<i>MyHealthPortal</i> – A web-based e-Healthcare web portal for out-of-hospital patient care
Bibliographic record
Abstract
Current e-Health portal platforms provide support for patients only if they have previously registered and received service from a healthcare facility (e.g., hospital, healthcare clinic, etc.). These portals are usually connected to a central EMR/EHR system linked to a central system. Furthermore, these portals are restrictive in that they are only accessible by these patients at the exclusion of parents, relatives and others that participate in providing care to the patient. Further complications include the increasing demand from our healthcare systems for patients to receive more off-site, non-primary, in-homecare, and/or specialized healthcare services at home (e.g., therapy, nursing, personal support, etc.). Lastly, an increasing number of people would like to have more autonomy over their health in terms of increased access to their own medical records and the services they receive. In this work, we addressed these limitations by creating MyHealthPortal – a patient portal aimed at non-primary care, in-homecare, and/or special healthcare for patients. MyHealthPortal can assist homecare and clinic-based healthcare services along with the benefits of existing portals (e.g., online appointment scheduling, monitoring, and information sharing). MyHealthPortal is secure, robust, flexible and user-friendly. We developed it in partnership with our industry partner, Closing the Gap Healthcare. Closing the Gap is a prominent homecare and clinic-based healthcare service provider that became the first homecare agency to score 100% on standards from accreditation Canada and was awarded the exemplary standing. In this paper we present MyHealthPortal, the architectural framework that we designed and developed to support the system, and the results of a usability study conducted from real field studies. Our system was tested in a variety of conditions and achieved SUS usability scores of 92.5% (high).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.121 | 0.061 |
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".