Towards an Integrated Care System in France: the "Pioneer Territories" experiment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".