Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making

Marianne A Messelink, Sina Fadaei*, Lise M Verhoef, Paco Welsing, Nienke C Nijhof, Heleen Westland

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objectives: A rapidly expanding number of prediction models is being developed, aiming to improve RA diagnosis and treatment. However, few are actually implemented in clinical practice. This study explores factors influencing the acceptance of prediction models in clinical decision-making by RA patients. Methods: A qualitative study design was used with thematic analysis of semi-structured interviews. Purposive sampling was applied to capture a complete overview of the influencing factors. The interview topic list was based on pilot data. Results: Data saturation was reached after 12 interviews. Patients were generally positive about the use of prediction models in clinical decision-making. Six key themes were identified from the interviews: (i) patients have a need for information about prediction models; (ii) factors influencing trust in model-supported treatment are described; (iii) patients envision the model to have a supportive role in clinical decision-making; (iv) patients hope to personally benefit from model-supported treatment in various ways; (v) patients are willing to contribute time and effort to contribute to model input; (vi) the effects of model-supported treatment on the relationship with the caregiver are discussed. Conclusion: Within this study, RA patients were generally positive about the use of prediction models in their treatment, given some conditions were met and concerns addressed. The results of this study can be used during the development and implementation in RA care of prediction models in order to enhance patient acceptability.

Original languageEnglish
Pages (from-to)1045-1051
Number of pages7
JournalRheumatology (Oxford, England)
Volume64
Issue number3
Early online date28 Mar 2024
DOIs
Publication statusPublished - Mar 2025

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