Periodic Reporting for period 1 - POC4TRIAGE (Point-of-Care Devices for Urgent Care Triage)
Okres sprawozdawczy: 2024-07-01 do 2025-12-31
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,a functional near-infrared spectroscopy (fNIRS) device for detection and continuous monitoring of brain 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.
The project Consortium comprises 18 Partners from 11 countries. The Consortium is led by the University of Turku, Finland. 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 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.
The project Consortium comprises 18 Partners from 11 countries. The Consortium is led by the University of Turku, Finland. POC4TRIAGE brings together some of Europe’s leading POC device developers, medical professionals and clinicians, patient representatives, ethics experts, data scientists, and health economists.
In POC4TRIAGE, we will develop, test, and clinically validate four compact cost- and energy-efficient POC devices for ambulance and emergency room/intensive care units in four well-defined use-cases, that is, multimodal patch to sense cardiorespiratory events in urgent care; novel sub-hairline non-invasive EEG based head caps for rapid stroke diagnosis, including detection of large vessel occlusion stroke; a wearable functional near-infrared spectroscopy (fNIRS) device for detection and continuous monitoring of brain stroke, and a portable immunosensor to detect blood biomarkers for stroke. For each device, we will develop new algorithms and edge AI computing models.
Edge AI computing models that run directly on devices without any need of cloud computing resources provide robust, fast, and accurate triaging estimates, improved response times, better resource allocation and enhanced patient monitoring with shortened delay to correct treatment. By developing these we can unlock new possibilities and create more efficient and effective edge AI applications. Each device alone and in synergy will provide critical data for medical decision making and treatment guidance. We will clinically validate these POC devices with four clinical studies in ambulance and emergency room care for i) diagnostic accuracy (sensitivity and specificity); ii) ease-of-use (one-step); iii) speed (≤10 min); iv) compactness; v) low-cost.
In addition to POC devices, we will also develop a POC Device-hospital connectivity platform (DHCP) for urgent care that seamlessly integrates with HIS or clinical workflows. DHCP will have unique features; i) it integrates POC devices into HIS to ensure that data will be available to medical personnel in the hospitals, ii) it notifies the receiving hospital of the estimated time of arrival thus ensuring readiness to receive the patient, iii) it contains an AI-based algorithm, to be developed during POC4TRIAGE, that integrates data from individual devices and uses them for better prediction, iv) it establishes connectivity between different hospital departments personnel through a mobile plug-in (JOIN and JOIN TRIAGE application) that can seamlessly help in data transfer and management of patients in urgent care facilities, v) it develops a flexible communication module to allow automatic transfer of data from POC device to DHCP, vi) it visually presents data from individual devices as well as generated by the AI module to facilitate decision making by ambulance and emergency room personnel.
POC devices and DHCP will provide clinicians with real-time data about patients’ electrical brain activity, mechanosignals, and biomarker concentrations. It will enable clinicians and healthcare professionals to carry out specific medical interventions, and thus personalized treatment with improved patient safety. With POC4TRIAGE, we will successfully advance technologies from proof-of-concept to functional prototypes and integrate them in operational environments and ready them for market launch.
Edge AI computing models that run directly on devices without any need of cloud computing resources provide robust, fast, and accurate triaging estimates, improved response times, better resource allocation and enhanced patient monitoring with shortened delay to correct treatment. By developing these we can unlock new possibilities and create more efficient and effective edge AI applications. Each device alone and in synergy will provide critical data for medical decision making and treatment guidance. We will clinically validate these POC devices with four clinical studies in ambulance and emergency room care for i) diagnostic accuracy (sensitivity and specificity); ii) ease-of-use (one-step); iii) speed (≤10 min); iv) compactness; v) low-cost.
In addition to POC devices, we will also develop a POC Device-hospital connectivity platform (DHCP) for urgent care that seamlessly integrates with HIS or clinical workflows. DHCP will have unique features; i) it integrates POC devices into HIS to ensure that data will be available to medical personnel in the hospitals, ii) it notifies the receiving hospital of the estimated time of arrival thus ensuring readiness to receive the patient, iii) it contains an AI-based algorithm, to be developed during POC4TRIAGE, that integrates data from individual devices and uses them for better prediction, iv) it establishes connectivity between different hospital departments personnel through a mobile plug-in (JOIN and JOIN TRIAGE application) that can seamlessly help in data transfer and management of patients in urgent care facilities, v) it develops a flexible communication module to allow automatic transfer of data from POC device to DHCP, vi) it visually presents data from individual devices as well as generated by the AI module to facilitate decision making by ambulance and emergency room personnel.
POC devices and DHCP will provide clinicians with real-time data about patients’ electrical brain activity, mechanosignals, and biomarker concentrations. It will enable clinicians and healthcare professionals to carry out specific medical interventions, and thus personalized treatment with improved patient safety. With POC4TRIAGE, we will successfully advance technologies from proof-of-concept to functional prototypes and integrate them in operational environments and ready them for market launch.
POC4TRIAGE will be first of its kind to demonstrate that, i) novel POC technologies are highly capable of fast diagnosis of cardiorespiratory, stroke and infection related events with high sensitivity and specificity to be used in urgent care settings ii) are able to quickly transfer patient data to HIS, iii) help in patient triaging to a specific hospital, and thus enable appropriate care immediately upon arrival.