Towards an Integrated Care System in France: the "Pioneer Territories" experiment
Notice bibliographique
Résumé
Starting in 2018, the French Hospitals Federation (FHF) has developped a clinical integration model based on "Responsabilité Populationnelle" (accountabilty towards a population). The model rellies on localy developped "action plans" covering targeted populations, from "at risk" people to severly ill patients. The model is developped and tested in five "Pioneer Territories", covering 1.3M habitants, and built around Territorial Hospital Groups. The Five Territories ""share"" two common target populations : people at risk or suffering from Type 2 diabetes and Heart Failure. These populations represent around 60 000 ill persons, and 300 000 ""at risk"" individuals, in the Five regions. They share a common methodology, developped by FHF's teams and leading medical experts in the fields of diabetology, cardiology and public health. The model relies on extensive use of healthcare data, in particular the ability to ""stratify"" target populations into clinical profiles. To each profiles are attached clinical guidelines that must be executed for every patient, according to its profile. It is up to local actors to create their "actions plans", leveraging local assets to create local pathways, and local ressources to adress local needs. Large scale deployment started in January 2022. In September 2022, more than 700 healthcare professionals, as well as 60 ""patient/partners"" were actively involved in the program. More than 450 outreach/prevention actions had been performed, and more than 5 300 at risk individuals had been screend for T2D or HF. More than 1 700 patients were included in ""clinical programs"", according to their clinical profiles. We are already measuring impacts in terms of hospital use for these populations : reduced ER utilization, reduced lenght of stay, increased planned admissions. Key Takeaways: The model is inspired by other Integrated health systems. However, FHF having no regulatory power, the model is primarely built around clinical practices and intrinsic motivations. We rely on the ability of local healthcare professionals and actors to develop actions plans that will improve coordination between them and the timely use of healthcare ressources for the benefit of the populations. So far, the model is producing positive results in five very different regions, providing valuable insight as to the scalabilty of the model in other regions, and/or for other target populations. The Five regions have shown tremedous ability to mobilize their local ecosystems, ranging from community groceries to local authorities, to patient organizations and other community assets. Data analysis and utilization, in particular the stratification methods developped for the program, are based on DRG's, making them highly transferable to other HC organizations that use ICD-10 coding. These tools were well received by local HC professionals and are used on a daily basis in the Five Territories. Among the key challenges at this stage, the question of a unified Information System is the most pressing. At this stage, there is still no single EHR in France, making the connection between hospitals and ambulatory providers cumbersome. Morevoer, if we want to model to endure and to be generalyzed in France, regulatory intervention by National authorities will be necessary.
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 | 0,042 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».