Periodic Reporting for period 2 - AI4EMS (Artificial Intelligence for Emergency Medical Services: a smart digital assistant for faster and more accurate cardiac arrest recognition during emergency calls)
Reporting period: 2019-05-01 to 2020-01-31
What is the problem/issue being addressed?
In the EU-28, Cardiovascular Diseases (CVD) account for 37% of all deaths, over 1.8 million deaths annually, and it costs the EU economy an estimated €210bn per year mainly due to health care costs and productivity losses. As CVD prevalence and costs are projected to increase substantially, Out-of-Hospital Cardiac Arrest (OHCA) is projected to rise in the same proportion during next years. To date, OHCA is one of the leading causes of death in Europe, and worldwide. Currently, Emergency Medical Services (EMS) dispatchers use set medical protocols as a support to recognize medical conditions, like OHCA, during emergency calls prior to activating the emergency response system. However, EMSs are struggling as calls have increased in Europe from 100 million calls in 2003 to 300 million in 2018, stretching already thinned resources to the limit. Assistant decision tools will be necessary to help EMSs to faster identify time-critical conditions like a OHCA situations, and in a more sustainable way.
Our solution, AI4EMS, is a Software-as-a-Service (SaaS) platform that integrates state-of-the-art speech recognition and machine learning for augmenting in real-time the performance of human EMS dispatchers. AI4EMS is the first and only smart digital assistant for EMS dispatchers that supports triage decision-making process by (1) real-time processing and analysis of emergency calls; (2) real-time recognition of cardiac arrest in an evidence based process for Danish, English, French and Italian (and more languages to come); and, (3) presenting the most important insights to dispatchers in a user-friendly way. With AI4EMS, OHCA recognition is faster (reducing the EU average of 3’39’’ to 50 seconds) and more accurate (increasing the EU average of 73.9% human accuracy to 95%).
Why is it important for society?
Healthcare is like every other market driven by the relationship between supply and demand, and patients demand medical expertise since nobody wants mediocre medical treatment. This means that every job function is usually highly specialized, and it takes a lot of training and retraining to keep every employee up to date with best practices. This drives up the cost of treating each patient, which in turn makes resource utilization and allocation more important than ever before. Resources that are already scarce considering the growing aging population and the widely documented imbalances and shortages of health workforce in the European region, and globally. In this scenario, Emergency Medical Services (EMSs) are no exception. Effective decision assistant tools will have a large impact in healthcare, bridging the widening gap between medical professional resources and patients' needs.
What are the overall objectives?
AI4EMS aims to impact the economy and society at large by improving access and quality of healthcare. Our goal is to disrupt the Artificial Intelligence (AI) market for healthcare becoming world leaders in EMS artificial intelligence, saving lives and unnecessary costs. Sales will render revenues of €86.7 million in the first five years of commercialization and a total of 127 new jobs will be created by 2024, generating an estimated total economic benefit for all our end users of €108m.
In the EU-28, Cardiovascular Diseases (CVD) account for 37% of all deaths, over 1.8 million deaths annually, and it costs the EU economy an estimated €210bn per year mainly due to health care costs and productivity losses. As CVD prevalence and costs are projected to increase substantially, Out-of-Hospital Cardiac Arrest (OHCA) is projected to rise in the same proportion during next years. To date, OHCA is one of the leading causes of death in Europe, and worldwide. Currently, Emergency Medical Services (EMS) dispatchers use set medical protocols as a support to recognize medical conditions, like OHCA, during emergency calls prior to activating the emergency response system. However, EMSs are struggling as calls have increased in Europe from 100 million calls in 2003 to 300 million in 2018, stretching already thinned resources to the limit. Assistant decision tools will be necessary to help EMSs to faster identify time-critical conditions like a OHCA situations, and in a more sustainable way.
Our solution, AI4EMS, is a Software-as-a-Service (SaaS) platform that integrates state-of-the-art speech recognition and machine learning for augmenting in real-time the performance of human EMS dispatchers. AI4EMS is the first and only smart digital assistant for EMS dispatchers that supports triage decision-making process by (1) real-time processing and analysis of emergency calls; (2) real-time recognition of cardiac arrest in an evidence based process for Danish, English, French and Italian (and more languages to come); and, (3) presenting the most important insights to dispatchers in a user-friendly way. With AI4EMS, OHCA recognition is faster (reducing the EU average of 3’39’’ to 50 seconds) and more accurate (increasing the EU average of 73.9% human accuracy to 95%).
Why is it important for society?
Healthcare is like every other market driven by the relationship between supply and demand, and patients demand medical expertise since nobody wants mediocre medical treatment. This means that every job function is usually highly specialized, and it takes a lot of training and retraining to keep every employee up to date with best practices. This drives up the cost of treating each patient, which in turn makes resource utilization and allocation more important than ever before. Resources that are already scarce considering the growing aging population and the widely documented imbalances and shortages of health workforce in the European region, and globally. In this scenario, Emergency Medical Services (EMSs) are no exception. Effective decision assistant tools will have a large impact in healthcare, bridging the widening gap between medical professional resources and patients' needs.
What are the overall objectives?
AI4EMS aims to impact the economy and society at large by improving access and quality of healthcare. Our goal is to disrupt the Artificial Intelligence (AI) market for healthcare becoming world leaders in EMS artificial intelligence, saving lives and unnecessary costs. Sales will render revenues of €86.7 million in the first five years of commercialization and a total of 127 new jobs will be created by 2024, generating an estimated total economic benefit for all our end users of €108m.
