Association between high cost user status and end-of-life care in hospitalized patients: A national cohort study of patients who die in hospital
Bibliographic record
Abstract
Background:Studies comparing end-of-life care between patients who are high cost users of the healthcare system compared to those who are not are lacking.Aim:The objective of this study was to describe and measure the association between high cost user status and several health services outcomes for all adults in Canada who died in acute care, compared to non-high cost users and those without prior healthcare use.Settings and participants:We used administrative data for all adults who died in hospital in Canada between 2011 and 2015 to measure the odds of admission to the intensive care unit (ICU), receipt of invasive interventions, major surgery, and receipt of palliative care during the hospitalization in which the patient died. High cost users were defined as those in the top 10% of acute healthcare costs in the year prior to a person’s hospitalization in which they died.Results:Among 252,648 people who died in hospital, 25,264 were high cost users (10%), 112,506 were non-high cost users (44.5%) and 114,878 had no prior acute care use (45.5%). After adjustment for age and sex, high cost user status was associated with a 14% increased odds of receiving an invasive intervention, a 15% increased odds of having major surgery, and an 8% lower odds of receiving palliative care compared to non-high cost users, but opposite when compared to patients without prior healthcare use.Conclusions:Many patients receive aggressive elements of end-of-life care during the hospitalization in which they die and a substantial number do not receive palliative care. Understanding how this care differs between those who were previously high- and non-high cost users may provide an opportunity to improve end of life care for whom better care planning and provision ought to be an equal priority.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".