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Record W4400108321 · doi:10.1097/qmh.0000000000000460

Patient Comments and Patient Experience Ratings Are Strongly Correlated With Emergency Department Wait Times

2024· article· en· W4400108321 on OpenAlex
Diane Kuhn, Peter S. Pang, Benton R. Hunter, Paul I. Musey, Karl Y. Bilimoria, Xiaochun Li, Thomas Lardaro, Daniel B. Smith, Christian C. Strachan, S P Canfield, Patrick O. Monahan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality Management in Health Care · 2024
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsEmergency departmentMedical emergencyPsychologyMedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.351
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it