The Demography and Policies of Alternate Levels of Care: A Selection of Canadian Case Studies
Bibliographic record
Abstract
Alternate Level of Care [ALC] is a serious and costly issue that leads to hospital gridlock, driving inefficiency in delivery of health care services. At any given time, 10-20% of hospital beds in Canada are being occupied by patients who do not need the level of intensive care that hospitals are designated for (McGilton et al. 2021). These patients are unable to return home as they need some form of assistance or relocation to a care facility which is not yet available and become designated an ALC patient. While the Canadian context is unique, ALC is not solely a Canadian problem, nor solely a problem plaguing countries who practice publicly funded health care (which can further be publicly or privately delivered). Similar issues impact every health system in different ways, leading to reduced health outcomes and inefficient health services due to the gridlock and inaccessibility it creates. Many nations, including provinces (or health regions within provinces) in Canada have begun to implement policies and procedures targeted at reducing ALC rates and improving patient flow through the health system such as ALC avoidance frameworks, patient flow guides, waitlist management and financial incentives. Analyzing ALC data can inform policymakers about the potential trends in data and how different policies and procedures that are in place may have an impact on changing ALC rates. In trying to best understand the status of ALC in our hospitals it is best to gather data that illustrates how lengths of stay [LOS] have changed, how ALC rates differ by gender and age, what clinical category the patient falls under, where ALC patients are awaiting discharge too, and what kind of support patients are waiting for to be set up. This study looks at what policies and procedures have been implemented across Alberta, Ontario, and Saskatchewan, as well as analyzing how changes in ALC hospitalizations have changed over the five-year period of 2014-2018. While it is hard to ascribe changes in data to the specific policy implementation due to the numerous factors such as overall population health and overall age that are not examined in this study, understanding trends over time and across provinces related to ALC hospitalizations can be informative in aiding design of ALC policies and procedures. Ontario demonstrates the greatest number of policies and procedures in this study that have been implemented in different health regions within the province in attempts to reduce ALC rates. Governed by one single health authority respectively, Alberta and Saskatchewan have had much fewer policies or methods aimed at reducing ALC rates. The data highlights that ALC rates in terms of ALC hospitalizations are continuing to rise in Alberta and Saskatchewan while exhibiting slower changes in Ontario in the 2014-2018 period. All three province face high rates of patients needing ALC due to inadequate and inaccessible community services such as home care supports and long-term care facilities able to provide these patients more appropriate services outside of hospital. All provinces demonstrate that many ALC patients in hospital are admitted due to the ingestion of toxic drugs and/or poisons, high instances of psychosocial and mental diseases and disorders, and chronic diseases of the body’s systems such as those of the neurological system (such as dementia, Parkinson’s, and epilepsy). To truly impact and reduce ALC rates, regions must move to introduce and implement policies that work together to minimize and avoid ALC hospitalizations and improve patient flow. No one policy or procedure on its own is likely to have substantial effects on ALC rates. A multi-faceted approach using a combination of various methods and policies (with incremental improvement over time and adaption to changing conditions) is necessary to combat ALC issues from all angles to address the different challenges it creates.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".