An Educational Digital Tool to Improve the Implementation of Switching to a Biosimilar (Rapid Switch Trainer): Tool Development and Validation Study
Notice bibliographique
Résumé
Background: Switching to biosimilars is an effective and safe practice in treating inflammatory diseases; however, a nocebo effect may arise as a result of the way in which the switch is communicated to a given patient. Objective: We aimed to design a gaming-based digital educational tool (including a discussion algorithm) to support the training of health care professionals in efficiently communicating the switch to biosimilars, minimizing the generation of a nocebo effect and thus serving as an implementation strategy for the recommended switch. Methods: The tool was developed based on interviews and focus group discussions with key stakeholders, both patients and health care professionals. Messages likely to either generate trust or to trigger a nocebo effect were generated on the basis of the interviews and focus group discussions. Results: A total 7 clinicians and 4 nurses specializing in rheumatology, gastroenterology, and dermatology, with balanced levels of responsibility and experience, as well as balance between geographic regions, participated in the structured direct interviews and provided a list of arguments they commonly used, or saw used, to justify the switching, and objections given by the patients they attended. Patients with immune-mediated inflammatory diseases who were taking biologic drugs with (n=4) and without (n=5) experience in switching attended the focus groups and interviews. Major topics of discussion were the reason for the change, the nature of biosimilars, and their quality, safety, efficacy, and cost. Based on these discussions, a list of objections and of potential arguments was produced. Patients and health care professionals rated the arguments for their potential to evoke trust or a nocebo effect. Two sets of arguments, related to savings and sustainability, showed discrepant ratings between patients and health care professionals. Objections and arguments were organized by categories and incorporated into the tool as algorithms. The educators then developed additional arguments (with inadequate answers) to complement the valid ones worked on in the focus groups. The tool was then developed as a collection of clinical situations or vignettes that appear randomly to the user, who then has to choose an argument to counteract the given objections. After each interaction, the tool provides feedback. The tool was further supported by accredited medical training on biosimilars and switching. Conclusions: We have developed a digital training tool to improve communication on switching to biosimilars in the clinic and prevent a nocebo effect based on broad and in-depth experiences of patients and health care professionals. The validation of this implementation strategy is ongoing.
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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 | 0,044 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».