals.Objectives: To evaluate and optimize a new discharge MedRec quality audit tool and to use it at 3 urban teaching hospitals.Methods: The discharge MedRec quality audit tool, developed by the Canadian Patient Safety Institute and the Institute for Safe Medication Practices Canada, was assessed and modified to improve comprehensiveness, clarity, and quality. The modified tool was then used to evaluate the quality of the discharge MedRec process for adult patients discharged to home from the general internal medicine service at 3 academic hospitals. Postdischarge telephone interviews were conducted with consenting patients, their community pharmacists, and their family doctors.Results: The audit tool required modification to include aspects of admission MedRec, high-risk medication discrepancies, and direct communication of discharge MedRec to key follow-up providers. Thirty-five patients (mean age 67.7 years, standard deviation [SD] 18.0 years; 17 [49%] women), with a mean of 8.8 (SD 4.5) prescribed medications at discharge, participated in the discharge MedRec evaluation. Documentation of any discharge MedRec was found for only 1 patient (3%), and no discharge MedRec was carried out by pharmacists. Postdischarge follow-up interviews elicited major gaps in communication with community pharmacists and with family physicians, which could lead to serious medication errors.Conclusions: The modified audit tool was useful for identifying gaps in the quality of discharge MedRec.RÉSUMÉContexte : Le bilan comparatif des médicaments (BCM) au moment du congé est conçu pour réduire les erreurs médicamenteuses et informer les patients ainsi que les principaux prestataires de soins de santé après le congé, mais sa mise en œuvre systématique dans les hôpitaux canadiens s’est heurtée à de grandes difficultés.Objectifs : Évaluer et optimiser un nouvel outil d’évaluation de la qualité du BCM au moment du congé et l’utiliser dans trois hôpitaux universitaires urbains.Méthodes : Cet outil développé par l’Institut canadien pour la sécurité des patients (ICSP) et l’Institut pour la sécurité des médicaments aux patients du Canada (ISMP) a fait l’objet d’une évaluation et d’une modification visant à améliorer son exhaustivité, sa clarté et sa qualité. L’outil modifié a ensuite servi à évaluer la qualité du processus du BCM pour des patients adultes ayant obtenu leur congé après un séjour dans un service général de médecine interne dans trois hôpitaux universitaires. Des entretiens téléphoniques après le congé ont été menés avec les patients consentants, leur pharmacien communautaire et leur médecin de famille.Résultats : L’outil d’évaluation a dû être modifié pour inclure le BCM au moment de l’admission, des écarts de médication à haut risque et une communication directe du BCM aux prestataires de soins de santé principaux chargés du suivi après le congé. Trente-cinq patients (âge moyen : 67,7 ans; écart type [ET] 18 ans; 17 [49 %] femmes), chacun ayant reçu en moyenne 8,8 (ET 4,5) médicaments prescrits, ont participé à l’évaluation du BCM au congé de l’hôpital. Au moment du congé, on n’a trouvé de renseignements relatifs au BCM que pour un seul patient (3 %) et aucun BCM n’avait été préparé par les pharmaciens. Le suivi après le congé a généré des écarts de communication importants entre les pharmaciens communautaires et les médecins de famille, ce qui pourrait entraîner des erreurs médicamenteuses importantes.Conclusions : L’outil d’évaluation modifié a été utile pour déterminer les écarts relatifs à la qualité du BCM au moment du congé.

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)0.079
metaresearch head score (Gemma)0.158
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.158
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.132
GPT teacher head0.402
Teacher spread0.270 · 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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Citations3
Published2019
Admission routes3
Has abstractyes

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