A Case-Mix System for Adults with Developmental Disabilities
Bibliographic record
Abstract
Effective management of publicly funded services matches the provision of needed services with cost-efficient payment methods. Payment systems that recognize differences in care needs (eg, case-mix systems) allow for greater proportions of available funds to be directed to providers supporting individuals with more needs. We describe a new way to allocate funds spent on adults with intellectual disabilities (ID) as part of a system-wide Medicaid payment reform initiative in Arkansas. Analyses were based on population-level data for persons living at home, collected using the interRAI ID assessment system, which were linked to paid service claims. We used automatic interactions detection to sort individuals into unique groups and provide a standardized relative measure of the cost of the services provided to each group. The final case-mix system has 33 distinct final groups and explains 26% of the variance in costs, which is similar to other systems in health and social services sectors. The results indicate that this system could be the foundation for a future case-mix approach to reimbursement and stand the test of "fairness" when examined by stakeholders, including parents, advocates, providers, and political entities.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".