A Collaborative Quality Improvement Model and Electronic Community of Practice to Support Sepsis Management in Emergency Departments: Investigating Care Harmonization for Provincial Knowledge Translation
Bibliographic record
Abstract
Emergency medicine departments within several organizations are now advocating the adoption of early intervention guidelines for patients with the signs and symptoms of sepsis. This proposed research will lead to a comprehensive understanding of how diverse emergency department (ED) sites across British Columbia (BC), Canada, engage in a quality improvement collaborative to lead to improvements in time-based process measures and clinical outcomes for septic patients in EDs. To address the challenge of sepsis management, in 2007, the BC Ministry of Health began working with emergency health professionals, including health administrators, to establish a provincial ED collaborative: Evidence to Excellence (E2E). The E2E initiative employs the Institute for Healthcare Improvement (IHI) model and is supported by a Web-based community of practice (CoP) in emergency medicine. It aims to (1) support clinicians in accessing and applying evidence to clinical practice in emergency medicine, (2) support system change and clinical process improvement, and (3) develop resources and strategies to facilitate knowledge translation and process improvement. Improving sepsis management is one of the central foci of the E2E initiative. The primary purpose of our research is to investigate whether the application of sepsis management protocols leads to improved time-based process measures and clinical outcomes for patients presenting to EDs with sepsis. Also, we seek to investigate the implementation of sepsis protocols among different EDs. For example: (1) How can sepsis protocols be harmonized among different EDs? (2) What are health professionals' perspectives on interprofessional collaboration with various EDs? and (3) What are the factors affecting the level of success among EDs? Lastly, working in collaboration with the BC Ministry of Health as our policy-maker partner, the research will investigate how the demonstrated efficacy of this research can be applied on a provincial and national level to establish a template for policy makers from other jurisdictions to translate knowledge into action for EDs. This research study will employ the IHI model for improvement, incorporate the principles of participatory action research, and use the E2E online CoP to engage ED practitioners (eg, physicians, nurses, and administrators, exchanging ideas, engaging in discussions, sharing resources, and amalgamating knowledge) from across BC to (1) share the evidence of early intervention in sepsis, (2) adapt the evidence to their patterns of practice, (3) develop a common set of orders for implementing the sepsis pathway, and (4) agree on common indicators to measure clinical outcomes. Our hypothesis is that combining the social networking ability of an electronic CoP and its inherent knowledge translation capacity with the structured project management of the IHI model will result in widespread and sustained improvement in the emergency and overall care of patients with severe sepsis presenting to EDs throughout BC.
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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 | 0.255 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| 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".