A Scenario-Based Study of Doctors and Patients on Video Conferencing Appointments from Home
Bibliographic record
Abstract
Telemedicine systems that involve the use of video conferencing technologies have been available for more than three decades. Yet, they have primarily been used for specialist appointments or within health care facilities. We are now seeing a shift with the proliferation of commercial technologies, such as smartphone apps that allow people to have appointments with a general practitioner from nearly any location for various reasons. Telemedicine has also seen an uptake due to the COVID-19 pandemic. However, little is known about how doctors and patients perceive smartphone-based telemedicine systems, what types of medical ailments are best suited for these systems, what sociotechnical challenges might emerge through their usage, and how systems should be designed to best meet the needs of both doctors and patients. Thus, we applied a scenario-based design method by presenting a set of medical situations to both general practitioners and patients, and conducted contextual interviews with them to investigate their thoughts on video-based appointments for a range of medical situations. Results show that video consultations using smartphone apps could raise challenges in delivering appropriate care and utilization, conducting camera work to assist different types of examinations, supporting doctor–patient relationship creation and maintenance, allowing doctors to maintain control over the appointment, as well as protecting patients’ and doctors’ privacy. This suggests the need to create designs that can support particular workflows, relationship building, safety and privacy protection, and camera work for varying contexts.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".