OP53 A systematic review of economic evaluations of advance care planning: data limitations and ethical considerations
Bibliographic record
Abstract
Background Evidence regarding the degree and direction of economic impacts of implementing Advance Care Planning (ACP) is inconsistent. Also, available reviews have not systematically assessed the quality of the costing data in the primary studies. We aimed to synthesize current evidence on the economic impacts of implementing ACP and explore implications for policy and practice. Methods We conducted a comprehensive search of online bibliographic databases. Reference lists of included articles were also reviewed. We assessed the quality of costing in studies using the Consensus on Health Economics Criteria Checklist (CHEC). Results We included 33 studies; the majority were from the USA (78.8%). Studies were conducted in various settings, mostly hospitals (60%). Almost 64% of studies reported cost savings from the healthcare systems’ perspectives; no study included patients’ perspectives (out-of-pocket-costs). Assessing quality of costing using CHEC revealed weaknesses in studies including: flaws with costs identification (37.9%), measurement (39.3%), and valuation (44.8%); no consideration of intervention costs (87.9%); not including all relevant variables in sensitivity analyses (34.5%); and not discounting the costs (55.6%). Discussion We detected substantial methodological issues with current economic evaluations of ACP that compromise the validity of evidence. To inform policy makers about ACP, which is a multifaceted process, methodologically robust studies are needed that capture costs of the program from all major payers. A comprehensive report on cost evaluations is highly recommended. Meanwhile, respecting patient choice remains a valid clinical basis for promoting use of ACP.
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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 | 0.415 | 0.741 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".