A Case-Mix System for Children and Youth With Developmental Disabilities
Bibliographic record
Abstract
Limited funding across health and social service programs presents a challenge regarding how to best match resources to the needs of the population. There is increasing consensus that differences in individual characteristics and care needs should be reflected in variations in service costs, which has led to the development of case-mix systems. The present study sought to develop a new approach to allocate resources among children and youth with intellectual and developmental disabilities (IDD) as part of a system-wide Medicaid payment reform initiative in Arkansas. To develop the system, assessment data collected using the interRAI Child and Youth Mental Health-Developmental Disability instrument was matched to paid service claims. The sample consisted of 346 children and youth with developmental disabilities in the home setting. Using automatic interactions detection, individuals were sorted into unique, clinically relevant groups (ie, based on similar resource use) and a standardized relative measure of the cost of services provided to each group was calculated. The resulting case-mix system has 8 distinct, final groups and explains 30% of the variance in per diem costs. Our analyses indicate that this case-mix classification system could provide the foundation for a future prospective payment system that is centered around stability and equitability in the allocation of limited resources within this vulnerable population.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".