Text Messaging Between Patients With Inflammatory Rheumatic Diseases and Pharmacists to Solve Drug-Related Problems: Prospective Feasibility Study
Bibliographic record
Abstract
Background: Patients with inflammatory rheumatic diseases often experience drug-related problems (DRPs). As these can result in negative health consequences, DRPs should be identified and addressed in a timely manner. Text messaging between patients and pharmacists at the initiative of the patient has the potential to deliver support with DRPs more continuously, increase accessibility and efficiency, and enhance patient involvement in the process of identifying and solving DRPs. Objective: This study aimed to assess the feasibility of text messaging from both the patients' and health care practitioners' perspectives before a large-scale implementation. Methods: Adult patients using a disease-modifying antirheumatic drug were given access to text messaging with pharmacists to discuss DRPs for a period of 8 weeks. Patients received a response from a pharmacist within 4 working hours. Feasibility was evaluated based on five domains of Bowen's framework for designing feasibility studies: (1) demand: actual use, expressed interest (user version of the Mobile Application Rating Scale - section E), and factors impacting future use; (2) limited efficacy: number of DRPs solved, DRPs resulting in follow-up, and DRPs warranting involvement of health care provider; (3) implementation: degree of execution (number of conversations answered within service level) and resources needed (pharmacists' time investment per conversation); (4) acceptability: satisfaction and appropriateness (theoretical framework of acceptability); and (5) practicality: ability to carry out intervention activities (System Usability Scale). Data were collected by means of usage data and a questionnaire. Results: In total, 45 patients (median age 57, IQR 52-65 y; n=31, 69% female) and 5 pharmacists (median age 41, IQR 26-47 y; n=1, 20% female) actively participated in this study. In the demand domain, 158 unique DRPs were raised in 133 conversations, with a median of 3 (IQR 2-4) unique DRPs per patient. Expressed interest was rated high by patients (median 4, IQR 4-5), and 90% (37/41) of patients would recommend text messaging to others. In the limited-efficacy domain, all DRPs were solved, and 77% (122/158) of DRPs warranted involvement of a health care provider. In the implementation domain, 87% (116/133) of conversations were answered within the promised timeframe with a median time investment of 4:15 (IQR 2:21-7:27) minutes per conversation. Acceptability was rated high by patients (median 4, IQR 4-5) and pharmacists (median 5, IQR 4-5). Finally, in the practicality domain, System Usability Scale was scored above average for patients (mean 72, SD 18) and pharmacists (mean 81, SD 16). Conclusions: Text messaging with pharmacists at the initiative of patients with rheumatic diseases seems feasible for discussing DRPs in terms of limited efficacy, implementation, acceptability, demand, and practicality for patients and pharmacists.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".