A 3-year study of high-cost users of health care
Bibliographic record
Abstract
BACKGROUND: Characterizing high-cost users of health care resources is essential for the development of appropriate interventions to improve the management of these patients. We sought to determine the concentration of health care spending, characterize demographic characteristics and clinical diagnoses of high-cost users and examine the consistency of their health care consumption over time. METHODS: We conducted a retrospective analysis of all residents of Ontario, Canada, who were eligible for publicly funded health care between 2009 and 2011. We estimated the total attributable government health care spending for every individual in all health care sectors. RESULTS: More than $30 billion in annual health expenditures, representing 75% of total government health care spending, was attributed to individual costs. One-third of high-cost users (individuals with the highest 5% of costs) in 2009 remained in this category in the subsequent 2 years. Most spending among high-cost users was for institutional care, in contrast to lower-cost users, among whom spending was predominantly for ambulatory care services. Costs were far more concentrated among children than among older adults. The most common reasons for hospital admissions among high-cost users were chronic diseases, infections, acute events and palliative care. INTERPRETATION: Although high health care costs were concentrated in a small minority of the population, these related to a diverse set of patient health care needs and were incurred in a wide array of health care settings. Improving the sustainability of the health care system through better management of high-cost users will require different tactics for different high-cost populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".