An mHealth App to Support Patients With Psoriasis in Relation to Follow-up Consultations: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Teledermatology has the potential to help deliver health care by transforming the relationship between patients and health care professionals (HCPs), shifting the power of consultation so that patients can become more informed, assertive, and involved in their care. Mobile health (mHealth) is a promising and reliable tool for the long-term management of patients with psoriasis on systemic treatment. In an attempt to facilitate a more patient-centered approach in clinical practice, we designed and developed an mHealth solution to support patients with self-management and empowerment. OBJECTIVE: The aim of this study is to explore the experiences and perceptions of patients and health care professionals of using an mHealth solution that was developed using a participatory design approach. METHODS: This was an exploratory qualitative study. Data were collected through semistructured interviews with patients and focus group interviews with HCPs. RESULTS: All participants found it easy to use the mHealth solution, and the patients found it convenient. Patients' reflexivity was improved because they could prepare ahead of consultations. Video consultations provided patients with a degree of freedom in their everyday lives, with not having to attend in-person visits. Among the HCPs, there were concerns regarding their medical responsibilities, as they could not assess the patients' skin as they used to. The mHealth solution required new workflows and procedures that were not part of the existing consultation routines. CONCLUSIONS: The mHealth solution can strengthen the relationship between HCPs and patients and facilitate patients to become more active in their care. Alignment and structure in relation to the selection of eligible patient candidates for being offered the mHealth solution could reduce social health inequalities. In addition, video consultations changed HCPs' work practice, necessitating new types of skills to communicate with patients.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".