Ambulatory Care Fragmentation and Total Health Care Costs
Bibliographic record
Abstract
BACKGROUND: The magnitude of the relationship between ambulatory care fragmentation and subsequent total health care costs is unclear. OBJECTIVE: To determine the association between ambulatory care fragmentation and total health care costs. RESEARCH DESIGN: Longitudinal analysis of 15 years of data (2004-2018) from the national Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, linked to Medicare fee-for-service claims. SUBJECTS: A total of 13,680 Medicare beneficiaries who are 65 years and older. MEASURES: We measured ambulatory care fragmentation in each calendar year, defining high fragmentation as a reversed Bice-Boxerman Index ≥0.85 and low as <0.85. We used generalized linear models to determine the association between ambulatory care fragmentation in 1 year and total Medicare expenditures (costs) in the following year, adjusting for baseline demographic and clinical characteristics, a time-varying comorbidity index, and accounting for geographic variation in reimbursement and inflation. RESULTS: The average participant was 70.9 years old; approximately half (53%) were women. One-fourth (26%) of participants had high fragmentation in the first year of observation. Those participants had a median of 9 visits to 6 providers, with the most frequently seen provider accounting for 29% of visits. By contrast, participants with low fragmentation had a median of 8 visits to 3 providers, with the most frequently seen provider accounting for 50% of visits. High fragmentation was associated with $1085 more in total adjusted costs per person per year (95% CI $713 to $1457) than low fragmentation. CONCLUSIONS: Highly fragmented ambulatory care in 1 year is independently associated with higher total costs the following year.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".