Who Are the High-Cost Users? A Method for Person-Centred Attribution of Health Care Spending
Bibliographic record
Abstract
OBJECTIVE: To develop person-centered episodes of care (PCE) for community-dwelling individuals in the top fifth percentile of Ontario health care expenditures in order to: (1) describe the main clinical groupings for spending; and (2) identify patterns of spending by health sector (e.g. acute care, home care, physician billings) within and across PCE. DATA SOURCES: Data were drawn from population-based administrative databases for all publicly funded health care in Ontario, Canada in 2010/11. STUDY DESIGN: This study is a retrospective cohort study. DATA COLLECTION/EXTRACTION METHODS: A total of 587,982 community-dwelling individuals were identified among those accounting for the top 5% of provincial health care expenditures between April 1, 2010 and March 31, 2011. PCE were defined as starting with an acute care admission and persisting through subsequent care settings and providers until individuals were without health system contact for 30 days. PCE were classified according to the clinical grouping for the initial admission. PCE and non-PCE costs were calculated and compared to provide a comprehensive measurement of total health system costs for the year. PRINCIPAL FINDINGS: Among this community cohort, 697,059 PCE accounted for nearly 70% ($11,815.3 million (CAD)) of total annual publicly-funded expenditures on high-cost community-dwelling individuals. The most common clinical groupings to start a PCE were Acute Planned Surgical (35.2%), Acute Unplanned Medical (21.0%) and Post-Admission Events (10.8%). Median PCE costs ranged from $3,865 (IQR = $1,712-$10,919) for Acute Planned Surgical to $20,687 ($12,207-$39,579) for Post-Admission Events. Inpatient acute ($8,194.5 million) and inpatient rehabilitation ($434.6 million) health sectors accounted for the largest proportions of allocated PCE spending over the year. CONCLUSIONS: Our study provides a novel methodological approach to categorize high-cost health system users into meaningful person-centered episodes. This approach helps to explain how costs are attributable within individuals across sectors and has applications in episode-based payment formulas and quality monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".