Health technologies for the improvement of chronic disease management: a review of the Medical Advisory Secretariat evidence-based analyses between 2006 and 2011.
Notice bibliographique
Résumé
BACKGROUND: As part of ongoing efforts to improve the Ontario health care system, a mega-analysis examining the optimization of chronic disease management in the community was conducted by Evidence Development and Standards, Health Quality Ontario (previously known as the Medical Advisory Secretariat [MAS]). OBJECTIVE: The purpose of this report was to identify health technologies previously evaluated by MAS that may be leveraged in efforts to optimize chronic disease management in the community. DATA SOURCES: The Ontario Health Technology Assessment Series and field evaluations conducted by MAS and its partners between January 1, 2006, and December 31, 2011. REVIEW METHODS: Technologies related to at least 1 of 7 disease areas of interest (type 2 diabetes, coronary artery disease, atrial fibrillation, chronic obstructive pulmonary disease, congestive heart failure, stroke, and chronic wounds) or that may greatly impact health services utilization were reviewed. Only technologies with a moderate to high quality of evidence and associated with a clinically or statistically significant improvement in disease management were included. Technologies related to other topics in the mega-analysis on chronic disease management were excluded. Evidence-based analyses were reviewed, and outcomes of interest were extracted. Outcomes of interest included hospital utilization, mortality, health-related quality of life, disease-specific measures, and economic analysis measures. RESULTS: Eleven analyses were included and summarized. Technologies fell into 3 categories: those with evidence for the cure of chronic disease, those with evidence for the prevention of chronic disease, and those with evidence for the management of chronic disease. CONCLUSIONS: The impact on patient outcomes and hospitalization rates of new health technologies in chronic disease management is often overlooked. This analysis demonstrates that health technologies can reduce the burden of illness; improve patient outcomes; reduce resource utilization intensity; be cost-effective; and be a viable contributing factor to chronic disease management in the community. PLAIN LANGUAGE SUMMARY: People with chronic diseases rely on the health care system to help manage their illness. Hospital use can be costly, so community-based alternatives are often preferred. Research published in the Ontario Health Technology Assessment Series between 2006 and 2011 was reviewed to identify health technologies that have been effective or cost-effective in helping to manage chronic disease in the community. All technologies identified led to better patient outcomes and less use of health services. Most were also cost-effective. Two technologies that can cure chronic disease and 1 that can prevent chronic disease were found. Eight technologies that can help manage chronic disease were also found. Health technologies should be considered an important part of chronic disease management in the community.
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 | 0,023 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,019 | 0,023 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».