An Active In-Home Physician Model of Palliative Care and Its Resulting Performance Indicators Related to Home Deaths, Unplanned Emergency Department Visits and Unplanned Hospital Admissions
Bibliographic record
Abstract
Background: Limited research has characterized team-based models of home palliative care and the outcomes of patients supported by these care teams. Case presentation: A retrospective case series describing care and outcomes of patients managed by the London Home Palliative Care Team between May 1, 2017 and April 1, 2019. Case management: The London Home Palliative Care (LHPC) Team care model is based upon 3 pillars: 1) physician visit availability 2) active patient-centered care with strong physician in-home presence and 3) optimal administrative organization. Case outcomes: In the 18 month study period, 354 patients received care from the London Home Palliative Care Team. Most significantly, 88.4% ( n = 313) died in the community or at a designated palliative care unit after prearranged direct transfer; no comparable provincial data is available. 21.2% ( n = 75) patients visited an emergency department and 24.6% ( n = 87) were admitted to hospital at least once in their final 30 days of life. 280 (79.1%) died in the community. These values are better than comparable provincial estimates of 62.7%, 61.7%, and 24.0%, respectively. Conclusion: The London Home Palliative Care (LHPC) Team model appears to favorably impact community death rate, ER visits and unplanned hospital admissions, as compared to accepted provincial data. Studies to determine if this model is reproducible could support palliative care teams achieving similar results.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".