A Pilot of Digital Whiteboards for Improving Patient Satisfaction in the Emergency Department: Nonrandomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Electronic paper (E-paper) screens use electrophoretic ink to provide paper-like low-power displays with advanced networking capabilities that may potentially serve as an alternative to traditional whiteboards and television display screens in hospital settings. E-paper may be leveraged in the emergency department (ED) to facilitate communication. Providing ED patient status updates on E-paper screens could improve patient satisfaction and overall experience and provide more equitable access to their health information. OBJECTIVE: We aimed to pilot a patient-facing digital whiteboard using E-paper to display relevant orienting and clinical information in real time to ED patients. We also sought to assess patients' satisfaction after our intervention and understand our patients' overall perception of the impact of the digital whiteboards on their stay. METHODS: We deployed a 41-inch E-paper digital whiteboard in 4 rooms in an urban, tertiary care, and academic ED and enrolled 110 patients to understand and evaluate their experience. Participants completed a modified Hospital Consumer Assessment of Health Care Provider and Systems satisfaction questionnaire about their ED stay. We compared responses to a matched control group of patients triaged to ED rooms without digital whiteboards. We designed the digital whiteboard based on iterative feedback from various departmental stakeholders. After establishing IT infrastructure to support the project, we enrolled patients on a convenience basis into a control and an intervention (digital whiteboard) group. Enrollees were given a baseline survey to evaluate their comfort with technology and an exit survey to evaluate their opinions of the digital whiteboard and overall ED satisfaction. Statistical analysis was performed to compare baseline characteristics as well as satisfaction. RESULTS: After the successful prototyping and implementation of 4 digital whiteboards, we screened 471 patients for inclusion. We enrolled 110 patients, and 50 patients in each group (control and intervention) completed the study protocol. Age, gender, and racial and ethnic composition were similar between groups. We saw significant increases in satisfaction on postvisit surveys when patients were asked about communication regarding delays (P=.03) and what to do after discharge (P=.02). We found that patients in the intervention group were more likely to recommend the facility to family and friends (P=.04). Additionally, 96% (48/50) stated that they preferred a room with a digital whiteboard, and 70% (35/50) found the intervention "quite a bit" or "extremely" helpful in understanding their ED stay. CONCLUSIONS: Digital whiteboards are a feasible and acceptable method of displaying patient-facing data in the ED. Our pilot suggested that E-paper screens coupled with relevant, real-time clinical data and packaged together as a digital whiteboard may positively impact patient satisfaction and the perception of the facility during ED visits. Further study is needed to fully understand the impact on patient satisfaction and experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT04497922; https://clinicaltrials.gov/ct2/show/NCT04497922.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".