Technology-Enabled Health Care Collaboration in Pediatric Chronic Illness: Pre-Post Interventional Study for Feasibility, Acceptability, and Clinical Impact of an Electronic Health Record–Linked Platform for Patient-Clinician Partnership
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) technology has the potential to support the Chronic Care Model's vision of closed feedback loops and patient-clinician partnerships. OBJECTIVE: This study aims to evaluate the feasibility, acceptability, and short-term impact of an electronic health record-linked mHealth platform (Orchestra) supporting patient and clinician collaboration through real-time, bidirectional data sharing. METHODS: We conducted a 6-month prospective, pre-post, proof-of-concept study of Orchestra among patients and parents in the Cincinnati Children's Hospital inflammatory bowel disease (IBD) and cystic fibrosis (CF) clinics. Participants and clinicians used Orchestra during and between visits to complete and view patient-reported outcome (PRO) measures and previsit plans. Surveys completed at baseline and at 3- and 6-month follow-up visits plus data from the platform were used to assess outcomes including PRO completion rates, weekly platform use, disease self-efficacy, and impact on care. Analyses included descriptive statistics; pre-post comparisons; Pearson correlations; and, if applicable, effect sizes. RESULTS: We enrolled 92 participants (CF: n=52 and IBD: n=40), and 73% (67/92) completed the study. Average PRO completion was 61%, and average weekly platform use was 80%. Participants reported improvement in self-efficacy from baseline to 6 months (7.90 to 8.44; P=.006). At 6 months, most participants reported that the platform was useful (36/40, 90%) and had a positive impact on their care, including improved visit quality (33/40, 83%), visit collaboration (35/40, 88%), and visit preparation (31/40, 78%). PRO completion was positively associated with multiple indicators of care impact at 3 and 6 months. CONCLUSIONS: Use of an mHealth tool to support closed feedback loops through real-time data sharing and patient-clinician collaboration is feasible and shows indications of acceptability and promise as a strategy for improving pediatric chronic illness management.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".