Exploring Usability Issues of a Smartphone-Based Physician-to-Physician Teleconsultation App in an Orthopedic Clinic: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Physician-to-physician teleconsultation has increasingly played an essential role in delivering optimum health care services, particularly in orthopedic practice. In this study, the usability of a smartphone app for teleconsultation among orthopedic specialists was investigated to explore issues informing further recommendations for improvement in the following iterations. OBJECTIVE: This study aimed to explore usability issues emerging from users' interactions with MEDIC app, a smartphone-based patient-centered physician-to-physician teleconsultation system. METHODS: Five attending physicians in the Department of Orthopedics in a large medical school in Bangkok, Thailand, were recruited and asked to perform 5 evaluation tasks, namely, group formation, patient registration, clinical data capturing, case record form creation, and teleconsultation. In addition, one expert user was recruited as the control participant. Think aloud was adopted while performing the tasks. Semistructured interviews were conducted after each task and prior to the exit. Quantitative and qualitative measures were used to identify usability issues in 7 domains based on the People At the Centre of Mobile Application Development model: effectiveness, efficiency, satisfaction, learnability, memorability, error, and cognitive load. RESULTS: Several measures indicate various aspects of usability of the app, including completion rates, time to completion, number of clicks, number of screens, errors, incidents where participants were unable to perform tasks, which had previously been completed, and perceived task difficulty. Major and critical usability issues based on participant feedback were rooted from the limitation of screen size and resolution. Errors in data input (eg, typing errors, miscalculation), action failures, and misinterpretation of data (ie, radiography) were the most critical and common issues found in this study. A few participants did not complete the assigned tasks mostly owing to the navigation design and misreading/misunderstanding icons. However, the novice users were quite positive that they would be able to become familiar with the app in a short period of time. CONCLUSIONS: The usability issues in physician-to-physician teleconsultation systems in smartphones, in general, are derived from the limitations of smartphones and their operating systems. Although some recommendations were devised to handle these usability issues, usability evaluation for additional development should still be further investigated.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".