Accuracy of a Prescription Claims Database for Medication Reconciliation for Outpatients with Heart Failure
Bibliographic record
Abstract
ABSTRACT Objective: To quantify agreement between the British Columbia prescription database (PharmaNet) and an interview-based assessment of current use of prescription medications. Methods: Outpatients taking prescription medications for heart failure were identified through hospital clinic and community pharmacy records. Consenting patients brought their prescription medications to an interview during which the PharmaNet profile was reviewed and a consensus reached regarding current medication use. Results: Discrepancies between the PharmaNet profile and the interview-based prescription medication history were identified for 138 (71.1%) of the 194 patients interviewed; these discrepancies represented a total of 353 (24.2%) of 1457 medications. The most common discrepancy involved medications that were listed in PharmaNet records but appeared inactive (overdue) according to refill records (205 medications for 81 patients). Eighty-five dosing discrepancies were identified, most frequently the result of adverse effects (11/85 or 13%) or ineffectiveness (10/85 or 12%). Diuretics were involved in discrepancies more frequently than other medication classes (77/353 or 21.8%), followed by s-blockers (38/353 or 10.8%) and angiotensin-converting enzyme inhibitors (28/353 or 7.9%). Conclusions: The majority of prescription medications taken by outpatients with heart failure appeared somewhere on the PharmaNet profile; however, without interviewing the patient, it was often difficult to determine which medications were still active or the dose that was currently being taken. PharmaNet profiles can be a valuable tool for determining an accurate prescription medication history if they are reviewed and clarified with patients during an interview. RESUME Objectif : Evaluer quantitativement la concordance entre les donnees repertoriees dans la base de donnees sur les ordonnances de la Colombie-Britannique (PharmaNet) et l’utilisation reelle des medicaments d’ordonnance selon une evaluation faite a partir d’entretiens avec les patients. Methodes : Des patients externes prenant des medicaments d’ordonnance pour l’insuffisance cardiaque ont ete selectionnes a partir des dossiers de cliniques externes et de pharmacies communautaires. Les patients consentants ont apporte leurs medicaments d’ordonnance dans le cadre d’un entretien au cours duquel les profils de PharmaNet ont ete examines, et un consensus a ete degage concernant l’utilisation actuelle des medicaments. Resultats : Des differences entre le profil PharmaNet et l’historique des medicaments d’ordonnance issu de l’entretien ont ete relevees chez 138 (71,1 %) des 194 patients interroges; ces differences touchaient un total de 353 (24,2 %) des 1457 medicaments prescrits. La difference la plus frequente avait trait a des medicaments qui apparaissaient dans PharmaNet, mais qui portaient le statut inactif (ordonnance a renouveler) d’apres l’enregistrement des renouvellements d’ordonnance (205 medicaments chez 81 patients). On a note 85 differences au chapitre de la posologie, la plupart du temps attribuables a des effets indesirables (11/85 ou 13 %) ou a l’inefficacite (10/85 ou 12 %). Les differences etaient attribuables plus frequemment aux diuretiques qu’aux autres medicaments (77/353 ou 21,8 %), suivis des â-bloquants (38/353 ou 10,8 %) et des inhibiteurs de l’enzyme de conversion de l’angiotensine (28/353 ou 7,9 %). Conclusions : La majorite des medicaments d’ordonnance pris par les patients externes souffrant d’insuffisance cardiaque apparaissaient dans le profil PharmaNet; toutefois, il etait difficile, sans parler au patient, de determiner quels medicaments ils prenaient toujours ou quelle etait la dose reellement prise. Les profils PharmaNet peuvent etre un outil precieux pour determiner un historique de medicaments d’ordonnance precis, s’ils sont examines et clarifies dans le cadre d’un entretien avec les patients.
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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 | 0.029 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".