Improving End-of-Life Communication and Decision Making: The Development of a Conceptual Framework and Quality Indicators
Bibliographic record
Abstract
CONTEXT: The goal of end-of-life (EOL) communication and decision making is to create a shared understanding about a person's values and treatment preferences that will lead to a plan of care that is consistent with these values and preferences. Improvements in communication and decision making at the EOL have been identified as a high priority from a patient and family point of view. OBJECTIVES: The purpose of this study was to develop quality indicators related to EOL communication and decision making. METHODS: We convened a multidisciplinary panel of experts to develop definitions, a conceptual framework of EOL communication and decision making, and quality indicators using a modified Delphi method. We generated a list of potential items based on literature review and input from panel members. Panel members rated the items using a seven-point Likert scale (1 = very little importance to 7 = extremely important) over four rounds of review until consensus was achieved. RESULTS: About 24 of the 28 panel members participated in all four rounds of the Delphi process. The final list of quality indicators comprised 34 items, divided into the four categories of our conceptual framework: Advance care planning (eight items), Goals of care discussions (13 items), Documentation (five items), and Organization/System aspects (eight items). Eleven items were rated "extremely important" (median score). All items had a median score of five (moderately important) or greater. CONCLUSION: We have developed definitions, a conceptual framework, and quality indicators that researchers and health care decision makers can use to evaluate and improve the quality of EOL communication and decision making.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".