The Impact of the Healthcare Environment on Patient Experience in the Emergency Department: A Systematic Review to Understand the Implications for Patient-Centered Design
Bibliographic record
Abstract
BACKGROUND: The emergency department (ED) is a complex, volatile, and limited-resource healthcare setting. Many environmental factors, including high patient volumes, overburdened staff, long waits, and a tense atmosphere, converge in the ED. The objective of this study was to perform a systematic review of extant literature to understand how the ED environment drives patient experience and identify methodological or empirical insights for patient-centered ED design. METHODS: We searched eight academic databases (Web of Science, PubMed, Scopus, Medline [Ovid], CINAHL, PyscInfo, Compendex, and IEEE Explore) to identify studies that employed observational (descriptive) or interventional (evaluative) methodology. We performed a co-citation analysis of potentially eligible articles and a qualitative synthesis of findings from studies included in our final sample. RESULTS: Our search yielded 117 records. Of the 35 potentially relevant articles, 18 were published in the last 5 years, and 50% were authored by investigators in the United States. We used 33 articles for a co-citation analysis, revealing three interdisciplinary clusters and promising potential for collaboration across fields. Thirty articles were subjected to a full-text analysis, resulting in the identification of three overarching dimensions linking the ED environment to patient experience. CONCLUSION: The most commonly identified factors influencing patient experience in the ED included overcrowding and wait times, privacy, and communication; however, existing literature is limited. More research is needed to understand how ED environments configure patient experience and can be improved through design. Particularly, there is little research on participatory interventional strategies in the ED, despite strong evidence suggesting a need for stakeholder participation.
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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 | 0.031 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".