Patients’, nurses’ and physicians’ perception of delays in emergency department care
Bibliographic record
Abstract
Background: Patients often judge their experiences in the emergency department (ED) based upon how long they have to wait, the attitudes of staff, and the information provided them. Objective: The objective of this study was to assess the causes in constraints to patient flow in emergency departments by comparing staff, patient, and DSS data findings. Methods: A random sample of patients and their healthcare providers were administered a survey asking them to rank the reasons for delay during three points after triage (60, 120, 180 minutes). A comparison was then done using Spearman’s rank correlations and a regression model with independent indicators collected from the hospitals Decision Support System (DSS) which included: time to be seen by doctors, time to laboratory test results, time for radiological results, wait time for hospital bed and discharge in order to compare if the perceptions of constraints are related to the actual reasons for delays in the ED. This study was approved by the Internal Review Board. Results: There was a significant correlation in the ranking of the reason for delays within the first, second and third hours between patients, nurses and doctors. However, when comparing perceptions for delay and independent data, only nurses within the third hour were correct in their understanding of the constraints that lead to delays. Conclusions: Overall, patients and staff view similar reasons for constraints to their timely flow through the ED. There is, however, very little correlation between the survey responses and the independent factors that did constrain the flow of the ED. A more extensive use and integration of the DSS system by staff could provide more reliable information for reasons for delay that could be communicated to the ED patients which could improve customer service.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".