Integrated care networks for the vulnerable elderly: North American prototypes, performance and lessons
Bibliographic record
Abstract
PURPOSE: To describe in general terms the managed care network (MCN) rationale and approach to the organisation and delivery of services to the long term care (LTC) elderly, report on the prototypes and performance associated with three major North American models (PACE in U.S. and SIPA and PRISMA in Canada), and present the major lessons learned from these three programmes. CONTEXT: Irrespective of cross-national differences in LTC for the elderly, countries confront broadly similar challenges, including fragmented services, disjointed care, less-than-optimal quality, system inefficiencies and difficult-to-control costs. The MCN model, which more or less combines responsibilities, resources and funding for a broad array of LTC under ‘one roof’, is attracting increasing international attention as a promising structural solution to the problem of how best to deliver seamless, cost-effective care to a burgeoning vulnerable elderly population. North America—both the U.S. and Canada—is an especially fertile proving ground for MCNs for the vulnerable elderly. DATA SOURCES: Data are derived from the respective programme evaluations and other sources of publicly available descriptive and outcomes data pertaining to the projects under review. PRELIMINARY CONCLUSIONS: Although MCM models should be considered ‘works in progress’, they are responsible for a promising pattern of outcome measures in terms of access, clinical co-ordination and continuity, health and functional status, serviced utilisation, institutional placement, quality of life, carer burden, client satisfaction, and costs. Four main organisational elements—perhaps acting synergistically—appear to be responsible for these positive results: 1) umbrella organisational structure; 2) case-managed, multi-disciplinary team care; 3) organised provider network; and, 4) financial incentives. DISCUSSION: In order to unlock the full potential of MCN models for the vulnerable elderly, we will need: 1) better understanding of the relationships between structures, team working, services, service provision, and outcomes; 2) greater focus on client- and user-defined outcomes; and, 3) more insight into what specific organisational, managerial, clinical, and other levers produce positive results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".