Pos0142 a Clinical and Scientific Research Platform for Secondary Use of Real World Data With AI Methods – Consent Rate Analysis From the Informed Broad Consent

Background: There is an increasing need for integrated real-world data generation at the patient level across different siloes and healthcare settings (e.g., secondary and primary care) for clinical, scientific, and pharmaceutical research including AI methods. However, these data are scattered between different organizations, systems, and often remain unused for secondary purposes. Connecting and unlocking these data can advance clinical research to better understand disease and improve patient care. Thus, we established a clinical and scientific research platform that brings all these data into an integrated platform for researchers in a Rheumatology setting. The platform has been designed to bridge the gaps in the patient journey by integrating data throughout the patient journey within a centralized platform. This is not done in routine clinical practice in Germany. Objectives: Evaluate the informed broad consent (BC) for the platform predicated on the principles of the Medical Informatics Initiative in Germany that was implemented and administered to our patients scheduled for routine follow-ups and visits within the Rheumatology outpatient clinic at a tertiary centre. Methods: The analyses focused on the consent rates and options selected to the pre-given BC options. Positive ethics vote and DRKS registration were obtained. Results: From 07/2023 to 12/2023 n=254 outpatients (48% with university entrance diploma) signed the BC and agreed for their data to be processed for the purposes of the platform. The median years of age of the consented patients was 57, most of which were female (71%), and the median disease duration was 11 years. Disease groups included rheumatoid arthritis (36%), psoriatic arthritis (11%), spondyloarthritis (6%), systemic lupus erythematosus (28%), systemic sclerosis (4%), and others (6%). Apart from the signature, eight predefined BC options could be selected with either a 'yes' or 'no', with the average number of 'yes' answers (standard deviation (SD)) being 7.4±1.5, and the average number of 'no' answers being 0.2±0.5. The mean number of missings over 8 BC items was 0.4±1.5. No differences between disease groups were noted. Even where signed informed consent was given, n=6 (2%) did not select any of the BC options at all. In addition, n=198 (78%) signed with consenting to all BC options. The approval rates for the individual BC items are listed in Table 1. Conclusion: Our study focuses on the individual BC for a platform that collects and pools data from various sources within a centralized platform for scientific and clinical research Rheumatology embedded in routine care. The various aspects of our established BC are based on the template of the MII were well accepted by our predominantly female patients. Our patients extensive support for the integration of real-world data generation at the patient level across different siloes and healthcare settings gives us a high flexibility for performing clinical, scientific, and pharmaceutical research including AI methods, as well as a firm legal basis to do so that is rooted in informed consent pursuant to the GDPR. Detailed analyses on patients' motivations and fears from an accompanying evaluation questionnaire are to be explored subsequently. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests: Jutta G. Richter: None declared, Hasan Acar: None declared, Antonia Becker: None declared, Waldemar Ockert., not grants, but professional fees for professional services performed, Dominykas Kriauciunas., not grants, but professional fees for professional services performed, Markus Schröder: None declared, Jörg Distler: None declared, Matthias Schneider: None declared.