Healthcare systems lack flexible AI solutions that allow hospitals to improve efficiency and the quality of patient care. Current solutions provide limited scalability and are confined to isolated applications. Scalable models that address data sharing, integration, privacy, and ethics are needed to to ensure better adoption of AI in healthcare. The AICCELERATE project introduces an approach for scaling up AI-enabled digital solutions for different hospital use cases. AICCELERATE will develop partners’ existing digital solutions further to enable the development of a Smart Hospital Care Pathway (SHCP) Engine. This engine serves as a toolset for AI models and robotics to improve quality of care and health outcomes. It will also enable lean management and effective decision-making. These tools are tested in three pilots that (will) provide feedback for improving the SHCP Engine: (i) patient flow management for ER and surgical units, (ii) digital care pathway for Parkinson’s disease, and (iii) paediatric service delivery. AICCELERATE provides an adaptable model for varied clinical use cases to enhance patient-centric digital care pathways and to optimize patient flow management. Patient empowerment and evidence-based trust towards AI is a key part of the project. The pilots are carried out by 5 hospital partners: Helsinki Univ. Hospital and Oulu Univ. Hospital in Finland, Ospedale Pediatrico Bambino Gesù in Italy, Barcelona Children's Hospital in Spain, and Univ. hospital Università degli Studi di Padova in Italy. The other partners of the consortium consist of; Erasmus Univ. Rotterdam from Netherlands, a RTO Fundació Eurecat from Spain, a Spanish non-profit TICBioMed, 6 SMEs aiming to advance the digitalization of the European healthcare services: Chino from Italy, Symptoma from Austria, Nuromedia from Germany, SRDC from Turkey, Evondos from Finland, NeuroPath from Belgium, and 2 large enterprises NEC Laboratories Europe from Germany and Innofactor from Finland.

We will develop tools and knowledge to support physicians in accurately managing Long COVID syndrome (LCS) which has a significant impact on sufferers as well as their surroundings. Although much is now known regarding appropriate clinical management of acute COVID-19, very little is known about clinical manifestations, risk factors and underlying mechanisms for development of the highly heterogenous LCS. In this project, we aim to understand and mechanisms of LCS by combining front-line expertise from the fields of clinical medicine, virology, metabolism and immunology. We will study the pathogenesis of LCS by conducting geographically diverse cohort and registry studies, by conducting mechanistic studies, by using novel high-throughput methods for biomarker analysis, and by conducting interventional and follow-up studies on LCS patients. We will combine results from clinical and mechanistic studies to identify molecular and physiological parameters and/or pathways to decipher the mechanisms underlying LCS. We will exploit the high-throughput omics technologies to identify the predisposing factors and biomarkers that lead to the development of LCS. We will collect data from the cohort, mechanistic, biomarker and interventional studies and use these to validate the predictive artificial intelligence algorithms and to produce information and gain understanding on the combination of factors that lead to certain clustering of patients into different groups with specific symptoms. A machine learning and AI-informed Long Covid Prediction Support (LCPS) tool will be developed for the use of clinicians to predict the LCS and its possible clinical manifestations in patients. It will also help in the choice of personalized treatments for LCS patients. Additionally, an interactive graphic user interface infographic will also be available to clinicians and patients; this will communicate novel and understandable information about LCS and recommendations for patient management.

Cardiorespiratory diseases and stroke are among the top four leading causes of death in the EU in prehospital settings. Urgent care services are in huge need of novel Point of care (POC) computing technologies that can specifically detect patient condition and enable Hospital information system (HIS) with real-time data for triaging patients for right care. Our goal in POC4TRIAGE is to develop robust and accurate POC technologies, from POC testing (devices) to POC systems (platform) that is capable of fast diagnosis and efficient transfer of data to HIS. We will develop and clinically validate four rapid (<10 min) easy-to-use, compact, cost- and energy-efficient POC devices with Edge AI computing models, to be used in ambulance & emergency room settings. POC4TRIAGE devices include a multimodal patch for real-time monitoring of cardiorespiratory data, novel sub-hairline non-invasive EEG based head caps for rapid stroke diagnosis, including detection of large vessel occlusion stroke, and a handheld, rapid immunodetector to diagnose stroke with clinical utility for various conditions. These devices integrate into a new Device Hospital Connectivity Platform (DHCP) that visualizes data, uses AI from multiple devices to triage and seamlessly integrates with hospital systems and clinical workflows. The POC devices and DHCP will be clinically validated. POC4TRIAGE brings together some of Europe's leading POC device developers, medical professionals and clinicians, patient representatives, ethics experts, data scientists, and health economists. POC4TRIAGE will shorten the time to treatment and improve clinical outcome. POC4TRIAGE has potential to revolutionize healthcare delivery, making it more accessible and efficient, traceable, and interpretable for patients and providers alike. As the POC device and computation market is growing fast, the new POC devices, real-time data analysis, and secure computing have potential for major economic impact.