IRHIS

Demo Scale Lab 1 – Joint Musculoskeletal Care Demonstration

In Europe, there are 28 million patients diagnosed with knee and/or hip Osteoarthritis (OA), and a total of ~840 000 total knee replacements (TKR) and ~600 000 total hip replacement (THR) surgeries are performed annually. The demographic shift and skills shortage require alternative solutions to support pre- and post-operational musculoskeletal rehabilitation.

The objective is to 1) scale up the companies behind 3 medical sensor technologies and 2) to combine them into an innovative remote care platform. IRHIS will leverage the gathered data from various sensors with intelligent learning models to personalize clinical paths, with a focus on cardiovascular and orthopedic parameters. It aims to improve patient rehabilitation journeys and offer value-driven insights for both post-rehab patients and sport practitioners while ensuring regulatory compliance and cross-device interoperability.

Demo Scale Lab 2 – Demonstrating Advanced Neurological Sensing and Treatment

Patients with neurological disorders are often misdiagnosed, or only correctly diagnosed after lengthy delays preventing diagnostic understanding and appropriate treatments.

The objective is to combine an automated rehab technology encompassing a range of neurological disorders (reducing the number of misdiagnoses by 30%), while focusing on tremors including Parkinson and lactate sensor technology to improve prevention and rehabilitation. They will feed into an innovative Business Process Model and Notation (BPMN) Platform. Thus, patient outcomes and their quality of life can be improved, costs can be reduced, and in particular access to and resource planning in rehabilitation facilities can be optimized.

Demo Scale Lab 3 – Critical Patient Condition Detection Tech Upscale

Intensive care units (ICU) see a lot of damaging conditions. Among them, Sepsis is a global health crisis. It affects 50 million people every year, 11 million of whom die – 1 death every 2.8 seconds. 20% of worldwide deaths are associated with sepsis. In an ICU it comes with a wide range of other conditions (e.g., ~1 of 4 people living with cardiovascular disease(s), that usually is undiagnosed or diagnosed at a late stage).

The objective is to enhance and integrate various sensors, including glucose, humidity, and lactate sensors, into a breakthrough intensive care (ICU) remote monitoring solution, alongside the development of AI predictive analytics. This enables to predict and prevent deterioration of patients with e.g., early sepsis at the emergency department, improves patient care, and reduces healthcare costs.