Patient Comments and Patient Experience Ratings Are Strongly Correlated With Emergency Department Wait Times
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hospitals and clinicians increasingly are reimbursed based on quality of care through financial incentives tied to value-based purchasing. Patient-centered care, measured through patient experience surveys, is a key component of many quality incentive programs. We hypothesize that operational aspects such as wait times are an important element of emergency department (ED) patient experience. The objectives of this paper are to determine (1) the association between ED wait times and patient experience and (2) whether patient comments show awareness of wait times. METHODS: This is a cross-sectional observational study from January 1, 2019, to December 31, 2020, across 16 EDs within a regional health care system. Patient and operations data were obtained as secondary data through internal sources and merged with primary patient experience data from our data analytics team. Dependent variables are (1) the association between ED wait times in minutes and patient experience ratings and (2) the association between wait times in minutes and patient comments including the term wait (yes/no). Patients rated their "likelihood to recommend (LTR) an ED" on a 0 to 10 scale (categories: "Promoter" = 9-10, "Neutral" = 7-8, or "Detractor" = 0-6). Our aggregate experience rating, or Net Promoter Score (NPS), is calculated by the following formula for each distinct wait time (rounded to the nearest minute): NPS = 100* (# promoters - # detractors)/(# promoters + # neutrals + # detractors). Independent variables for patient age and gender and triage acuity, were included as potential confounders. We performed a mixed-effect multivariate ordinal logistic regression for the rating category as a function of 30 minutes waited. We also performed a logistic regression for the percentage of patients commenting on the wait as a function of 30 minutes waited. Standard errors are adjusted for clustering between the 16 ED sites. RESULTS: A total of 50 833 unique participants completed an experience survey, representing a response rate of 8.1%. Of these respondents, 28.1% included comments, with 10.9% using the term "wait." The odds ratio for association of a 30-minute wait with LTR category is 0.83 [0.81, 0.84]. As wait times increase, the odds of commenting on the wait increase by 1.49 [1.46, 1.53]. We show policy-relevant bubble plot visualizations of these two relationships. CONCLUSIONS: Patients were less likely to give a positive patient experience rating as wait times increased, and this was reflected in their comments. Improving on the factors contributing to ED wait times is essential to meeting health care systems' quality initiatives.
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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 | 0.002 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".