Allied Health Professionals and Support Staff Perspectives on Personal Health Record Implementation: A Qualitative Study of Family Health Teams
Bibliographic record
Abstract
Primary care multi-disciplinary teams were central to recent reform plans for Canadian primary care, in response to limited resources and increasing demands. Health Information Technology was also an integral part of those plans as supporting infrastructure for the modernization of healthcare services, facilitating coordination, collaboration and access to services. As provider-centric Health Information Technology matures, attention turns to the patient. The hallmark of patient-centered applications is the electronic Personal Health Record System (PHR). These systems have grown beyond simple repositories of personal health information, extending to a range of information collection, sharing, self-management and exchange functions. The implementation of PHRs in primary care multi-disciplinary teams involves many stakeholders including patients, physician, allied health professionals and support staff. There is significant literature on physician and patient perspectives on all PHR functions. However, little attention has been given to the other stakeholders: allied health professionals and support staff. In this study, we explored the views of Allied Health Professionals (AHPs) and support staff, working in a primary care clinic adopting a patient-centered, multi-disciplinary model called the Family Health Team (FHT) model. Participants provided their insight on benefits, concerns and recommendations regarding the implementation of MyOSCAR, a PHR, at their clinic. Qualitative data was collected through semi-structured one-on-one interviews that were analyzed to extract common themes and summarize participant views. Process diagrams were produced to highlight opportunities for improvement of current work processes through the integration of MyOSCAR functions. As more teams are created in primary care and they attempt to implement new technologies, it is important to get a complete picture of all stakeholder views. This is the first study that focuses on the views of AHPs and support staff, contributing to the literature on PHR implementations. Findings from this study can contribute to future PHR implementations by informing planning and implementation.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".