Perspectives and Experiences of Patient-Led Melanoma Surveillance Using Digital Technologies From Clinicians Involved in the MEL-SELF Pilot Randomized Controlled Trial: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The growing number of melanoma patients who need long-term surveillance increasingly exceeds the capacity of the dermatology workforce, particularly outside of metropolitan areas. Digital technologies that enable patients to perform skin self-examination and send dermoscopic images of lesions of concern to a dermatologist (mobile teledermoscopy) are a potential solution. If these technologies and the remote delivery of melanoma surveillance are to be incorporated into routine clinical practice, they need to be accepted by clinicians providing melanoma care, such as dermatologists and general practitioners (GPs). OBJECTIVE: This study aimed to explore perceptions of potential benefits and harms of mobile teledermoscopy, as well as experiences with this technology, among clinicians participating in a pilot randomized controlled trial (RCT) of patient-led melanoma surveillance. METHODS: This qualitative study was nested within a pilot RCT conducted at dermatologist and skin specialist GP-led melanoma clinics in New South Wales, Australia. We conducted semistructured interviews with 8 of the total 11 clinicians who were involved in the trial, including 4 dermatologists (3 provided teledermatology, 2 were treating clinicians), 1 surgical oncologist, and 3 GPs with qualifications in skin cancer screening (the remaining 3 GPs declined an interview). Thematic analysis was used to analyze the data with reference to the concepts of "medical overuse" and "high-value care." RESULTS: Clinicians identified several potential benefits, including increased access to dermatology services, earlier detection of melanomas, reassurance for patients between scheduled visits, and a reduction in unnecessary clinic visits. However, they also identified some potential concerns regarding the use of the technology and remote monitoring that could result in diagnostic uncertainty. These included poor image quality, difficulty making assessments from a 2D digital image (even if good quality), insufficient clinical history provided, and concern that suspicious lesions may have been missed by the patient. Clinicians thought that uncertainty arising from these concerns, together with perceived potential medicolegal consequences from missing a diagnosis, might lead to increases in unnecessary clinic visits and procedures. Strategies suggested for achieving high-value care included managing clinical uncertainty to decrease the potential for medical overuse and ensuring optimal placement of patient-led teledermoscopy within existing clinical care pathways to increase the potential for benefits. CONCLUSIONS: Clinicians were enthusiastic about the potential and experienced benefits of mobile teledermoscopy; however, managing clinical uncertainty will be necessary to achieve these benefits in clinical care outside of trial contexts and minimize potential harms from medical overuse. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12616001716459; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=371865.
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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 | 0.070 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".