Use of a digital application to optimize the clinical trajectory of patients in a TAVI program
Bibliographic record
Abstract
Abstract Background/Purpose Application-based technology has been studied for patient engagement and collecting patient-reported outcomes (PROs) in several one-day surgical specialties with limited research in transcathether valve therapies program. 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 transcatheter valve implantation (TAVI) program. Methods Patients accessed an interactive app via smartphones. Patients were guided from their visit to the TAVI clinic via reminders, tasks, PRO surveys, and evidence-based education. They received notifications and health surveys 2 days before the procedure to ensure they were ready for the procedure. In the postoperative period, patients were engaged with daily health surveys to track warning signs and recovery milestones for 7 days. 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 (TAVI program's nurse coordinator). Follow-up surveys and Kansas City Cardiomyopathy Questionnaire (KCCQ12) were sent to patients at 1, 3, 6 and 12 months to evaluate their functional recovery. All data are reported with median and interquartile range. Results 227 patients underwent a TAVI procedure at the MHI from December 2020 to October 2021. 99 patients (44%) accepted to use the application and formed the digital application group. The two groups (digital application group vs. non-digital application group) were comparable in terms of age (76 years old (72, 81) vs 77 years old (71,82)), STS (Society of Thoracic Surgeons' risk model) score (6,1% vs 6,7%), vascular complications (8,1 vs 7.8%) and post procedure pacemakers (10,1% vs 10.9%). In the digital application group, 93% recommended the application, 95% said it helped them feel more confident before the procedure and 84%, after the procedure. KCCQ12 score (PRO) improved from 34/70 (28, 41) at baseline to 54/70 (48,62) at 1 month after the procedure and remained stable over the 12 months. Digital application use helped to reduce emergency visits by 33.5% (7.9 vs 11.9%) and rehospitalizations related to the procedure by 50.1% (7.9% vs 15.9%) in the month following the TAVI procedure. Conclusion App-based technology for patient engagement is an effective modality to enhance the patient experience, better understand the trajectory of recovery, gather PRO and reduce unnecessary health services utilization in aging population of a TAVI program. Funding Acknowledgement Type of funding sources: None.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".