Achieving wait time reduction in the emergency department
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide details on a study to determine the wait time and service time for various emergency department (ED) patient care processes and to apply the science of plan‐do‐study‐act (PDSA) cycles to improve patient flow. Design/methodology/approach The paper used direct observation to collect patient flow data on 1,728 patients at multiple ED sites in Saskatchewan, Canada. It calculated wait times and services associated with important care processes and then tested, measured and implemented ideas to reduce wait time. Findings On an average, patients spend nearly five hours in the ED with about one‐half of the visit devoted to waiting for the next required service to take place. Waiting for an inpatient bed, specialist consultation or physician reassessment comprised relatively long wait times. Through the use of visual reminders and standard process worksheets, quality improvement teams were able to achieve large reductions in physician reassessment waiting time. These improvements required minimal materials cost and no additional staff. Research limitations/implications The case study featured EDs within a particular Canadian province, so may not be generalizeable to other settings. We only sampled a fraction of ED patients at each facility. Practical implications Admitted patients waiting for a hospital bed represent a key contributor to ED congestion. PDSA cycles are a valuable approach to achieving quality improvement in health care. Originality/value The paper fulfils an identified need by breaking down an ED patient's waiting time into several high‐level processes. It also applies improvement science to ED patient flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".