Home to Stay: An Integrated Monitoring System Using a Mobile App to Support Patients at Home Following Colorectal Surgery
Bibliographic record
Abstract
BACKGROUND: Patients undergoing colorectal surgery are vulnerable during their transition from hospital to home and require increased support following discharge from hospital. Study objectives were to perform an initial assessment of patient uptake, outcomes, and satisfaction with an integrated discharge monitoring system called Home to Stay. METHODS: The intervention was an integrated discharge monitoring system that uses a mobile app platform. Patients downloaded the app prior to discharge from hospital and received a Daily Health Check day #1 to #14, #21, and #30. Patient responses' were accessed by the health-care team via secure web site, and extreme responses were "flagged" to indicate that a follow-up telephone call was necessary. Primary outcomes were patient uptake, Quality of Recovery scores and satisfaction with the program. Secondary outcomes were 30-day emergency room (ER) visits and readmissions. RESULTS: One hundred and thirty-two patients were invited to participate and 106 accepted. Of these, 93 used the app at least once. The mean overall score on the Quality of Recovery Scale increased significantly from day 1 to day 14. Patient satisfaction with the app was high, with 92% of patients reporting overall satisfaction as good or excellent. The 30-day readmission rate was 6% and was lower than the 30-day readmission rate of 18% reported for the 4 months prior to the start of the study. CONCLUSIONS: The Home to Stay Program to support patients at home after colorectal surgery is feasible with high patient uptake and satisfaction. This program has the potential to reduce 30-day readmissions, however further studies are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".