ODIN will focus on hospitals, because they are crucial and expensive nodes of European National Health Systems.

• Enhanced Hospital Workers (eWorkers): the aim is to explore how to empower hospital workers (e.g., nurses, porters, technicians, doctors, etc.) with appropriate technologies in order to enhance their skills and support their daily work. Technology will be used to relieve workers from the overwhelming burden of their routinely activities, so that they can focus on those critical tasks which demand all their human capabilities.   
• Enhanced Robots (eRobots): the aim is to automatize those hospital processes, which are no more in need of humans or can benefit from automations. These robots will not necessarily be anthropomorphic and will be employed as centrally synchronized swarms with a certain degree of independency. ODIN robots will have advanced perception functions (smell, vision, tact, taste, and hearing), extreme connectivity features (with other robots, hospital assets, humans, medical locations), advanced AI reasoning capability (both locally and remotely) and capability to perform tasks (wheels, arms, hands, etc.).  
• Enhanced Locations (eLocations): the aim is to instrument medical locations for enabling them to proactively support hospital processes. Medical locations will be enhanced with sensors (smell, vision, tact, taste, and hearing), technologies for interacting with humans (screens, lights, speakers) and high connectivity in order to safely and effectively interact with workers, robots, devices and other relevant hospital assets. Moreover, eLocations will have the capability to give in real-time information on their underlying technological infrastructures (e.g., electric plants, water pipes, air conditioning, medical gasses) which are critical for human safety (patients, visitors and staff), robots, medical devices and equipment. 

These areas of intervention will be addressed in a wide variety of Hospital Use Cases, spanning from clinical, to logistic and covering clinical engineering, AI supported diagnosis, clinical experience, nursing or home tele-rehabilitation. ODIN Hospital Use Cases are:

1. Aided logistic support: ODIN will deploy technologies for contributing to improve the design, scheduling and execution of activities such as procurement, storage, and distribution of the different materials (medicines, medical and hotel supplies, meals, linens, waste). Currently, logistic management requires redundant activities (e.g., ordering consumables after using them, transport of objects, refill of ward magazines etc.). This use case will leverage on different combinations of eRobots, eWorkers and eLocations for optimizing procedures, improve working conditions of the healthcare operators, increase hospital efficiency and workflow.   
2. Clinical engineering, medical device locations real-time management and disaster preparedness: The main enablers of modern medicine are medical devices. However, only their manufacturing, market and now post-market surveillance are currently regulated at central level. EU Member States are still responsible for all the other aspects, including medical devices management. Consequently, Health Technology Management (HTM) is poorly harmonised and standardised. The ODIN technology will allow real-time management of medical devices and medical locations by combining IoT, robots, ambient sensors, wearable devices for workers and the WASP platform, a semantic solution for web architectures. The lack of real-time exchange of information among staff, medical devices and medical locations has been one of the main causes for adverse events in hospitals in the past years. ODIN will overcome this challenge by supporting the optimization of hospital processing, combining real-time staff info, with medical devices and medical locations. This will be paramount in routinely times and also in terms of disaster preparedness, for possible future pandemic events or adverse events causing high influx of patients. As the recent COVID-19 outbreak has demonstrated, hospitals can be suddenly called to speedily adapt their asset and layouts.
3. AI based support system for Diagnosis: Optimal medical therapy relies on establishing a firm and correct diagnosis. Hence, diagnosis forms a significant part in medical education and daily care. The number of available measurements and their interpretation can be overwhelming even for experienced clinicians. The aim of this Use Case is to facilitate a high efficiency in diagnostics trajectory by:
   • personalizing the diagnostic trajectory of patients based on probabilities.
   • providing integrated capacity management of the full diagnostic supply chain.
4. Clinical Tasks and patient experience: Some patients require potential guidance and physical support during motion (walking, sitting/standing), complementary assistance during physically demanding tasks (recovering from a fall, moving while in bed) and substitution in simple daily life activities (reaching and manipulating objects). Motion guidance and physical support in performing motor tasks are currently provided only by nurses or relatives. ODIN technology is expected to reduce the effort of the clinical staff, by overcoming practical difficulties such as monitoring and assistance for patients at night, improvement of support interventions on wards or improvement of comfort perceived by patients during hospitalization.
5. Automation of clinical workflows leveraging clinical care workflows and AI technologies: Clinical workflows are complex, expensive, labor intensive and error prone. These inefficiencies and delays are higher when trials are run at a large number of sites. This Use Case aims to implement and validate a workflow-driven solution supporting the automation of the clinical research execution processes by enabling clinical care workflows, data collection processes and sources.
6. Inpatient remote rehabilitation, follow up and home hospitalization: Remote monitoring technologies are well developed and commercially available but they miss a unique platform that allows an efficient communication of these different technologies with the hospital in an easy, automatic, and secure way. ODIN platform will work as a smart information hub creating a structured data flow between homes and hospitals. ODIN will be a multi-sensing platform (named transparent robot) with flexible data communication capabilities. This Use Case will deploy a robotic element collecting patient’s data from homes and helping healthcare personnel (clinicians and nurses) to monitor patient’s lifestyle remotely from hospitals.
7. Disaster preparedness: Leveraging on partners’ COVID-19 experience, ODIN will use its advanced technology in order to support the hospital in rapidly adapting to face disasters, properly considering the safety requirements for medical locations, medical devices, patient safety and workers’ safety. Unfortunately, the complexity of hospitals is such that it requires scientific simulation before implementing any change. For instance, in order to adapt a space to ICU, hospitals needs to verify the adequacy of underlying, safety requirements of medical devices and workers´ skills. ODIN, through the combination of WASP platform, eWorkers, eRobots and eLocation will introduce a shift into the current approach to hospital disaster preparedness.