Quality indicators for end-of-life care in the intensive care unit*
Bibliographic record
Abstract
OBJECTIVE: The primary goal of this study was to address the documented deficiencies in end-of-life care (EOLC) in intensive care unit settings by identifying key EOLC domains and related quality indicators for use in the intensive care unit through a consensus process. A second goal was to propose specific clinician and organizational behaviors and interventions that might be used to improve these EOLC quality indicators. PARTICIPANTS: Participants were the 36 members of the Robert Wood Johnson Foundation (RWJF) Critical Care End-of-Life Peer Workgroup and 15 nurse-physician teams from 15 intensive care units affiliated with the work group members. Fourteen adult medical, surgical, and mixed intensive care units from 13 states and the District of Columbia in the United States and one mixed intensive care unit in Canada were represented. METHODS: An in-depth literature review was conducted to identify articles that assessed the domains of quality of EOLC in the intensive care unit and general health care. Consensus regarding the key EOLC domains in the intensive care unit and quality performance indicators within each domain was established based on the review of the literature and an iterative process involving the authors and members of the RWJF Critical Care End-of-Life Peer Workgroup. Specific clinician and organizational behaviors and interventions to address the proposed EOLC quality indicators within the domains were identified through a collaborative process with the nurse-physician teams in 15 intensive care units. MEASUREMENTS AND MAIN RESULTS: Seven EOLC domains were identified for use in the intensive care unit: a) patient- and family-centered decision making; b) communication; c) continuity of care; d) emotional and practical support; e) symptom management and comfort care; f) spiritual support; and g) emotional and organizational support for intensive care unit clinicians. Fifty-three EOLC quality indicators within the seven domains were proposed. More than 100 examples of clinician and organizational behaviors and interventions that could address the EOLC quality indicators in the intensive care unit setting were identified. CONCLUSIONS: These EOLC domains and the associated quality indicators, developed through a consensus process, provide clinicians and researchers with a framework for understanding quality of EOLC in the intensive care unit. Once validated, these indicators might be used to improve the quality of EOLC by serving as the components of an internal or external audit evaluating EOLC continuous quality improvement efforts in intensive care unit settings.
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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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".