The digital health landscape at children’s hospitals in Canada
Bibliographic record
Abstract
Objectives: Canadian hospitals have historically lagged behind peer nations in terms of adoption of digital health tools. The aim of this study was to assess the current state of adoption of digital health tools at children's hospitals in Canada. Methods: We conducted an online survey of Canadian pediatric tertiary-care hospitals between January and July 2023. The 35-item questionnaire was administered in English and French. Hospital characteristics, informatics infrastructure data and electronic health record (EHR) functionality data were summarized using descriptive statistics. Results: The survey was completed by 15 of the 17 (88.2%) pediatric hospitals in Canada. All institutions had an EHR with 10 (66.6%) being fully digitized and five (33.3%) being partially digitized. Funding and availability of clinicians with expertise in clinical informatics were cited as barriers to implementing digital health tools. The availability of core EHR functionalities ranged from 53.3% for medication to 100% for the presence of a laboratory and radiology information system. Only five (33.3%) institutions reported that they had a patient portal. Discussion: While all hospitals in this study had an EHR, functionalities varied greatly between centers. Canada lags behind the United States in terms of adoption of digital health tools such as patient portals likely due to governmental mandates and incentives, and a focus on cultivating a physician informatics workforce. Conclusion: Canadian pediatric hospitals lag behind peer institutions in digital maturity. This study highlights the perceived need from hospitals for policies, funding, and resources from the Canadian government to help increase the uptake of comprehensive EHRs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".