Ontario's Emergency Department Process Improvement Program: The Experience of Implementation
Bibliographic record
Abstract
OBJECTIVES: In recent years, Lean manufacturing principles have been applied to health care quality improvement efforts to improve wait times. In Ontario, an emergency department (ED) process improvement program based on Lean principles was introduced by the Ministry of Health and Long-Term Care as part of a strategy to reduce ED length of stay (LOS) and to improve patient flow. This article aims to describe the hospital-based teams' experiences during the ED process improvement program implementation and the teams' perceptions of the key factors that influenced the program's success or failure. METHODS: A qualitative evaluation was conducted based on semistructured interviews with hospital implementation team members, such as team leads, medical leads, and executive sponsors, at 10 purposively selected hospitals in Ontario, Canada. Sites were selected based, in part, on their changes in median ED LOS following the implementation period. A thematic framework approach as used for interviews, and a standard thematic coding framework was developed. RESULTS: Twenty-four interviews were coded and analyzed. The results are organized according to participants' experience and are grouped into four themes that were identified as significantly affecting the implementation experience: local contextual factors, relationship between improvement team and support players, staff engagement, and success and sustainability. The results demonstrate the importance of the context of implementation, establishing strong relationships and communication strategies, and preparing for implementation and sustainability prior to the start of the project. CONCLUSIONS: Several key factors were identified as important to the success of the program, such as preparing for implementation, ensuring strong executive support, creation of implementation teams based on the tasks and outcomes of the initiative, and using multiple communication strategies throughout the implementation process. Explicit incorporation of these factors into the development and implementation of future similar interventions in health care settings could be useful.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".