AI ENABLED HEALTHCARE SERVICES DURING CROSS-BORDER MEDICAL EMERGENCIES AND REGULAR PATIENT SERVICES

The goal of ESCORT project is to bring prominent advances in IoT, Wearables, Artificial Intelligence (AI), and Machine Learning (ML) to facilitate enhanced patient treatment services for the enrichment of patient experience and European health and care service resilience.

The proposed innovations to be carried out in the project will assist int eh development of ethics-by-design policy intervention based on the evidence collected and analysed from the pilot studies.

Spanning across 36 months, the ESCORT project brings together the experiences of leading healthcare service providers from five (5) European (Greece, Italy, Belgium, Sweden, Ireland) and one (1) associated member (Israel) states specialising in emergency medicine and offering regular health and care service providers across six (6) countries (AUTH, UCSC, VUB, KI, NH, MDA).

The development of digital services will be designed in consultation with the stakeholders including patients, patient advocacy groups, health and care service professionals, health and care providers.

The development of technology will be supported by research experts (UTH), complementing the expertise of SMEs (RINI, MKM, COSI, MDS). The overall integration of the proposed tools and services will be achieved by INTRA.

The origins of ESCORT conceptualisation can be traced back to the supplier network creation within NO-FEAR (CSA) project in which validation of individual tools catering the demands of urgent and emergency medicine has been carried out.

Building on the success of the NO-FEAR project, the workplan of ESCORT has been designed to enable integration of core technologies to develop digital services addressing patient needs and requirements.

To achieve this project, the ESCORT consortium has identified three (3) scenarios which represents the pre-, and post- clinical patient care and the needs of victims requiring support for urgent and emergency medical care. All the scenarios will be validated across six (6) countries.