Development and Implementation of an Interprofessional Digital Platform to Increase Therapeutic Adherence: Protocol for a Mixed Design Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,077 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,045 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».