Reconciling patient and provider priorities for improving the care of critically ill patients: A consensus method and qualitative analysis of decision making
Bibliographic record
Abstract
BACKGROUND: Providers have traditionally established priorities for quality improvement; however, patients and their family members have recently become involved in priority setting. Little is known about how to reconcile priorities of different stakeholder groups into a single prioritized list that is actionable for organizations. OBJECTIVE: To describe the decision-making process for establishing consensus used by a diverse panel of stakeholders to reconcile two sets of quality improvement priorities (provider/decision maker priorities n=9; patient/family priorities n=19) into a single prioritized list. DESIGN: We employed a modified Delphi process with a diverse group of panellists to reconcile priorities for improving care of critically ill patients in the intensive care unit (ICU). Proceedings were audio-recorded, transcribed and analysed using qualitative content analysis to explore the decision-making process for establishing consensus. SETTING AND PARTICIPANTS: Nine panellists including three providers, three decision makers and three family members of previously critically ill patients. RESULTS: Panellists rated and revised 28 priorities over three rounds of review and reached consensus on the "Top 5" priorities for quality improvement: transition of patient care from ICU to hospital ward; family presence and effective communication; delirium screening and management; early mobilization; and transition of patient care between ICU providers. Four themes were identified as important for establishing consensus: storytelling (sharing personal experiences), amalgamating priorities (negotiating priority scope), considering evaluation criteria and having a priority champion. CONCLUSIONS: Our study demonstrates the feasibility of incorporating families of patients into a multistakeholder prioritization exercise. The approach described can be used to guide consensus building and reconcile priorities of diverse stakeholder groups.
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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 | 0.001 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".