Assessing the Impact of Early Identification of Patients Appropriate for Palliative Care on Resource Use and Costs in the Final Month of Life
Bibliographic record
Abstract
PURPOSE: This study evaluates whether an intervention to identify Canadian patients eligible for a palliative approach changes the use of health care resources and costs within the final month of life. METHODS: Between 2014 and 2017, physicians identified 1,187 patients in family practice units and cancer centers who were likely to die within 1 year based on diagnosis, symptom assessment, and performance status. A multidisciplinary intervention that included activation of community resources and initiation of palliative planning was started. By using propensity-score matching, patients in the intervention group were matched 1:1 with nonintervention controls selected from provincial administrative data. We compared health care use and costs (using 2017 Canadian dollars) for 30 days before death between patients who died within the 1-year follow-up and matched controls. RESULTS: Groups (n = 629 in each group) were well-balanced in sociodemographic characteristics, comorbidities, and previous health care use. In the last 30 days, there was no differences in proportions between the two groups of patients regarding emergency department visits, intensive care unit admissions, or inpatient hospitalizations. However, patients in the intervention group had greater use of palliative physician encounters, community home care visits, and/or physician home visits (92.8% v 88.4%; P = .007). In the 507 pairs with cancer, more patients in the intervention group underwent chemotherapy (44% v 33%; P < .001) and radiation (18.7% v 3.2%; P = .043) in the last 30 days. Mean cost per patient was similar for the intervention group (mean, $17,231; 95% CI, $16,027 to $18,436) and for the control group (mean, $16,951; 95% CI, $15,899 to $18,004). CONCLUSION: Even with the limitations in our observational study design, identification of palliative patients did not significantly change overall costs but may shift resources toward palliative services.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".