Exploring the use of a digital therapeutic intervention to support the pediatric cardiac care journey: Qualitative study on clinician perspectives
Bibliographic record
Abstract
Pediatric heart disease currently effects over one million infants, children, and adolescents in the United States alone. Unlike the adult population, pediatric patients face a more uncertain path with factors relating to their growth and maturation creating levels of complexity to their care management. With mobile phones increasingly being utilized amongst adolescents, digital therapeutics tools could provide a platform to help patients and families manage their condition. This study explored clinicians' views on the use of a digital therapeutic program to support pediatric heart disease management. Using the principles from user-centered design, semi-structured interviews were conducted with 4 cardiologists, 3 nurse practitioners and 1 cardiology fellow at the Hospital for Sick Children. All interview transcripts underwent inductive thematic analysis using Braun and Clarke's iterative six-phase approach. To further contextualize the analytic interpretation of the study findings, Eakin and Gladstone's value-adding approach was used. Five themes were identified: (i) multidisciplinary model of care; (ii) patient care needs change over time; (iii) treatment burden and difficulties in care management; (iv) transition to adulthood; and (v) filling care gaps with digital health. Clinicians valued the opportunity to monitor a patient's health status in real-time, as it allowed them to modify care regimens on a more preventive basis. However, with adolescent care often varying according to the patient's age and disease severity, a digital therapeutic program would only be valuable if it was customizable to the patients changing care journey. Digital therapeutic programs can ease the process of self-care for adolescents with heart disease throughout the growth and maturation of their care journey. However, to ensure the sustained use of a program, there is a need to work collaboratively with patients, caregivers, and clinicians to ensure their lived experiences guide the design and delivery of the overall program.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".