Home to Stay: A Randomized Controlled Trial Evaluating the Effect of a Postdischarge Mobile App to Reduce 30-Day Readmission Following Elective Colorectal Surgery
Bibliographic record
Abstract
OBJECTIVE: A randomized controlled trial was conducted to evaluate the effect of a postdischarge app on 30-day readmissions and patient-reported outcomes following colorectal surgery. BACKGROUND: Patients undergoing colorectal surgery are particularly vulnerable during their transition from hospital-to-home. There has been increasing interest in e-health to provide cost-effective transitional care. An integrated discharge monitoring program using a mobile app platform was developed to support patients after surgery. METHODS: A 2 arm, superiority randomized control trial was conducted at an academic tertiary care center with patients undergoing elective colorectal surgery. The intervention group received usual postoperative care and postdischarge monitoring with the app. The primary outcome was 30-day readmissions following hospital discharge. RESULTS: Two hundred eighty-two participants were randomized. The majority were young, had inflammatory bowel disease and underwent laparoscopic surgery. Intention to treat analysis showed no difference between groups for 30-day readmission (14.8% vs 17.6%, P =0.55), ER visits (25.0% vs 28.8%, P =0.49), primary care visits (12.5% vs 8.8%, P =0.34) or unplanned healthcare visits (34.4% vs 35.2%, P =0.89). All patient reported outcomes were significantly improved with median scores higher with the app for satisfaction [9, interquartile range (IQR): 8-10 vs 8, IQR: 7-9, P =0.001], well-being (7, IQR: 6-8 vs 6, IQR: 5-7, P =0.001) and significantly lower for anxiety (3, IQR: 2-5 vs 5, IQR: 3-6, P =0.001). CONCLUSIONS: Although the app did not show a significant reduction in 30-day readmission or ER visits, it did lead to significant improvements in patient-reported outcomes. The app may be an important tool to support patients following colorectal surgery.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".