Linked Data Paves the Way to Improved Health Care
Notice bibliographique
Résumé
ABSTRACTObjectivesOur organization links data from health care organizations and health ministries across Canada to create new knowledge. The new knowledge supports system and policy-decision makers in answering questions about the health or healthcare of population groups or healthcare sectors. This presentation provides an overview of recent analytical products that incorporate a variety of approaches to linking data.
 ApproachKey success factors in data linkages are the use of standards and robust methodologies. Standardized data can be linked at the individual level across hospital, long-term care, home care and community settings. Expanding knowledge about a specific sector can be achieved by linking physician billing, drug, and financial data. Linking clinical and financial data at the person and/or organization level provides insight into the cost of providing services, laying the foundation for examining efficiency and value for money. Data may also be used in non-traditional ways, for example, using payment data linked to clinical data to better understand how services are provided in various settings and to identify best practices.
 ResultsResults from these studies demonstrate how linked data creates new knowledge: 1) Hospital, emergency, and drug data were linked to show the number of emergency visits, hospitalizations, and the use of psychotropic medications by children and youth with mental health disorders are increasing, especially for those with mood and anxiety disorders living in urban areas. 2) Physician billing data was linked with hospital and emergency data to show that increased continuity of care with a family physician is associated with reductions in hospitalizations for people with chronic diseases and reductions in ED visits for people with conditions better managed in primary health care settings. 3) Drug data was linked with clinical assessment (InterRai) data to show that a large proportion of seniors living in long-term care exhibiting severe aggression were not being treated with antipsychotics, suggesting non-drug alternatives were often considered. 4) Financial data was linked with hospital clinical data to calculate an efficiency indicator that can be used at a system level to examine variation, and by individual managers to identify areas for further investigation and manage trends in spending.
 ConclusionThis presentation demonstrates how new knowledge created using linked data can inform decision and policy makers, and lead to action within the health system. Several examples are presented to illustrate the approach and types of data being linked.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,008 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,011 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».