Patterns and Costs of Health Care Use of Children With Medical Complexity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Health care use of children with medical complexity (CMC), such as those with neurologic impairment or other complex chronic conditions (CCCs) and those with technology assistance (TA), is not well understood. The objective of the study was to evaluate health care utilization and costs in a population-based sample of CMC in Ontario, Canada. METHODS: Hospital discharge data from 2005 through 2007 identified CMC. Complete health system use and costs were analyzed over the subsequent 2-year period. RESULTS: The study identified 15 771 hospitalized CMC (0.67% of children in Ontario); 10 340 (65.6%) had single-organ CCC, 1063 (6.7%) multiorgan CCC, 4368 (27.6%) neurologic impairment, and 1863 (11.8%) had TA. CMC saw a median of 13 outpatient physicians and 6 distinct subspecialists. Thirty-six percent received home care services. Thirty-day readmission varied from 12.6% (single CCC without TA) to 23.7% (multiple CCC with TA). CMC accounted for almost one-third of child health spending. Rehospitalization accounted for the largest proportion of subsequent costs (27.2%), followed by home care (11.3%) and physician services (6.0%). Home care costs were a much larger proportion of costs in children with TA. Children with multiple CCC with TA had costs 3.5 times higher than children with a single CCC without TA. CONCLUSIONS: Although a small proportion of the population, CMC account for a substantial proportion of health care costs. CMC make multiple transitions across providers and care settings and CMC with TA have higher costs and home care use. Initiatives to improve their health outcomes and decrease costs need to focus on the entire continuum of care.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".