Partners for Kids Care Coordination: Lessons From the Field
Bibliographic record
Abstract
OBJECTIVE: The goal of this trial was to present a case study of care coordination for children and youth with special health care needs from an exclusively pediatric accountable care organization, and compare precare and postcare data on their use of inpatient and emergency department services. METHODS: This pre–post comparison of the health care utilization included a subset of 733 children enrolled in Partners for Kids care coordination funded through a delegation arrangement with several Medicaid managed care plans. We compared inpatient admissions, hospital bed days, 30-day hospital readmissions, and emergency department visits during the 6 months before their enrollment in the coordination program versus the 6 months after enrollment. RESULTS: Approximately 16 000 referrals to the Partners for Kids care coordination program were made for an estimated 12 000 children. A total of 3072 unique individual children were enrolled; the most common condition classification was mental, behavioral, and neurodevelopmental disorders (25% of enrolled children). Due to rapid turnover/churn in Medicaid managed care eligibility, the subset of children with continuous enrollment was limited to 733 children. Among this subset, the counts of inpatient admissions, bed days, and 30-day readmissions between the pre-enrollment and post-enrollment period decreased (P < .05). CONCLUSIONS: These results suggest that it is possible for an accountable care organization to reduce inpatient and emergency department utilization. Going forward, the most important tasks of the care coordination team are to overcome obstacles to referral and participation and to develop methods to achieve better measures of patient-reported outcomes.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".