Improving prescribing in the elderly: a study in the long term care setting.
Notice bibliographique
Résumé
OBJECTIVES: To determine the prevalence and predictors of potentially inappropriate prescribing of medications in the long term care setting, and to determine the effectiveness of follow-up pharmacist letters to the prescribing physicians in improving prescribing. PATIENTS AND METHODS: The Improving Prescribing in the Elderly Tool was applied to the charts of all long term care patients aged 65 years and over at Parkwood Hospital, a rehabilitation hospital/long term care facility in London, Ontario. All potentially inappropriate prescriptions were verified by a consensus panel consisting of a family physician, a geriatric medicine specialist and a geriatric pharmacist. Follow-up letters to the prescribing physicians were developed that briefly described the concerns with the potentially inappropriate prescriptions and suggested safer alternatives. These letters were sent to the prescribing physicians, accompanied by a brief survey. Patient charts in which a potentially inappropriate prescription had been noted were reviewed for prescription changes two months after the prescribing physicians had received the follow-up letters. RESULTS: A total of 69 potentially inappropriate prescriptions were found in 65 of 355 long term care patients (18.3%). The most common types of potentially inappropriate prescriptions were anticholinergic drugs to manage antipsychotic side effects (17 cases), tricyclic antidepressants with active metabolites (16 cases), and long-acting benzodiazepines (14 cases). The total number of prescription medications (P<0.001), a history of mental illness (P=0.002) and a high minimum data set (MDS) score for depression (P=0.002) were all highly associated with potentially inappropriate prescribing. Variables that were not correlated with increased rates of potentially inappropriate prescribing included age, sex, code status, a diagnosis of dementia (as documented explicitly in the chart), high MDS scores for delirium or cognitive impairment, the date of the prescribing physician's graduation and the total Charlson comorbidity index score. Potentially inappropriate prescriptions were significantly less common in patients seen by a geriatric medicine specialist (P<0.001). In response to the follow-up letter suggesting safer alternatives, 37.9% of potentially inappropriate prescriptions were changed by the prescribing physician. Ninety-two per cent of responding physicians rated the follow-up letter as a "somewhat" or "very" helpful method for improving prescribing in elderly patients. CONCLUSIONS: Potentially inappropriate prescribing in the long term care setting is common and can be improved by the provision of a follow-up letter suggesting safer alternatives.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».