Reducing patient waiting time and length of stay in an Acute Care Pediatric Emergency Department
Bibliographic record
Abstract
Prolonged waiting times and length of stay in Pediatric Emergency Department, are the two of the most challenging patient and clinical outcomes of healthcare institution. These emerged due to various reasons, namely: the use of triaging process and patient flow criteria that eventually lead to bottlenecks and overcrowding in the ED. After realizing the root causes of the prolonged waiting times and length of stay, the KASCH ED instigated a team to study the factors and thereby arrive at a considerable conclusion that will result in an improvement. The quality improvement project was initiated and steps were undertaken to improve the flow, reduce the waiting times, and reduce the overcrowding in Pediatric Emergency Acute Care Unit. The primary cause identified was inadequate team awareness and lack of the ED process flow, thus creating confusion as to where the type of patients based on the triage level will be assessed, managed and treated. Using the Canadian Triage and Acuity Scale (CTAS) as guide in triaging patients, a theory called Pediatric Rapid Assessment and Management (PRAM) was introduced in the Acute Care Unit. This certain model is basically aimed to rapidly assess and managed the patients who were triaged as Level III and Level IV within a period of 30 minutes. Several PDSA cycles were tested and implemented in order to assure that the process fit the criteria and the process flow will be improved. Following the completion of each cycle, significant improvements were noted, such as patients being assessed in Initial Assessment Room on average time less than the target of 15 minutes. In like manner, patients' length of stay on average less than 15 minutes in PRAM bed. The total time for assessment and plan of management is with a target time of less than 30 minutes. The team continuously drive th process and monitored the key performance indicators of the PRAM during the study period and subsequent improvement strategies were likewise implemented.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".