Digital Health Intervention for Patient Monitoring in Immune-Mediated Inflammatory Diseases: Cocreation and Feasibility Study of the IMIDoc Platform
Bibliographic record
Abstract
Background: Immune-mediated inflammatory diseases, such as rheumatoid arthritis and spondyloarthritis, pose challenges due to recurrent flares and gaps in patient monitoring. Traditional health care models often fail to capture disease progression effectively. Objective: This study aimed to describes the structured cocreation of the IMIDoc platform, an interdisciplinary initiative aimed at improving patient monitoring, education, and health care provider decision-making. Methods: IMIDoc was cocreated through an interdisciplinary team involving clinical experts, biomedical engineers, and technical developers, using user-centered design principles. The development process included the identification of unmet clinical needs, user-centered app design, implementation of medication management features, patient data recording capabilities, and educational content. A 3-month feasibility and functionality testing was performed to evaluate the usability and technical performance of the apption. Results: During the feasibility testing, 111 entries were logged for the patient mobile app, comprising 76 errors identified and corrected, 16 improvements addressing functionality, usability, and performance, and 10 evolutionary suggestions. The professional interface received 45 entries, identifying 40 errors and 5 evolutionary suggestions. Ten iterative updates significantly enhanced the user interface intuitiveness and medication reminder functionality, aligning the solution closely with clinical workflows and user needs. Conclusions: The IMIDoc platform, developed by a multidisciplinary cocreation methodology, shows potential to improve the management of immune-mediated inflammatory diseases ithrough enhanced communication and monitoring. A multicenter clinical study with 360 patients across 5 Spanish hospitals will further evaluate its impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".