Assessing the impact of an integrated care system on the healthcare expenditures of children with special healthcare needs.
Bibliographic record
Abstract
OBJECTIVES: The Children's Medical Services Network, a carved-out fee-for-service healthcare system for Florida's children with special healthcare needs (CSHCN), chose to develop an integrated care system (ICS) for its enrollees. The goals of this study were to analyze the effects of a managed care program on the Medicaid expenditures of CSHCN and to evaluate the performance of econometric models used to analyze healthcare expenditures. STUDY DESIGN: We used administrative data from 3947 CSHCN enrolled in Florida's Medicaid program between 2006 and 2008 for 2 treatment and 2 control counties. The 2 treatment counties were subject to the new managed care ICS. METHODS: To account for the unique nature of healthcare expenditures data, 5 econometric models were constructed. Using a difference-in-differences approach, these models were used to estimate differences in healthcare expenditures between CSHCN in the reform and control counties. RESULTS: The ICS program decreased outpatient, inpatient, pharmacy, and total costs. These effects were statistically significant for 1 of the reform counties. Emergency department costs increased slightly, though not significantly. Among the econometric models, the generalized linear models outperformed the ordinary least squares regressions. CONCLUSIONS: This analysis provides evidence that managed care programs such as Florida's ICS have the potential to reduce healthcare expenditures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".