Factors Influencing Preferences of Patients With Rheumatic Diseases Regarding Telehealth Channels for Support With Medication Use: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients with rheumatic diseases are known to experience drug-related problems at various times during their treatment. As these problems can negatively influence patients' health, they should be prevented or resolved as soon as possible, for which patients might benefit from additional support. Telehealth has the potential to continuously provide information and offers the possibility to easily contact a health care provider in order to support patients with medication use. Knowledge of factors influencing the patient's preference for telehealth channels can improve the actual use of telehealth channels. OBJECTIVE: This study aims to identify factors that influence the preferences of patients with rheumatic diseases regarding telehealth channels for support with medication use. METHODS: A qualitative study with face-to-face interviews was performed among patients with an inflammatory rheumatic disease in the Netherlands. A total of 4 telehealth channels were used: a frequently asked questions page, a digital human, an app for SMS text messaging with health care providers, and an app for video-calling with health care providers. Using a semistructured interview guide based on domains of the Capability, Opportunity, Motivation, and Behavior (COM-B) model, participants were questioned about (1) their general opinion on the 4 telehealth channels, (2) factors influencing preference for individual telehealth channels, and (3) factors influencing preference for individual telehealth channels in relation to the other available channels. Interviews were recorded, transcribed, and categorically analyzed. RESULTS: A total of 15 patients were interviewed (female: n=8, 53%; male: n=7, 47%; mean age 55, SD 16.8 years; median treatment duration of 41, IQR 12-106 months). The following 3 categories of factors influencing patient preference regarding telehealth channels were identified: (1) problem-related factors included problems needing a visual check, problems specifically related to the patient, and urgency of the problem; (2) patient-related factors included personal communication preference and patient characteristics; and (3) channel-related factors included familiarity with the telehealth channel, direct communication with a health care provider, methods of searching, and conversation history. CONCLUSIONS: Preference for telehealth channels is influenced by factors related to the problem experienced, the patient experiencing the problem, and telehealth channel characteristics. As the preference for telehealth channels varies between these categories, multiple telehealth channels should be offered to enable patients to tailor the support with their medication use to their needs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".