Stakeholder Engagement to Identify Priorities for Improving the Quality and Value of Critical Care
Bibliographic record
Abstract
BACKGROUND: Large amounts of scientific evidence are generated, but not implemented into patient care (the 'knowledge-to-care' gap). We identified and prioritized knowledge-to-care gaps in critical care as opportunities to improve the quality and value of healthcare. METHODS: We used a multi-method community-based participatory research approach to engage a Network of all adult (n = 14) and pediatric (n = 2) medical-surgical intensive care units (ICUs) in a fully integrated geographically defined healthcare system serving 4 million residents. Participants included Network oversight committee members (n = 38) and frontline providers (n = 1,790). Network committee members used a modified RAND/University of California Appropriateness Methodology, to serially propose, rate (validated 9 point scale) and revise potential knowledge-to-care gaps as priorities for improvement. The priorities were sent to frontline providers for evaluation. Results were relayed back to all frontline providers for feedback. RESULTS: Initially, 68 knowledge-to-care gaps were proposed, rated and revised by the committee (n = 32 participants) over 3 rounds of review and resulted in 13 proposed priorities for improvement. Then, 1,103 providers (62% response rate) evaluated the priorities, and rated 9 as 'necessary' (median score 7-9). Several factors were associated with rating priorities as necessary in multivariable logistic regression, related to the provider (experience, teaching status of ICU) and topic (strength of supporting evidence, potential to benefit the patient, potential to improve patient/family experience, potential to decrease costs). CONCLUSIONS: A community-based participatory research approach engaged a diverse group of stakeholders to identify 9 priorities for improving the quality and value of critical care. The approach was time and cost efficient and could serve as a model to prioritize areas for research quality improvement across other 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 | 0.185 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".