BackgroundOnline symptom checkers are often developed and validated on data subject to self-selection and selective attrition, potentially introducing biases in prediction models.ObjectivesTo assess recruitment, selection, and attrition patterns in a large Dutch online symptom checker for musculoskeletal complaints and to evaluate potential biases by comparing participant characteristics across recruitment sources and with external target populations.MethodsUsing data from the online Dutch Rheumatic? Questionnaire on musculoskeletal complaints, we compared baseline characteristics and key self-reported symptoms between responders to the follow-up survey and nonresponders. The survey responders were furthermore compared according to source of recruitment to the questionnaire, i.e., via primary care clinics, secondary care clinics, or via different online sources. Sex, age and BMI distributions from the total study group were compared to external data of potential target populations of primary and secondary care patients within the Netherlands.ResultsThe total study group of answers to the questionnaire comprised 31,457 responders, of which 50% (n = 15,591) responded to the follow-up survey. Study participants were predominantly female (76%), middle-aged (one-third 50–60 years), never-smokers (66%), and overweight. While participants recruited through healthcare settings resembled target populations, follow-up survey responders were older, had more rheumatic diagnoses (49% vs. 32%), and reported more symptoms than non-responders. Participant characteristics varied by recruitment source, with social media attracting younger females while healthcare routes reached more diverse populations with varying symptom presentations.ConclusionPatterns of recruitment and attrition produced differences in participant characteristics. Healthcare-based recruitment yielded participants resembling intended target populations, and follow-up survey responders differed on some points from nonresponders. Awareness of these selection processes is essential when using real-world symptom checker data for model development. ... Read more

Rheumatic diseases are chronic, immune-mediated conditions characterized by significant heterogeneity in presentation and disease course. However, current clinical approaches often rely on snapshot-based assessments that fail to capture the complex longitudinal evolution of these conditions. To address these limitations and support the implementation of precision medicine, we present the design for the Rheumatic Digital Twin, a novel, modular conceptual framework intended to integrate heterogeneous multimodal data, ranging from electronic health records and clinical notes to imaging and omics, into a dynamic, computational representation of the patient journey. Our theoretical architecture addresses challenges related to data silos and variable availability of data modalities through a multistage approach that envisions the use of domain-specific foundation models to independently process distinct data modalities. To effectively model the temporal progression inherent in chronic diseases, the proposed design utilizes Transformer architectures, leveraging self-attention mechanisms to treat patient events, such as lab results or medication changes, as sequential data tokens. We describe how these unimodal representations would subsequently be fused via joint embedding techniques to construct a shared, multimodal representational space. Envisioned to function analogously to a recommender system, the Rheumatic Digital Twin framework is modeled to map patients into a latent space where proximity reflects clinical and biological similarity. By identifying “nearest neighbors,” historical patients with comparable trajectories, the system aims to enable in silico cohorting, theoretically allowing clinicians to forecast key clinical events, predict treatment responses, and identify likely disease courses based on the outcomes of similar peers. ... Read more