Delivery of Preventive Services to Older Adults by Primary Care Physicians
Notice bibliographique
Résumé
CONTEXT: Rates of preventive services remain below national goals. OBJECTIVE: To identify characteristics of physicians and their practices that are associated with the quality of preventive care their patients receive. DESIGN: Cross-sectional analysis of data on US physician respondents to the 2000-2001 Community Tracking Study Physician Survey linked to claims data on Medicare beneficiaries they treated in 2001. Physician variables included training and qualifications and sex. Practice setting variables included practice type, size, sources of revenue, and access to information technology. Analyses were adjusted for patient demographics and comorbidity, as well as community characteristics. SETTING AND PARTICIPANTS: Primary care delivered by 3660 physicians providing usual care to 24 581 Medicare beneficiaries aged 65 years and older. MAIN OUTCOME MEASURES: Proportion of eligible beneficiaries receiving each of 6 preventive services: diabetic monitoring with hemoglobin A(1c) measurement or eye examinations, screening for colon or breast cancer, and vaccination for influenza or pneumococcus in 2001. RESULTS: Overall, the proportion of beneficiaries receiving services was below national goals. Physician and, more consistently, practice-level characteristics were both associated with differences in the delivery of services. The strongest associations were with practice type and the percentage of practice revenue derived from Medicaid. For instance, beneficiaries receiving usual care in practices with less than 6% of revenue from Medicaid were more likely than those with more than 15% of revenue derived from Medicaid to receive diabetic eye examinations (48.9% vs 43%; P = .02), hemoglobin A1c monitoring (61.2% vs 48.4%; P<.001), mammograms (52.1% vs 38.9%; P<.001), colon cancer screening (10.0% vs 8.5%; P = .60), and influenza (50.2% vs 39.2%; P<.001) and pneumococcal (8.2% vs 6.4%; P<.001) vaccinations. Other variables associated with delivery of preventive services after adjustment for patient and geographic factors included obtaining usual health care from a physician who worked in group practices of 3 or more, who was a graduate of a US or Canadian medical school, or who reported availability of information technology to generate preventive care reminders or access treatment guidelines. CONCLUSIONS: Delivery of routine preventive services is suboptimal for Medicare beneficiaries. However, patients treated within particular practice settings and by particular subgroups of physicians are at particular risk of low-quality care. Profiling these practices may help develop tailored interventions that can be directed to sites where the opportunities for quality improvement are greatest.
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 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».