How healthcare systems evaluate their advance care planning initiatives: Results from a systematic review
Bibliographic record
Abstract
BACKGROUND: Advance care planning initiatives are being implemented across healthcare systems around the world, but how best to evaluate their implementation is unknown. AIM: To identify gaps and/or redundancies in current evaluative strategies to help healthcare systems develop future evaluative frameworks for ACP. DESIGN: Systematic review. METHODS: Peer-reviewed and gray literature searches were conducted till February 2015 to answer: "What methods have healthcare systems used to evaluate implementation of advance care planning initiatives?" A PICOS framework was developed to identify articles describing the implementation and evaluation of a health system-level advance care planning initiative. Outcome measures were mapped onto a conceptual quality indicator framework based on the Institute of Medicine and Donabedian models of healthcare quality. RESULTS: A total of 46 studies met inclusion criteria for analysis. Most articles reported on single parts of a healthcare system (e.g. continuing care). The most common outcome measures pertained to document completion, followed by healthcare resource use. Patient-, family-, or healthcare provider-reported outcomes were less commonly measured. Concordance measures (e.g. dying in place of choice) were reported by only 26% of studies. The conceptual quality indicator framework identified gaps and redundancies in measurement and is presented as a potential foundation from which to develop a comprehensive advance care planning evaluation framework. CONCLUSION: Document completion is frequently used to evaluate advance care planning program implementation; capturing the quality of care appears to be more difficult. This systematic review provides health system administrators with a comprehensive summary of measures used to evaluate advance care planning and may identify gaps in evaluation within their local context.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".