Estimating the Cost of Alternate Level of Care When It Is Inextricably Linked to the Cost of Acute Care: A Canadian Example
Bibliographic record
Abstract
In Canada, hospitals designate patients as Alternate Level of Care (ALC) after they have completed all the necessary treatments and are ready for discharge, but remain in the hospitals and await transfer to an appropriate destination, such as a facility-based long-term care bed, home with care services, or palliative care bed. Provincial governments fund acute care in hospitals. However, hospitals have to divert funds to serve ALC patients. In 2019-20, ALC accounted for 19.31% of total bed-days. Yet, there is no comprehensive estimate of the cost of ALC. Therefore, the objective is to estimate the ALC cost, which is challenging, as the cost data for ALC days is lacking. However, the hospitalization cost (acute care plus ALC costs) and the number of acute and ALC days are available. Applying the log-log regression model with interaction terms between provinces and the natural logarithm of ALC length-of-stay to the hospital discharge data, supplemented by hospitalization cost data, the cost elasticity of ALC length-of-stay was estimated for each province. Then, the estimated cost elasticity, average hospitalization cost, average ALC length-of-stay, and total ALC bed-days for each province were utilized to estimate the province-specific cost of ALC in Canada. Summing these costs across provinces, the total expenditure for ALC services in Canadian provinces was estimated at $2.48 billion in 2019-20. This funding could potentially be redirected to improve value for money and enable timely acute care. Additionally, the study identified key diagnoses driving ALC costs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".