An Educational Digital Tool to Improve the Implementation of Switching to a Biosimilar (Rapid Switch Trainer): Tool Development and Validation Study
Bibliographic record
Abstract
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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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 | 0.044 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".