Globally 1.Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, trial and error approach in initial treatment. SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways: - By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early. - By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online). - By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science SPIDERR will deliver three clinical models -a symptom checker for patients -a decision support tool for (primary) care providers providing guiding additional examination and referral decisions -a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines. Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.

RealHOPE will create an understanding of the real-life handling of protein drugs in hospital pharmacy, clinics and in the hands of patients by applying smart tag technologies. Different parameters will be logged and combined with protein characterisation at different stages, as well as with information from EFPIA partners on their drugs in use. Statistical evaluation of the data will be used to identify patterns in handling that are linked to protein destabilisation occurrence and type of protein degradation. Focus interviews with personnel in hospital pharmacies, clinics and with patients/care givers will be used to understand current handling practice and what the desired handling instructions and limitations are. These insights will be used to design in-use mimicking stability protocols for the protein drugs in the project. Ultra-scaled-down devices for stability assessment will be designed to be used in early phase for efficient development cost effective and safe future protein therapies. Protocols and devices will be validated towards the collected handling data base. Interventions in hospital pharmacies using e.g. compounding robots will be investigated. Techniques to assess e.g. aggregate formation in the final drug preparation situation will be evaluated together with SMEs producing analytical tools and hospital pharmacists. The collected data and interviews will form the base of development of teaching materials directed towards different target groups: hospital pharmacists, nurses and patients/care givers. App developers will be active in this part to design attractive and efficient apps for teaching and collecting therapy performance data.