Opportunities for digital health innovations to address patient-centered priorities in racialized pediatric populations: A qualitative study in British Columbia, Canada
Bibliographic record
Abstract
Background and aim: Patient- and family-centered care in pediatrics is associated with improved outcomes and cost-effectiveness, yet current models often overlook the needs of diverse and racialized families. Digital health innovations offer new opportunities to address inequities and tailor care to the needs of racialized patients and their families. This study explores gaps in pediatric patient experience and examines how emerging digital health technologies can support more equitable, effective care by engaging diverse communities through semi-structured interviews with caregivers. Methods: We conducted a retrospective qualitative study using quota-based sampling at BC Children's Hospital. Caregivers of children with complex healthcare experiences-including surgery, rehabilitation, chronic conditions, or hospital stays over 14 days-were recruited and interviewed through semi-structured interviews conducted via Zoom. Transcripts were thematically analyzed and categorized using the BC Health Quality Matrix to identify key gaps and opportunities in pediatric care. Results: Interviews revealed gaps in pediatric care, including inconsistent shared decision-making, pre-procedural patient anxiety, barriers to access, misdiagnoses, and long wait times. We identified digital health solutions such as mobile health apps, telehealth, and artificial intelligence as opportunities to address these solutions, but found that many of these solutions have not yet been implemented within a pediatric population or may exacerbate disparities. Discussion: Digital health technologies show potential to improve pediatric care by addressing gaps in racialized patient experience as identified in the current study. Incorporating diverse patient and caregiver perspectives is essential to ensure innovations are equitable and responsive to the diverse needs of British Columbia residents. Achieving digital health equity remains critical to realizing the broader benefits of patient-centered innovation and advancing the Quadruple Aim.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".