Quality improvement initiative for improved patient communication in an ED rapid assessment zone
Bibliographic record
Abstract
INTRODUCTION: Patient-clinician communication in the Emergency Department (ED) faces challenges of time and interruptions, resulting in negative effects on patient satisfaction with communication and failure to relieve anxiety. Our aim was to improve patient satisfaction with communication and to decrease related patient anxiety. METHODS: A multistage quality improvement (QI) initiative was conducted in the ED of Toronto General Hospital, a quaternary care centre in Ontario, Canada, from January to May 2018. We engaged stakeholders widely including clinicians, allied health and patients. We developed a 5-point Likert scale survey to measure patient and clinician rating of their communication experience, along with open-ended questions, and a patient focus group. Inductive analyses yielded interventions that were introduced through three Plan-Do-Study-Act (PDSA) cycles: (1) a clinician communication tool called Acknowledge-Empathize-Inform; (2) patient information pamphlets; and (3) a multimedia solution displaying patient-directed material. Our primary outcome was to improve patient satisfaction with communication and decrease anxiety by at least one Likert scale point over 6 months. Our secondary outcome was clinician-perceived interruptions by patients. We used statistical process control (SPC) charts to identify special cause variation and two-tailed Mann-Whitney U tests to compare means (statistical significance p<0.05). RESULTS: A total of 232 patients and 104 clinicians were surveyed over baseline and three PDSA cycles. Communication about wait times, ED process, timing of next steps and directions to patient areas were the most frequently identified gaps, which informed our interventions. Measurements at baseline and during PDSA 3 showed: patient satisfaction increased from 3.28 (5 being best; n=65) to 4.15 (n=59, p<0.0001). Patient anxiety decreased from 2.96 (1 being best; n=65) to 2.31 (n=59, p<0.001). Clinician-perceived interruptions by patients changed from 4.33 (5 being highest; n=30) to 4.18 (n=11, p=0.98) and did not meet significance. SPC charts showed special cause variation temporally associated with our interventions. CONCLUSIONS: Our pragmatic low-cost QI initiative led to statistically significant improvement in patient satisfaction with communication and decreased patient anxiety while narrowly missing our a priori improvement aim of one full Likert scale point.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".