Use of Mobile-Based Application for Collection of Patient-Reported Outcomes in Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: Application-based (app) technology has been studied for patient engagement and collecting patient-reported outcomes (PROs) in several surgical specialties with limited research in cardiac surgery. The aim of study was to determine the effectiveness of app-based technology for collecting PROs, improving the patient experience, and reducing health services utilization in a cardiac surgery center. METHODS: Patients accessed an interactive app via smartphones. Patients were guided from 4 weeks preoperative to 4 weeks postoperative via reminders, tasks, PRO surveys, and evidence-based education. In the postoperative period, patients were engaged with daily health surveys to track warning signs and recovery milestones. Based on the patient's signs and symptoms, the app escalated lower risk issues to self-care education or higher risk issues to the care team (e.g., phone call to a nurse). RESULTS: Sixty-six percent of patients (730 of 1,108) activated their app account. Two hundred seventy-seven patients completed an end-of-program feedback survey, with 94% of patients recommending the app and 98% of patients finding the app was helpful in recovery. Patients also reported using the app to avoid unnecessary health services utilization, with 45% of patients using the app to avoid at least 1 phone call and 28% of patients using the app to avoid at least 1 hospital visit. CONCLUSIONS: App-based technology for patient engagement is an effective modality to enhance the patient experience, better understand the trajectory of recovery, and reduce unnecessary health services utilization in cardiac surgery.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".