Establishing research priorities for patient safety in emergency medicine: a multidisciplinary consensus panel
Bibliographic record
Abstract
BACKGROUND: Patient safety in the context of emergency medicine is a relatively new field of study. To date, no broad research agenda for patient safety in emergency medicine has been established. The objective of this study was to establish patient safety-related research priorities for emergency medicine. These priorities would provide a foundation for high-quality research, important direction to both researchers and health-care funders, and an essential step in improving health-care safety and patient outcomes in the high-risk emergency department (ED) setting. METHODS: A four-phase consensus procedure with a multidisciplinary expert panel was organized to identify, assess, and agree on research priorities for patient safety in emergency medicine. The 19-member panel consisted of clinicians, administrators, and researchers from adult and pediatric emergency medicine, patient safety, pharmacy, and mental health; as well as representatives from patient safety organizations. In phase 1, we developed an initial list of potential research priorities by electronically surveying a purposeful and convenience sample of patient safety experts, ED clinicians, administrators, and researchers from across North America using contact lists from multiple organizations. We used simple content analysis to remove duplication and categorize the research priorities identified by survey respondents. Our expert panel reached consensus on a final list of research priorities through an in-person meeting (phase 3) and two rounds of a modified Delphi process (phases 2 and 4). RESULTS: After phases 1 and 2, 66 unique research priorities were identified for expert panel review. At the end of phase 4, consensus was reached for 15 research priorities. These priorities represent four themes: (1) methods to identify patient safety issues (five priorities), (2) understanding human and environmental factors related to patient safety (four priorities), (3) the patient perspective (one priority), and (4) interventions for improving patient safety (five priorities). CONCLUSION: This study established expert, consensus-based research priorities for patient safety in emergency medicine. This framework could be used by researchers and health-care funders and represents an essential guiding step towards enhancing quality of care and patient safety in the ED.
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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 | 0.374 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".