RealCare has set out to develop and validate new generations of point-of-care systems that have the ability to detect essential biomarkers in human biofluids in real-time. These systems are designed to be compact and energy-efficient and are integrated with extended reality interfaces to facilitate intuitive visualisation. The central goal is to enable rapid medical decision-making, with a specific focus on two major medical use cases: cancer (real-time monitoring in the surgery room and intensive care unit) and cardiac diseases (monitoring at the edge of intensive care unit to normal ward and discharge and home monitoring of transplanted patients).

To achieve this central goal, RealCare will focus on the development and optimisation of the following key components:

Clinical studies and PoC device specifications acts both as the driver and as the final check point for RealCare achieving TRL7 in clinical settings. This work package is led by Charité (DHZC hospital specialised in cardiac care) in strong partnership with the two cancer hospitals (IGR and its affiliate, HML), where the thoracic surgery of lung cancer patients takes place. These world-leading hospitals will define the medical hypothesis and needs (biomarkers with related concentration specifications) for continuous real-time monitoring of markers, inte¬gration of optical and electrical data with other patient data in the hospital under interoperability and data security criteria, and, finally the settings for supporting the new PoC technologies on the patient’s journey from the OR, ICU, and ward to home.

Real-Time PoCs sensing for Cancer, develops and unifies the main innovative optical technology platforms for the detection in real-time, at time granularity and under clinical specifications defined by Clinical studies and PoC device specifications. As RealCare is dealing with a multi-modal sensing platform, where the importance of each individual marker and their importance for the fast decisions should be still validated, this work package has planned the best existing methods and technologies starting from TRL 3 or 4, to address the challenging detection in complex matrix (arterial blood) of ctDNA, exosomes and nucleosomes, by CRISPR, biological amplification/florescent miniaturised microscope and mini-SPR, targeting at the end a maturity at TRL7. Real-Time PoCs sensing for Cancer also includes AFM and apertureless SNOM to validate and support the advancements of the other methods.

Real-Time PoCs for Cardiovascular is developing PoC electrical wearable technologies to cover the patient journey by monitoring key biomarkers, from the ICU to ward and at home. The generated data can be combined with optical data of some of the technologies of Real-Time PoCs sensing for Cancer in the ICU monitoring, resulting in complex multi-format multi-modal data. The activities of Real-Time PoCs for Cardiovascular use as biofluid of interest the interstitial fluid, for the collection of which a novel generation of polymer microneedles and low-volume microfluidic collectors will be developed. Multi-modal sensors, starting at TRL 3 and 4, are co-integrated in such wearable collectors, including sensors for pH, lactate, CRP, NT-proBNP and PSP and their advanced reference electrodes, with relevance for inflammation and monitoring markers for the CD medical case. The resulting PoC device could follow both CD and cancer patients and follows the specifications of 1 Clinical studies and PoC device specifications. This work package also includes a set of contactless monitoring technologies for vital signs that can be combined with the wearable monitor in ward and at home settings, adding high value to the multi-modal data. Finally, Real-Time PoCs for Cardiovascular considers the possibility to extend to the ICU one of the optical technologies of Real-Time PoCs sensing for Cancer.

PoC System integration is aiming at the hardware integration of the components of the PoCs sensing systems and providing them with electronic read-out, data processing units on the Edge and wireless interfaces and cloud connectivity. It develops a particularly challenging low power energy efficient interface hardware for the miniaturised multi-modal wearable PoC ISF monitoring that needs autonomy and battery operation for many days, as a platform that has the capability to be extended to include additional types of sensing signals. We develop interfaces for the contactless vital sign monitoring and their use in ward and at home under high security standards. The optical sensors will have specific developments of the readout hardware for the processing of the optical format of their outputs and integration of the data from these sensing methods in hospital sensing. At the hardware level, we address the interoperability of these systems and plan to validate their operation in clinical settings.

Data analytics with real-time visual and extended reality interfaces forms a central technology platform of the project for the multi-format multi-modal data integration in support of medical decision in clinical settings by the use of advanced AI and innovative visualisation interfaces. First, this work package will define principles of data handling, analysis and solution implementation to identify and set the foundations of digital fingerprints based on dynamic markers to support real-time intervention in cancer and CD, along the whole journey of the patient in hospital (from operation room to ICU, ward and at home). The use of ML and other AI technique to support medical decisions will involve close cooperation with Clinical studies and PoC device specifications in order to develop interpretable and explainable ML based on multi-format multi-modal data for the support of medical decisions in real-time. Another important activity concerns the development of extended reality interfaces for integrating and navigating into multimodal imaging and biomarker data for defined medical use cases, in clinical settings, under data security and privacy standards. The proposed designs of interfaces will be based on medical and patient user feedback and under gender and diversity criteria.