A Multidisciplinary Home Visiting Program for Children With Medical Complexity
Bibliographic record
Abstract
OBJECTIVES: Given the high needs and costs associated with the care of children with medical complexity (CMC), innovative models of care are needed. Home-visiting care models are effective in subpopulations of pediatrics and medically complex adults, but there is no literature on this model for CMC. We describe the development and outcomes of a multidisciplinary program that provides comprehensive home-based primary care for CMC. METHODS: Medical records from our institution were reviewed for patients enrolled in our program from July 2013 through March 2019. Demographics, clinical characteristics, and health care use were collected. We compared the differences in pre- and postprogram enrollment health care use using Wilcoxon signed rank test. We applied Cox proportional hazard models to examine the association between the time-dependent postenrollment health care use and numbers of home visits. We collected total claims data for a subset of our patients to examine total costs of care. RESULTS: We reviewed data collected from 121 patients. With our findings, we demonstrate that enrollment in our program is associated with reductions in average length of stay. More home visits were associated with decreased emergency department visits and hospitalizations. We also observed in patients with available cost data that total costs of care decreased after enrollment into the program. CONCLUSIONS: Our model has the potential to improve health outcomes and be financially sustainable by providing home-based primary care to CMC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".