The new Medical Device Regulation (MDR) bears the potential to hamper Europe’s innovation competitiveness since reiterated, widened testing efforts are required. Especially SMEs must plan with early exits in the face of costly clinical studies. A structural remedial action reinstates the balance between economy and safety. The MDOT working group develops a set of coordinated measures: 1) Support with mandatory conformity assessment using a database approach based on device risk class. 2) Data exchange forum: A mutual, cross-enterprise exchange of medical device testing data on a safe and transparent platform, which aims at saving costs and streamlining clinical tests as far as possible. 2) Test foundry: Joint evaluations of commonly used parts and devices. 3) Development of advanced testing methods with a focus on in vitro and in silico data. This is all performed with regulatory support taking test beds and device innovations towards the level of clinical trials (TRL 4 – 7). This platform realizes one-stop-shop processing reducing complexity and individual costs. The operability of MDOT will be demonstrated within medical product segments growing fastest and with urgent medical need. The initial consortium consists of MD industry R&D, translational institutions and networks as well as clinical research centers. It will grant open access to new clients already during the funding period. Goal of the project is to implement the platform as a meta-network to preserve MedTech innovation and economic strength, reduce animal testing, and support MDR’s new level of patient safety.

Invasive mechanical ventilation (MV) is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, when MV alone is insufficient, extracorporeal lung support (ELS) is initiated. However, MV is recognised as potentially harmful, because inappropriate MV settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that MV is often not properly provided despite clear evidence and guidelines. Furthermore, treatment decisions by the healthcare providers, especially regarding MV and ELS, often remain incomprehensible to the patients and their relatives, since flow of information from caregivers to patients is challenged by a number of factors, including limited time and resources, communication problems as well as patients? capacity to comprehend and memorise information. The project proposed herein aims at clinically validating and extending our IntelliLung Artificial Intelligence Decision Support System (AI-DSS) designed to optimise MV and ELS settings to improve the care of ICU patients, alongside caregiver-patient communication. Thereby, best practice evidence-based MV and ELS within safer therapy corridors for longer periods, faster weaning from MV, and improved survival could be achieved - even in non-experienced hands. Additionally, this project will improve the information flow from caregivers to patients and relatives in the ICU setting. Therefore, we will develop a digital solution that allows automatic generation of an extensive plain-language information package for patients and their relatives, communicating highly individualised information on diseases and knowledge-based disease-management strategies, thus facilitating high-quality current and subsequent care through health literacy empowerment and patient-centredness. We will perform a retrospective and prospective multi-centre study to validate our IntelliLung AI-DSS and the patient information software.