Managing Access and Flow through Appropriate Discharge: Preventing Common Errors and Improving Processes
Bibliographic record
Abstract
Increased pressure on acute care hospitals to move patients seamlessly through the healthcare system has resulted in more attention to the process of discharging patients, particularly seniors, from hospitals. When alignment with the Health Care Consent Act is lacking, errors can occur in the process. Examples of mistakes by healthcare professionals include these: taking direction from the wrong substitute decision-maker (SDM); taking direction from a family member when the patient is capable; allowing an SDM to make an advance directive on behalf of a patient; being aware of a known prior expressed wish but ignoring that wish when considering a placement plan; waiting for an SDM who is not available, willing and capable instead of proceeding down the hierarchy of decision-makers; or permitting families to propose discharge plans. Such errors have the potential to compromise quality of care, but they also work to prevent timely and appropriate discharge. In order to minimize these common errors in the consent process for placements, we have proposed a checklist to help meet ethical and legal obligations in the discharge process. We suggest the checklist may minimize avoidable conflict and misunderstanding and promote a seamless discharge process.
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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 | 0.152 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".