Adoption of Electronic Personal Health Records in Canada: Perceptions of Stakeholders
Bibliographic record
Abstract
BACKGROUND: Healthcare stakeholders have a great interest in the adoption and use of electronic personal health records (ePHRs) because of the potential benefits associated with them. Little is known, however, about the level of adoption of ePHRs in Canada and there is limited evidence concerning their benefits and implications for the healthcare system. This study aimed to describe the current situation of ePHRs in Canada and explore stakeholder perceptions regarding barriers and facilitators to their adoption. METHODS: Using a qualitative descriptive study design, we conducted semi-structured phone interviews between October 2013 and February 2014 with 35 individuals from seven Canadian provinces. The participants represented six stakeholder groups (patients, ePHR administrators, healthcare professionals, organizations interested in health technology development, government agencies, and researchers). A detailed summary of each interview was created and thematic analysis was conducted. RESULTS: We observed that there was no consensual definition of ePHR in Canada. Factors that could influence ePHR adoption were related to knowledge (confusion with other electronic medical records [EMRs] and lack of awareness), system design (usability and relevance), user capacities and attitudes (patient health literacy, education and interest, support for professionals), environmental factors (government commitment, targeted populations) and legal and ethical issues (information control and custody, confidentiality, privacy and security). CONCLUSION: ePHRs are slowly entering the Canadian healthcare landscape but provinces do not seem well-prepared for the implementation of this type of record. Guidance is needed on critical issues regarding ePHRs, such as ePHR definition, data ownership, access to information and interoperability with other electronic health records (EHRs). Better guidance on these issues would provide a greater awareness of ePHRs and inform stakeholders including clinicians, decision-makers, patients and the public. In turn, it may facilitate their adoption in the country.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".