Comparison of Methods to Define High Use of Inpatient Services Using Population‐Based Data
Bibliographic record
Abstract
BACKGROUND: A variety of methods have been proposed to define "high users" of inpatient services, which may have implications for targeting subgroups for intervention. OBJECTIVE: To compare 3 common definitions of high inpatient service use and their influence on patient capture, outcomes, and inpatient burden. DESIGN, SETTING, PATIENTS: We defined "high use" based on the upper 5th percentile of the population by 3 definitions: (1) number of inpatient episodes (≥3 hospitalizations/year), (2) cumulative length of stay (≥56 days in hospital/year), and (3) cumulative cost based on hospitalization resource intensity weights (≥ $63,597 Canadian dollars/year). Clinical characteristics, health outcomes, and overall health burden were compared across definitions and stratified by age. RESULTS: Of that population, 10.3% of individuals were common to all definitions. High users based on number of inpatient episodes were more likely to be admitted for acute conditions, with most high users based on length of stay admitted for mental health-related conditions, while those based on costs were more likely to have hospitalizations resulting in death (9.3%). High-episode individuals accounted for 16.6% of all inpatient episodes, high-length of stay individuals for 46.4% of all hospital days, and high-cost individuals for 38.9% of total cost. CONCLUSIONS: Three definitions of high users of inpatient services captured significantly different groups of patients. This has implications for targeting subgroups for intervention and highlights important considerations for selecting the most suitable definition for a given objective.
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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 | 0.113 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".