Creating and implementing a medical consultation recording app: Improving health information recall and shared decision-making with My Care Conversations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research indicates that recording medical consultations benefits patients by helping them recall information pertinent to their care. Cancer Care Alberta set out to develop a mobile recording app to enable patients to safely and securely record appointments and take notes. Stakeholder engagement was conducted with patients, healthcare providers, and the Alberta Health Services Legal & Privacy team. App testing was completed with patient and family advisors. The app was piloted in a clinic to assess workflow impacts before moving to a public launch. The app launched in late November 2018 and continues to be used by patients in the cancer program and beyond. Earlier in 2024, the app underwent additional testing with advisors and user-friendly improvements were made based on feedback and previous user reviews. This article summarizes the development, implementation, and sustainment of the My Care Conversations app. Implementation challenges and effective strategies are highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it