The association between physician competence at licensure and the quality of asthma management and patient morbidity
Notice bibliographique
Résumé
Asthma imposes a substantial burden on patient health and health care expenditures. Persistent trends of sub-optimal asthma management and significant morbidity indicate the need to search for other key barriers and facilitators of quality of care. Through the use of administrative databases, this project first addressed the methodological challenge of identifying asthma patients, and then investigated the role of physicians and determinants of their approach to effective asthma management. Our objectives were 1) to develop an algorithm to identify patients with asthma, based on potential asthma-specific markers, from medical service and prescription claims databases, 2) to estimate the extent to which physician characteristics, specifically clinical competence, influenced the quality of asthma medication utilization and asthma morbidity. In the first study, 1,434 patients with confirmed asthma were identified from clinic medical records available through an existing electronic medical record project. Therapeutic indication for electronic prescriptions and the confirmed asthma from an inter-institutional automated problem list were used as the gold standard for physician-confirmed asthma. Using multiple logistic regression, we estimated the probability of the presence of asthma, using a combination of five groups of asthma-specific markers from administrative databases. Receiver Operating Characteristic (ROC) curves was used to assess the optimal cut-off probability of algorithms. The algorithm that showed the best performance in discriminating between the patients with asthma and those without it included indicators from medical services, pharmacy, and the demographic databases. The best fitting algorithm used a cut-off probability of 0.128 for asthma with sensitivity of 71%, specificity of 93%, and positive predictive value of 62%. In the second study, a prospective cohort of 609 physicians, who took the Medical Council of Canada (MCC) Part 2 examination between 1993 and1996 and provided a care for asthma patients in Quebec between 1993 and 2003 was assembled. Patients whose asthma was out-of-control at the index visit were followed for 6 months after the first visit with a study physician (index visit). Patients of physicians who achieved higher scores in communication (per 1 Standard Deviation (SD) increase in score) had a lower risk of persistent Fast-Acting Beta Agonist (FABA) overuse (OR=0.97; 95%CI: 0.94-1.0) and multiple ER visits for respiratory problems (OR=0.90; 95%CI:0.82-1.00). Higher MCCQE1, MCCQE2 and MCCQE2 communication scores were associated with a 4 to7% greater likelihood of inhaled corticosteroid (ICS) use (per 1SD increase). Similarly, higher scores achieved on the MCCQE1 as well as the MCCQE2 exams were associated with a 4 to 9% higher likelihood of the ICS/Total asthma medication (ICS plus FABA) ratio being >0.5 (per 1 SD increase). This project presents two major contributions. First, we demonstrated a unique and practical methodological approach to identify patients with asthma from administrative claims databases for the future assessment of asthma management. Second, we identified important physician abilities for effective management of patients with out-of-control asthma.
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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 | 0,008 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 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 ».