Using Front Line Networks to Develop and Implement Data Driven Policy
Notice bibliographique
Résumé
Introduction: Using data to drive policy development is desirable but typically leads to a top-down approach which may have limited implementation success at the clinical level. The province of Alberta, Canada, with a universal public health system, has robust, linked data sources which can inform healthcare policy.Objective: We sought to develop a mechanism whereby data could be more effectively used to create healthcare policy that would be acceptable and implementable within primary care.Targeted population: Our process was targeted to involve the approximately 3500 family doctors (representing 86% of all primary care) who practice in Primary Care Networks and who deliver primary care in the province of Alberta, population 4.2 million persons.Highlights: In our approach we used the robust, comprehensive data that comes from a universal health care system and which ties together elements such as resource utilization, physician and system contacts, diagnoses, lab and other clinical data, demographics, etcetera. Rather than having health administrators or the government interpret the data and develop policy which would then be sought to be implemented at a clinical level, we took a different approach. Instead, we shared the data with a formalized network that encompassed representation from all of Alberta's 42 primary care networks. The significance of the data was discussed, its priority and impact on various aspects of the health system agreed upon, and voluntary but binding policy decisions were made by consensus. Responsibility for implementation of the policies then fell to a joint partnership of the Alberta health care provision system (Alberta Health Services) and to the primary care networks and their individual practices. Once this process was underway, the Ministry of Health was advised and consulted so that policy could be formalized at a governmental level and be aligned with that of the health delivery system and provider organization. An important component of the approach was monitoring and tracking policy implementation and measuring its impact.Transferability: This approach can be used, in context and as appropriate, in many settings provided there are groups of organized providers and sources of data that are meaningful to those providers.Conclusions: By having an organizational structure that allows front-line providers to assess data and agree upon policy in collaboration with the healthcare system, and for the good of the population, formalization of such policy is easily carried out but, more importantly, there is buy-in across the provider community that allows for rapid and relatively easy implementation compared to traditional top-down policy implementation. However, success is dependent on having an organizational structure for the providers that can commit their constituents to a course of action; robust and comprehensive data along with the ability to interpret such data; as well as a trusting relationship between the providers, their organization, and in this case, the health care delivery system and government.
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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 | 0,156 | 0,158 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,007 | 0,011 |
| Communication savante | 0,025 | 0,025 |
| Science ouverte | 0,007 | 0,020 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,006 |
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 ».