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Record W2061294015 · doi:10.4212/cjhp.v62i4.809

Unintended Medication Discrepancies Associated with Reliance on Prescription Databases for Medication Reconciliation on Admission to a General Medical Ward

2009· article· en· W2061294015 on OpenAlexaffvenueabout
Kelli Kalb, Stephen Shalansky, Michael Legal, Nadia Khan, Irene Ma, Garth Hunte

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2009
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryVancouver Coastal Health
Fundersnot available
KeywordsMedical prescriptionMedicineMedical recordHarmEmergency medicineDatabaseHospital admissionMEDLINEFamily medicinePediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: In a recent study, 50% of the patients who were admitted to a hospital’s general medicine ward had at least one error in medication orders at the time of admission related to inaccuracies in the medication history. The use of computerized prescription databases has been suggested as a way to improve medication reconciliation at the time of admission.Objective: To quantify and describe unintended discrepancies between a best possible medication history and medications ordered on admission to the general medicine ward in a hospital with routine access to a provincial outpatient prescription database (British Columbia’s PharmaNet).Methods: This prospective study involved 20 patients who were regularly using at least 4 prescription medications before admission to hospital. The best possible medication history for each patient (based on a review of the medical chart and the PharmaNet record and an interview with the patient) was compared with the physician’s admission orders to identify any discrepancies. The frequency and perceived severity of discrepancies, graded independently by 3 physicians, were compared with observations from a similar study conducted at a hospital where a prescription database was not available.Results: The 20 patients were recruited between September 2005 and January 2006. For 8 patients (40%), information in the PharmaNet database was consistent with the prescription medication list obtained during the best possible medication history at the time of admission. For the other 12 patients, a total of 30 unintended discrepancies were identified, 13 (43%) of which were classified as having potential for moderate or severe harm. The proportion of patients with unintended discrepancies was similar to that for the comparison cohort (60% versus 54%). Although the percentage of discrepancies involving omissions was lower than in the comparison population (37% versus 46%), these results were offset by a higher proportion of commission discrepancies (27% versus 0%).Conclusion: Unintended discrepancies were frequent, despite use of the PharmaNet database at the time of admission. Inconsistencies between the PharmaNet record and patients’ actual medication use, coupled with failure to verify PharmaNet data with patients, were likely contributing factors.RÉSUMÉ Contexte : Dans une récente étude, 50 % des patients admis à une unité de médecine générale avaient au moment de leur admission au moins une erreur d’ordonnance de médicament liée à une histoire médicamenteuse inexacte. Il a été suggéré de recourir à des bases de données informatisées sur les ordonnances comme moyen d’améliorer le bilan comparatif des médicaments à l’admission.Objectif : Quantifier et décrire les différences accidentelles entre le meilleur schéma thérapeutique possible et les ordonnances de médicaments rédigées à l’admission à l’unité de médecine générale en ayant un accès courant à la base de données provinciale sur les ordonnances externes (PharmaNet, en Colombie-Britannique).Méthodes : Il s’agit d’une étude prospective de 20 patients qui prenaient couramment au moins quatre médicaments d’ordonnance avant leur admission à l’hôpital. Le meilleur schéma thérapeutique possible pour chaque patient (établi grâce à l’étude du dossier médical et du registre PharmaNet et à un entretien avec le patient) a été comparé aux ordonnances rédigées à l’admission par le médecin pour détecter toute différence. La fréquence et l’importance perçue des disparités classées indépendamment par trois médecins ont été comparées aux observations tirées d’une étude similaire menée dans un hôpital où il n’y avait pas d’accès à une telle base de données.Résultats : Les 20 patients ont été recrutés entre septembre 2005 et janvier 2006. Chez huit patients (40 %), l’information dans la base de données PharmaNet concordait avec la liste de médicaments d’ordonnance obtenue du meilleur schéma thérapeutique possible relevé au moment de l’admission. Pour les 12 autres patients, on a noté un total de 30 différences accidentelles, dont 13 (43 %) ont été classées comme ayant un potentiel délétère modéré à grave. La proportion de patients chez qui on a observé des différences accidentelles était similaire à celle de la cohorte de référence (60 % contre 54 %). Bien que le pourcentage de différences impliquant des omissions était plus faible que celui de la cohorte de référence (37 % contre 46 %), ces résultats ont été contrebalancés par une proportion plus élevée de différences de commission (27 % contre 0 %).Conclusion : Les différences accidentelles étaient fréquentes malgré le recours à la base de données PharmaNet au moment de l’admission. Des disparités entre le registre PharmaNet et les médicaments réels du patient, jumelées à l’absence de vérification des données tirées de PharmaNet avec les patients, constituaient vraisemblablement des facteurs contribuant à de telles différences accidentelles.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.127
GPT teacher head0.406
Teacher spread0.279 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations19
Published2009
Admission routes3
Has abstractyes

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