Data-driven insights into interhospital care fragmentation: Implications for health policy and equity among older adults
Bibliographic record
Abstract
OBJECTIVE: To determine factors leading to interhospital care fragmentation (ICF); evaluate how ICF affects rehospitalization costs, length of stays (LOS), and delayed discharge; and analyze ICF disparity among equity-seeking groups. MATERIALS AND METHODS: We used a 13-year retrospective cohort of older adults (65+) in Ontario, Canada. Utilizing multivariable logistic regression, we identified characteristics associated with ICF and determined its association with outcomes. RESULTS: Discharge to facilities except home and homecare and travel distance were the strongest risk factors for ICF. Patients were less likely to experience ICF if they were older, frail, or had multiple comorbidities. ICF was strongly associated with an increase in the daily costs of readmission. Moreover, the risks of a prolonged LOS after ICF and delayed discharge were higher among returning surgical patients. The rural residency was a source of health inequality. CONCLUSIONS: ICF exacerbates health disparities and worsens patient outcomes. Our study identified several risk factors associated with ICF, some of which are controllable, paving the way for interventions to mitigate this issue. To promote health equity and reduce adverse outcomes, policymakers should focus on policies for reducing care discontinuity, particularly addressing the controllable risk factors.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".