Experience of Emergency Department Patients With Using the Talking Pole Device: Prospective Interventional Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Patient engagement is important. However, it can be difficult in emergency departments (EDs). OBJECTIVE: The aim of this study was to evaluate the satisfaction of ED patients using a patient-friendly health information technology (HIT) device, the "Talking Pole," and to assess the factors relevant to their satisfaction. METHODS: This study was conducted in May 2017 at the ED of a tertiary hospital. The "Talking Pole" is a smartphone-based device attached to a intravenous infusion pole with sensors. It is capable of sensing patient movement and fluid dynamics. In addition, it provides clinical information from electronic medical records to patients and serves as a wireless communication tool between patients and nurses. Patients and caregivers who entered the observation room of the ED were selected for the study. The "Talking Pole" devices were provided to all participants, regardless of their need for an intravenous pole upon admittance to the ED. After 2 hours, each participant was given an 18-item questionnaire created for this research, measured on a 5-point Likert scale, regarding their satisfaction with "Talking Pole." RESULTS: Among 52 participants recruited, 54% (28/52) were patients and the remaining were caregivers. In total, 38% (20/52) were male participants; the average age was 54.6 (SD 12.9) years, and 63% (33/52) of the participants were oncology patients and their caregivers. The overall satisfaction rate was 4.17 (SD 0.79 ) points. Spearman correlation coefficient showed a strong association of "overall satisfaction" with "comparison to the previous visit" (ρ=.73 ), "perceived benefit" (ρ=.73), "information satisfaction" (ρ=.70), and "efficiency" (ρ=.70). CONCLUSIONS: In this study, we introduced a patient-friendly HIT device, the "Talking Pole." Its architecture focused on enhancing information delivery, which is regarded as a bottleneck toward achieving patient engagement in EDs. Patient and caregiver satisfaction with the "Talking Pole" was positive in the ED environment. In particular, correlation coefficient results improved our understanding about patients' satisfaction, HIT devices, and services used in the ED.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".