How Might We Have Known? Using Administrative Data to Predict 30-Day Hospital Readmission in Clients Receiving Home Care Services from 2018 to 2021
Bibliographic record
Abstract
Background: Reducing hospital readmissions can improve individual health outcomes and lower system-level costs. This study aimed to understand the characteristics of home care Personal Support clients who experienced a hospital admission (ie, hospital hold) and to identify factors that predict hospital readmission within 30 days of resuming home care Personal Support services. Methods: We conducted a retrospective cohort study using client administrative data from a home healthcare provider organization (2018-2021). The sample included clients (⩾18 years) who received publicly funded Personal Support services and experienced a hospital hold. Descriptive statistics and a binary logistic regression model analyzed the relationship between demographics, hospital service utilization, home care service utilization, and contextual factors on the outcome of 30-day hospital readmission. Results: Approximately 17% (n = 662) of all clients with a hospital hold (n = 3992) were readmitted to hospital within 30 days. Compared with non-readmitted clients, those with greater home care Personal Support service intensity after the index hospital hold were less likely to experience a hospital 30-day readmission. In contrast, those with greater acuity, higher assessed care needs, more hospital holds overall, more extended hospital stays (⩾2 weeks), and lower social support had a higher likelihood of 30-day hospital readmission. Conclusion: The findings from this study provide a greater understanding of factors associated with home care clients' risk of hospital readmission within 30 days and can be used to inform targeted, evidence-based support to reduce home care clients' hospital readmissions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".