Specialized nursing practice for chronic disease management in the primary care setting: an evidence-based analysis.
Notice bibliographique
Résumé
BACKGROUND: In response to the increasing demand for better chronic disease management and improved health care efficiency in Ontario, nursing roles have expanded in the primary health care setting. OBJECTIVES: To determine the effectiveness of specialized nurses who have a clinical role in patient care in optimizing chronic disease management among adults in the primary health care setting. DATA SOURCES AND REVIEW METHODS: A literature search was performed using OVID MEDLINE, OVID MEDLINE In-Process and Other Non-Indexed Citations, OVID EMBASE, EBSCO Cumulative Index to Nursing & Allied Health Literature (CINAHL), the Wiley Cochrane Library, and the Centre for Reviews and Dissemination database. Results were limited to randomized controlled trials and systematic reviews and were divided into 2 models: Model 1 (nurse alone versus physician alone) and Model 2 (nurse and physician versus physician alone). Effectiveness was determined by comparable outcomes between groups in Model 1, or improved outcomes or efficiency in Model 2. RESULTS: Six studies were included. In Model 1, there were no significant differences in health resource use, disease-specific measures, quality of life, or patient satisfaction. In Model 2, there was a reduction in hospitalizations and improved management of blood pressure and lipids among patients with coronary artery disease. Among patients with diabetes, there was a reduction in hemoglobin A1c but no difference in other disease-specific measures. There was a trend toward improved process measures, including medication prescribing and clinical assessments. Results related to quality of life were inconsistent, but patient satisfaction with the nurse-physician team was improved. Overall, there were more and longer visits to the nurse, and physician workload did not change. LIMITATIONS: There was heterogeneity across patient populations, and in the titles, roles, and scope of practice of the specialized nurses. CONCLUSIONS: Specialized nurses with an autonomous role in patient care had comparable outcomes to physicians alone (Model 1) based on moderate quality evidence, with consistent results among a subgroup analysis of patients with diabetes based on low quality evidence. Model 2 showed an overall improvement in appropriate process measures, disease-specific measures, and patient satisfaction based on low to moderate quality evidence. There was low quality evidence that nurses working under Model 2 may reduce hospitalizations for patients with coronary artery disease. The specific role of the nurse in supplementing or substituting physician care was unclear, making it difficult to determine the impact on efficiency. PLAIN LANGUAGE SUMMARY: Nurses with additional skills, training, or scope of practice may help improve the primary care of patients with chronic diseases. This review found that specialized nurses working on their own could achieve health outcomes that were similar to those of doctors. It also found that specialized nurses who worked with doctors could reduce hospital visits and improve certain patient outcomes related to diabetes, coronary artery disease, or heart failure. Patients who had nurse-led care were more satisfied and tended to receive more tests and medications. It is unclear whether specialized nurses improve quality of life or doctor workload.
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 | 0,018 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,012 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».