Can We Improve Quality of Care in a Diabetes Clinic Through Patient-Entered Electronic Health Data?: Protocol for a Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Quality care for pediatric type 1 diabetes (T1D) requires frequent, multidisciplinary visits. Technological and clinical innovation have led to changes in T1D management resulting in increasing data exchange required during these visits. Capturing comprehensive personal health and diabetes-related information discretely and integrating it into the clinical workflow is critical for optimal T1D care, but time consuming. Time spent on data transfer often results in less time for holistic care, and can result in unmet needs for patients, families and health care providers, and increased time pressures in clinic. To address this, the Children’s Hospital of Eastern Ontario (CHEO) developed a caregiver proxy-reported questionnaire distributed via the MyChart® patient portal, allowing families to input care information ahead of visits, with the aim of dedicating more clinic time to personalized care. The launch of this tool that integrates caregiver-entered information directly into the physician’s documentation workflow, brings the opportunity to systematically evaluate its impact on care quality and efficiency, with potential implications for broader adoption. OBJECTIVE Our objective is to evaluate the impact of a caregiver proxy-reported, EHR-integrated pre-clinic questionnaire (MyChart questionnaire) on the quality of care in a pediatrics diabetes clinic, through measurement of its impact on caregiver-perceived quality of care compared to standard of care using two validated measures of care quality. We also aim to explore the impact of the intervention on glycemic control and visit efficiency. METHODS We conducted a single-centre, parallel-group randomized controlled trial designed for 222 children with T1D. Participants were randomly allocated in a 1:1 ratio to either the intervention (MyChart questionnaire) or standard care. Our primary outcome is caregiver-perceived quality of care as measured by the Patient’s Evaluation of the Quality of Diabetes Care (PEQD) at 8 months, administered with caregivers serving as proxy respondents for patients. Secondary outcomes are the PEQD at 4 months and Perceived Quality of Medical Care (PQMC) at 4 and 8 months. Tertiary outcomes include glycemic control and physician-reported visit efficiency at 4 and 8 months. ANCOVA models will be used to assess changes between baseline and post-intervention outcomes across treatment groups. RESULTS Recruitment for this study began in April 2023, and was completed in February of 2024, with a total of 139 participants successfully enrolled. Data collection has concluded, and the first results are expected in the spring of 2026. CONCLUSIONS This study is the first randomized trial to assess the impact of a caregiver proxy-reported, EHR-integrated, pre-clinic questionnaire distributed via a patient portal on caregiver-perceived quality of care in a pediatric care setting. Results will guide changes in health service infrastructure and delivery to enhance comprehensive data capture and improve care quality within and beyond pediatric T1D. CLINICALTRIAL ClinicalTrials.gov NCT05979077; https://clinicaltrials.gov/study/NCT05979077?term=NCT05979077&rank=1
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.090 | 0.012 |
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".