Individual Survey Items in Emergency Department Patient Experience Surveys Have Limited Ability to Distinguish Unique Aspects of Care.

Kuhn, Diane; Harle, Christopher A; Monahan, Patrick O; Schenkel, Stephen M · Ann Emerg Med · 2026

cross_sectional · Level IV

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Abstract

Emergency department (ED) patient experience surveys, such as the Emergency Department Consumer Assessment of Healthcare Providers and Systems (ED CAHPS), NRCHealth, and Press Ganey, often include more than or equal to 10 items designed to measure distinct aspects of patient-centered care. However, it is unclear whether responses reflect unique constructs or primarily represent patients' overall experience. Our objective is to determine the extent to which ED patient experience survey items capture distinct constructs and examine the association between constructs and clinical/operational factors. We conducted a cross-sectional study of NRCHealth ED patient experience surveys collected from 13 EDs within a large regional health system between January 2022 and December 2023. Survey responses were merged with electronic health record data, including patient demographics, wait times, hallway bed placement, initial and change in pain scores, and ED crowding. Exploratory factor analysis using tetrachoric correlations was performed to assess dimensionality of very positive survey responses, which are referred to as "top-box" responses. Logistic regression was used to estimate associations between individual survey items and clinical and operational predictors. Among 58,523 respondents, factor analysis demonstrated that survey items loaded strongly (0.83 to 0.96) on a single underlying factor. Logistic regression showed that individual items had similar associations with operational factors, particularly hallway bed placement, wait times, and ED crowding, despite measuring conceptually distinct aspects of care. ED patient experience survey items may reflect overall experience rather than distinct constructs. Shorter surveys or alternative formats, such as incorporating free-text responses with natural language processing, may improve the efficiency and interpretability of patient experience measurement.

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