Managing High‐Cost Healthcare Users: The International Search for Effective Evidence‐Supported Strategies
Bibliographic record
Abstract
High-cost healthcare users (HCUs) are a small proportion of the population who use a disproportionate amount of healthcare resources. Although the phenomenon occurs across the entire age spectrum, older adults represent the majority of HCUs. HCUs have drawn increasing attention internationally from clinicians, health policy-makers, and government administrators. Many experts have suggested that the short- and long-term sustainability of the healthcare system is threatened unless current approaches to the care and healthcare costs of this population are modified. Complex case management and care coordination models are being implemented internationally to address HCUs despite a lack of strong evidence to support their effectiveness in improving clinical outcomes or savings in costs of care. We review what is known about HCUs and the available evidence for the effectiveness of interventions designed to manage their high and costly healthcare use.
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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 | 0.062 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".