Improving the healthcare experience: Developing a comprehensive patient health record (PHR)
Bibliographic record
Abstract
Digital technology has permeated every aspect of our lives, including the way in which our health records are managed. Currently, healthcare providers are responsible for maintaining records with limited access or control for patients. However, because of the siloed structure of the healthcare system in Canada, patients have little access or control of their medical information Our previous research focused on understanding the patient experience and revealed pain points within their care journey. The key recommendation was to design a comprehensive patient medical record (PMR) which would give patients ownership of their health information. In response, we created a mobile app to give patients ownership and control over their medical records. The app was tested with six patients and six healthcare providers and proved successful in responding to patient pain points within the current healthcare system including: instances of misunderstandings, privacy risks, and time-consuming procedures. Responses from the study showed patients were highly satisfied with being able to access, control and share their medical records. Participants felt confident their medical information is authentic, safe, and secure. Further, they were also pleased with how the app would save time and eliminate repetitive processes during their care journey. Healthcare providers confirmed the app would improve their work experience and interaction with patients. This study demonstrates the value of designing a PMR for a mobile app and testing with real world tasks. The results from this study can be applied to improve the patient experience during care.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".