Effect of SMS Ward Round Notifications on Inpatient Experience in Acute Medical Settings: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Ward rounds are an essential component of inpatient care. Patient participation in rounds is increasingly encouraged, despite the occasional complicated circumstances, especially in acute care settings. Objective: This study aimed to evaluate the effect of real-time ward round notifications using SMS text messaging on the satisfaction of inpatients in an acute medical ward. Methods: Since January 2021, a service implementing real-time ward round notifications via text messaging (WR-SMS) has been operational at a tertiary-care medical center in Korea. To assess its impact, we conducted a retrospective cohort study of patients admitted to the acute medical unit who participated in a patient experience survey. Patient satisfaction was compared between patients admitted in 2020 (pre-WR-SMS group) and 2021 (post-WR-SMS group). Results: From January 2020 to December 2021, a total of 100 patients were enrolled (53 patients in the pre-WR-SMS group and 47 patients in the post-WR-SMS group). Compared with the pre-WR-SMS group, the post-WR-SMS group showed significantly greater satisfaction about being informed about round schedules (mean 3.43, SD 0.910 vs mean 3.89, SD 0.375; P<.001) and felt more emotionally supported during admission (mean 3.49, SD 0.800 vs mean 3.87, SD 0.397; P<.001). Regarding other questionnaire scores, the post-WR-SMS group showed an overall, although statistically insignificant, improvement compared with the pre-WR-SMS group. Conclusions: Real-time round notifications using a user-friendly SMS may improve inpatient satisfaction effectively.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".