Reducing time to X-ray in emergency department ambulatory patients: a quality improvement project
Bibliographic record
Abstract
This quality improvement project began when physicians and nurses at our hospital observed patients waiting for excessive periods of time for a porter to escort patients from the emergency department (ED) to medical imaging (MI). However, certain patients may not need staff escort and are able to ambulate from ED to MI by themselves. This would reduce waiting time from when the X-ray is ordered to X-ray being done, which may reduce overall ED length of stay and improve patients' experience.Our project aim is to decrease the time to X-ray by 50% within 6 months by having appropriate ambulatory patients walk from the ED to the X-ray department without a porter. To achieve our goal, several strategies were employed. First, brainstorm sessions were held to better understand the barriers and ways to implement the new process. Second, a patient survey was conducted to understand their thoughts on the change idea. Third, data were collected to assess the inefficiency problem on the number of times non-porter staff escorted patients due to porters being unavailable. A total of 14 PDSA (Plan-Do-Study-Act) cycles were completed between December 2018 and May 2019. A human factor specialist was consulted to examine the process for safety and optimisation of the patient journey.In our PDSA cycles, self-ambulatory patients were compared with ambulatory patients who required an escort. An improvement was found from time to X-ray of 28 min (11 min vs 39 min). The new self-ambulatory process was implemented in June 2019 on a daily basis.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".