Patient Comments and Patient Experience Ratings Are Strongly Correlated With Emergency Department Wait Times
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».