The vision of AICCELERATE project is to introduce 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) pediatric 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.  

Our Role

SRDC is one of the main R&D partners of the project. SRDC is the leader of “WP1 Data Infrastructure” and lead the activities to design and develop the data infrastructure for SHCP.  These activities includes; 

  • Analyzing and defining general data requirements of the Smart Hospital Care Pathway Engine and identifying all available datasets within the partner organizations. 
  • Based on these requirements, defining the common data model, data exchange API and methodology based on HL7 FHIR standard 
  • By using its product onFhir.io (https://onfhir.io/), setting up a FHIR repository for SHCP for storing and managing collected data  
  • Providing scalable data integration suite to integrate data from the data sources to be utilized in pilot studies 
  • Implementing a data extraction suite for FHIR to prepare the required datasets for AI  solutions in an efficient and distributed way 
1. Helsinki University Hospital Finland
2. Northern Ostrobothnia Hospital District Finland
3. Chino Srls Italy
4. Symptoma GmbH Austria
5. Fundació Eurecat Spain
6. Fundació Sant Joan de Déu Spain
7. Nuromedia GmbH Germany
8. NEC Laboratories Europe GmbH Germany
9. Ospedale Pediatrico Bambino Gesù Italy
10. SRDC Turkey
11. Evondos Finland
12. TICBioMed Spain
13. NeuroPath Belgium
14. Erasmus University Rotterdam Netherlands
15. University of Padova Italy
16. Innofactor Software Oy Finland