Cost impact of introducing a treatment escalation/limitation plan during patients’ last hospital admission before death
Bibliographic record
Abstract
OBJECTIVE: A recent study found that the use of a treatment escalation/limitation plan (TELP) was associated with a significant reduction in non-beneficial interventions (NBIs) and harms in patients admitted acutely who subsequently died. We quantify the economic benefit of the use of a TELP. DESIGN: NBIs were micro-costed. Mean costs for patients with a TELP were compared to patients without a TELP using generalized linear model regression, and results were extrapolated to the Scottish population. SETTING: Medical, surgical and intensive care units of district general hospital in Scotland, UK. PARTICIPANTS: Two hundred and eighty-seven consecutive patients who died over 3 months in 2017. Of these, death was 'expected' in 245 (85.4%) using Gold Standards Framework criteria. INTERVENTION: Treatment escalation/limitation plan. MAIN OUTCOME MEASURE: Between-group difference in estimated mean cost of NBIs. RESULTS: The group with a TELP (n = 152) had a mean reduction in hospital costs due to NBIs of GB £220.29 (US $;281.97) compared to those without a TELP (n = 132) (95% confidence intervals GB £323.31 (US $413.84) to GB £117.27 (US $150.11), P = <0.001). Assuming that a TELP could be put in place for all expected deaths in Scottish hospitals, the potential annual saving would be GB £2.4 million (US $3.1 million) from having a TELP in place for all 'expected' deaths in hospital. CONCLUSIONS: The use of a TELP in an acute hospital setting may result in a reduction in costs attributable to NBIs.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".