Hospital Utilization Among Children With the Highest Annual Inpatient Cost
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children who experience high health care costs are increasingly enrolled in clinical initiatives to improve their health and contain costs. Hospitalization is a significant cost driver. We describe hospitalization trends for children with highest annual inpatient cost (CHIC) and identify characteristics associated with persistently high inpatient costs in subsequent years. METHODS: Retrospective study of 265 869 children age 2 to 15 years with ≥1 admission in 2010 to 39 children's hospitals in the Pediatric Health Information System. CHIC were defined as the top 10% of total inpatient costs in 2010 (n = 26 574). Multivariate regression and regression tree modeling were used to distinguish individual characteristics and interactions of characteristics, respectively, associated with persistently high inpatient costs (≥80th percentile in 2011 and/or 2012). RESULTS: The top 10% most expensive children (CHIC) constituted 56.9% ($2.4 billion) of total inpatient costs in 2010. Fifty-eight percent (n = 15 391) of CHIC had no inpatient costs in 2011 to 2012, and 27.0% (n = 7180) experienced persistently high inpatient cost. Respiratory chronic conditions (odds ratio [OR] = 3.0; 95% confidence interval [CI], 2.5-3.5), absence of surgery in 2010 (OR = 2.0; 95% CI, 1.8-2.1), and technological assistance (OR = 1.6; 95% CI, 1.5-1.7) were associated with persistently high inpatient cost. In regression tree modeling, the greatest likelihood of persistence (65.3%) was observed in CHIC with ≥3 hospitalizations in 2010 and a chronic respiratory condition. CONCLUSIONS: Most children with high children's hospital inpatient costs in 1 year do not experience hospitalization in subsequent years. Interactions of hospital use and clinical characteristics may be helpful to determine which children will continue to experience high inpatient costs over time.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".