Impact of PharmaNet-Based Admission Medication Reconciliation on Best Possible Medication Histories for Warfarin
Bibliographic record
Abstract
ABSTRACT Background: Inaccurate documentation of medication histories may lead to medication discrepancies during hospital admissions. Obtaining a best possible medication history (BPMH) for warfarin can be challenging because of frequent dosage changes and nonspecific directions of use (e.g., “take as directed”). On February 27, 2012, the study hospital implemented an admission medication reconciliation (MedRec) process using a form that compiled the most recent 6 months of outpatient prescription dispensing history from a provincial electronic database called PharmaNet. It was unclear whether admission MedRec had improved the process of obtaining warfarin BPMHs and the quality of their documentation. Objective: To compare the rates of complete warfarin BPMH documentation before and after implementation of PharmaNet-based admission MedRec. Methods: A single-centre, retrospective chart review was conducted using the health records of patients receiving warfarin who were admitted to the hospital’s Internal Medicine service before and after implementation of admission MedRec. The study periods were October 1, 2009, to February 26, 2012, and February 27, 2012, to July 31, 2014, respectively. The primary outcome was the rate of complete warfarin BPMH documentation during each period. Results: Data were recorded for 100 patients in the pre-implementation phase and 100 patients in the post-implementation phase. The rates of complete warfarin BPMH documentation were 65% and 84% in these 2 phases, respectively (p = 0.002). Conclusion: Implementation of PharmaNet-based admission MedRec was associated with a statistically significant increase in the rate of complete warfarin BPMH documentation. RÉSUMÉ Contexte : La consignation inexacte des schémas thérapeutiques peut mener à des divergences au chapitre des médicaments durant l’hospitalisation. Il peut être difficile d’établir un meilleur schéma thérapeutique possible (MSTP) pour la warfarine à cause de fréquents changements de posologie et de modes d’emploi imprécis (par exemple, « usage connu »). Le 27 février 2012, l’hôpital où s’est déroulée l’étude a mis en place un processus de bilan comparatif des médicaments (BCM) à l’admission. Celui-ci emploie un formulaire dressant la liste des médicaments d’ordonnance délivrés aux patients externes au cours des six derniers mois selon PharmaNet, une base de données numérique provinciale. On ignorait si les BCM à l’admission avaient amélioré le processus d’obtention et la qualité de la consignation des MSTP liés à la warfarine. Objectif : Comparer les taux de MSTP relatifs à la warfarine parfaitement consignés avant et après la mise en place d’un processus de BCM à l’admission qui s’appuie sur PharmaNet. Méthodes : Une analyse rétrospective des dossiers médicaux de patients menée dans un seul centre a été réalisée. Elle a porté sur les patients recevant de la warfarine et ayant été hospitalisés au service de médecine interne de l’hôpital avant ou après la mise en place d’un processus de BCM à l’admission (respectivement du 1er octobre 2009 au 26 février 2012 et du 27 février 2012 au 31 juillet 2014). Le principal paramètre d’évaluation était le taux de MSTP relatifs à la warfarine parfaitement consignés pendant ces périodes. Résultats : On a recueilli des données sur 100 patients hospitalisés avant la mise en place du processus et sur 100 patients hospitalisés après sa mise en place. Les taux de MSTP relatifs à la warfarine parfaitement consignésétaient de 65 % avant la mise en place et de 84 % après la mise en place (p = 0,002). Conclusion : La mise en place d’un processus de BCM à l’admission s’appuyant sur les données de PharmaNet était associée à une augmentation statistiquement significative du taux de MSTP relatifs à la warfarine parfaitement consignés.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".