Factors Associated with Alternate Level of Care Status Designation: a Case-Control Study and Model to Optimize Care Trajectories
Bibliographic record
Abstract
Background As health-care demand is growing, our health-care system will require the optimization of the care trajectories. Patients with an alternate level of care (ALC) status could be a target for flow optimization. We aimed to characterize ALC patients and risk factors for ALC status, and to propose an integrated model to analyze the trajectory of ALC patients and discuss solutions to reduce their burden. Methods A case-control design was used to compare 60 ALC and 60 non-ALC patients admitted to the geriatric unit of the Centre hospitalier de l’Université de Montréal in 2021, collecting medical and sociodemographic data. Based on our model, univariate statistical analyses were computed to compare groups and identify risk factors for ALC status. Results ALC patients were less independent (22% performed five to six activities of daily living vs. 43%, p = .03). Both groups were comparable in terms of mobility and neurocognitive disorders. ALC patients were more likely to receive a new diagnosis of a neurocognitive disorder or new behavioural or psychological symptoms (37% vs. 15%, p = .008). Up to 25% of ALC patients were admitted despite presenting no active medical condition (vs. 3% of non-ALC patients, p = .002). Conclusions The optimization of the care trajectory of ALC patients is mainly based on pre-hospital and post-hospital factors. A proportion of ALC admissions might be avoidable with additional investment in home care resources and relocation procedures. Fluidity of ALC trajectory may benefit from improved orientation at discharge procedures. Full optimization of ALC trajectories requires a systemic understanding of the health-care system.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".