Work performed:
During the first period of the AI4EMS project, we primarily focused on the technology maturation and final prototyping of our product. This entailed a major allocation of resources and time towards the optimization of several aspects of our core product (speech recognition model, triage classifiers and cardiac arrest detection model, user interface and technical configuration/ integration work). In line with the planned timeline of the project the first milestone 'Finalization of piloting requirements' was reached. The European piloting sites selected through our collaboration with the European Emergency Number Association (EENA) are the EMSs of AREU (Milan, IT) and SAMU74 (Annecy, FR).
During the second period, we continuously gathered feedback and improved our solution for our end users in terms of user interface, function extension and customization. AI4EMS is moving towards its commercial readiness expected for the end of the project, Q1-2 2020. To that regard, we expanded effective Sales and Customer support unit as we approached market launch and set up a marketing team for communication activities.
Main results of the project:
1) AI4EMS technology has been transformed into product, tested in real environment, deployed for end users and developed during the piloting activities. We engaged 5 piloting site which are Copenhagen, Milan, Annecy, Seattle and Perth EMS.
2) We covered 5 languages including Danish, English, French, Italian and Swedish.
2) Academic papers regarding our achievement and piloting results have been published.
3) We applied for a patent during the second period of the project.
4) We carried out many communication activities and commercial explorations, reaching out to over 50 potential customers from 18 countries.
Exploitation and dissemination:
A wide range of communication activities has been carried out during the project period in order to raise awareness on the project, reaching out to the general public as well as selected target groups (PSAPs, Policy makers, Investors, scientific community, etc.). We attended key industry and scientific conferences and have been working towards publishing research articles, some of which have been already published: one scientific article and four conference papers. We built accessible dissemination materials such as demos and organized training activities and workshops. We also constantly follow the patent opportunity from our inner development. Besides, we have been referred to by many presses such as MIT technology review and won awards including 2019 Global Startup Awards and 2020 Digital Europe Unicorn Award.
During the first period of the AI4EMS project, we primarily focused on the technology maturation and final prototyping of our product. This entailed a major allocation of resources and time towards the optimization of several aspects of our core product (speech recognition model, triage classifiers and cardiac arrest detection model, user interface and technical configuration/ integration work). In line with the planned timeline of the project the first milestone 'Finalization of piloting requirements' was reached. The European piloting sites selected through our collaboration with the European Emergency Number Association (EENA) are the EMSs of AREU (Milan, IT) and SAMU74 (Annecy, FR).
During the second period, we continuously gathered feedback and improved our solution for our end users in terms of user interface, function extension and customization. AI4EMS is moving towards its commercial readiness expected for the end of the project, Q1-2 2020. To that regard, we expanded effective Sales and Customer support unit as we approached market launch and set up a marketing team for communication activities.
Main results of the project:
1) AI4EMS technology has been transformed into product, tested in real environment, deployed for end users and developed during the piloting activities. We engaged 5 piloting site which are Copenhagen, Milan, Annecy, Seattle and Perth EMS.
2) We covered 5 languages including Danish, English, French, Italian and Swedish.
2) Academic papers regarding our achievement and piloting results have been published.
3) We applied for a patent during the second period of the project.
4) We carried out many communication activities and commercial explorations, reaching out to over 50 potential customers from 18 countries.
Exploitation and dissemination:
A wide range of communication activities has been carried out during the project period in order to raise awareness on the project, reaching out to the general public as well as selected target groups (PSAPs, Policy makers, Investors, scientific community, etc.). We attended key industry and scientific conferences and have been working towards publishing research articles, some of which have been already published: one scientific article and four conference papers. We built accessible dissemination materials such as demos and organized training activities and workshops. We also constantly follow the patent opportunity from our inner development. Besides, we have been referred to by many presses such as MIT technology review and won awards including 2019 Global Startup Awards and 2020 Digital Europe Unicorn Award.
AI4EMS hinges on the basic proposition of proving the proficiency of a machine learning model functioning in a realm of multi-modal data, usually only operated by humans. We combines the Machine Learning technology with Natural Language Processing to provide AI tools for EMS dispatchers. Before deploying our technology, the triage process was mainly done by human decision and manual input. And AI4EMS not only improves dispatchers' efficiency, but also saves more lives by increasing accuracy. As it is proved at the end of project period, we took the extremely promising technology out of the realm of untested and highly localized, all the way to the mass market as it works across location, dialect and language, providing second opinion for EMS dispatchers and preparing the healthcare system for future emergency accidents.
Potential impact:
We leverage AI technology to save limited medical resources and save lives which are the most important recourse for human society. Further more, our project promotes the utilization of AI technology in healthcare system and the progress of medical intelligentization. This will also impact the global debate on what tasks and roles are uniquely human, and how we define our jobs in the future healthcare system, which is an extremely important debate that Corti as a company will need to play a role in shaping and facilitating, as the technology matures and expands to new markets and use-cases, changing our perception of clinical care and good emergency management.
Potential impact:
We leverage AI technology to save limited medical resources and save lives which are the most important recourse for human society. Further more, our project promotes the utilization of AI technology in healthcare system and the progress of medical intelligentization. This will also impact the global debate on what tasks and roles are uniquely human, and how we define our jobs in the future healthcare system, which is an extremely important debate that Corti as a company will need to play a role in shaping and facilitating, as the technology matures and expands to new markets and use-cases, changing our perception of clinical care and good emergency management.