Co-Design of a Consultation Audio-Recording Mobile App for People With Cancer: The SecondEars App
Bibliographic record
Abstract
BACKGROUND: Many patients choose to audio-record their medical consultations so that they can relisten to them at home and share them with family. Consultation audio-recordings can improve patients' recall and understanding of medical information and increase their involvement in decision making. A hospital-endorsed consultation audio-recording mobile app would provide patients with the permission and means to audio-record their consultations. The Theory of Planned Behavior provides a framework for understanding how patients can be encouraged to appropriately audio-record consultations. OBJECTIVE: The aim of this study was to use a co-design process to develop a consultation audio-recording mobile app called SecondEars. METHODS: App development began with stakeholder engagement, followed by a series of 6 co-design workshops and then user acceptance testing. Stakeholder engagement included advice from legal, information technology (IT), clinical and allied health leads; digital strategy; and medical records. he co-design workshops were attended by: patient consumers, members of the research team, IT staff, the app designers, clinicians, and staff from medical records. During workshops 1 to 4, the purpose and scope of the app were refined, possible pitfalls were addressed, and design features were discussed. The app designers then incorporated the results from these workshops to produce a wireframe mock-up of the proposed SecondEars app, which was presented for feedback at workshops 5 and 6. RESULTS: The stakeholders identified 6 requirements for the app, including that it be patient driven, secure, clear in terms of legal responsibilities, linked to the patient's medical record, and that it should require minimal upfront and ongoing resources. These requirements informed the scope of the co-design workshops. The workshops were attended by between 4 and 13 people. The workshop attendees developed a list of required features and suggestions for user interface design. The app developers used these requirements and recommendations to develop a prototype of the SecondEars app in iOS, which was then refined through user acceptance testing. CONCLUSIONS: The SecondEars app allows patients to have control and autonomy over audio-recording and sharing their consultations while maintaining privacy and safety for medical information and legal protection for clinicians. The app has been designed to have low upkeep and minimal impact on clinical processes. The SecondEars prototype is currently being tested with patients in a clinical setting.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".