Can a mobile app technology reduce emergency department visits and readmissions after lung resection? A prospective cohort study
Bibliographic record
Abstract
Background: Emergency department (ED) visits and readmissions after thoracic surgery are a major health care problem. We hypothesized that the addition of a novel post-discharge mobile app specific to thoracic surgery to an existing home care program would reduce ED visits and readmissions compared to a home care program alone. Methods: We conducted a prospective cohort study of patients undergoing major lung resection for malignant disease between November 2016 and May 2018. Patients received either home care alone (control group) or home care plus a patient-input mobile app (intervention group). Primary outcomes were 30-day readmission and ED visit rates. Secondary outcomes included reasons for ED visits and readmissions, perioperative complications, 30-day mortality, anxiety (assessed with the Generalized Anxiety Disorder-7 Scale [GAD-7]) and app-related adverse events. We compared outcomes between the 2 groups, analyzing the data on an intention-to-treat basis. Results: Despite the greater number of open surgery and anatomic resections in the intervention cohort, patients in that group were less likely than those in the control group to visit the ED within 30 days of discharge (24.0% v. 38.8%, p = 0.02). Thirty-day readmission rates were similar between the intervention and control groups (10.1% v. 12.2%, p = 0.6). In a subset of patients, there was no difference between the 2 groups in the proportion of patients with a GAD-7 score of 0 (control group 79.8%, intervention group 79.5%, p = NS), which indicated a similar absence of postdischarge anxiety and depression symptoms in the 2 cohorts. Conclusion: The addition of a mobile app to a home care program after thoracic surgery was associated with a reduced frequency of ED visits, in spite of the higher proportions of thoracotomies and anatomic resections in the app cohort. More studies are needed to evaluate the full effect of this new, emerging technology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".