Development and Implementation of an Interprofessional Digital Platform to Increase Therapeutic Adherence: Protocol for a Mixed Design Study
Bibliographic record
Abstract
BACKGROUND: Adherence to care plans is a major issue in health care systems. Improved adherence has several potential benefits such as ensuring treatment effectiveness and control of chronic diseases. There is currently a lack of tools to maximize treatment adherence in an integrated manner, that is, covering multiple aspects of patients' health continuously throughout their medical care. To ensure better adherence, such tools must meet the needs of patients with chronic conditions as well as those of health care professionals. Acknowledging the health issues associated with nonadherence to treatment, an industry-research-clinical partnership aims to adapt a digital platform-facilitating patient-health care professional interactions-to improve therapeutic adherence in patients with chronic illnesses. The platform allows for exchanges between patients and health care professionals to facilitate the timing of medication use or chronic disease management and maximize patient adherence. OBJECTIVE: This study aims to (1) identify the needs of patients living with a chronic condition and their health professionals concerning their interactions regarding treatment; (2) codevelop an adaptation of an interactive patient-professional platform that meets the needs identified; and (3) then test the platform and document its effects and acceptability in a clinical setting. METHODS: The study will use a creative design thinking process based on the needs expressed by users (patients and health professionals) concerning treatment adherence for chronic diseases (eg, diabetes, asthma, high blood pressure, depression and anxiety, chronic obstructive pulmonary disease). A mixed method evaluation research design will be used to develop and evaluate the platform. Qualitative data will be used to assess user needs and acceptability of the platform, and quantitative data will provide the necessary insights to document its effects. RESULTS: Technological development of the platform has been completed. Recruitment for the first part of Phase 1 started in May 2022. The results of this project to codevelop an interprofessional digital platform to increase therapeutic adherence will be relevant to clinicians and managers seeking contemporary solutions that support patient adherence to treatment for chronic diseases. These results will enable optimal use of the platform and identify areas for improvement in interactive patient-health care professional apps. CONCLUSIONS: The adoption of an interactive digital platform to facilitate effective exchanges between patients and health care professionals in primary care settings could improve adherence to treatment. The platform tested in this project takes a first step in this direction by ensuring that the technological product is developed according to the needs of patients as well as the health professionals who are likely to use it. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34463.
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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 | 0.077 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".