Today, the healthcare delivery process is becoming a cyber-physical system, emerging in parallel with Industry 4.0. Let us find out which are the main enabling technologies related to the fourth revolution in the world of healthcare.
The term Healthcare 4.0 refers to the application of Industry 4.0 technologies to the field of healthcare, but from a different perspective, as industry manufactures products, while healthcare involves caring for people. Hence, we are talking about the use of new enabling technologies to support the personalisation of healthcare in real time or near real time for patients, operators and social workers.
From patient-doctor meetings to digital medical records
Healthcare systems share many characteristics with manufacturing systems and, just like the manufacturing sector, healthcare delivery has experienced a long history of evolution. Therefore, using our knowledge of the evolution from Industry 1.0 to 4.0, we can describe similar stages to represent the evolution from Healthcare 1.0 to Healthcare 4.0. Healthcare 1.0 refers to the basic patient-doctor encounter, during which a patient visits a clinic and meets a doctor and other team members. Through consultation, tests and diagnosis, the medical doctor provides prescriptions and a therapy plan for the treatment of a disease, as well as follow-up plans.
Together with the important development of health and life sciences, all sorts of new medical equipment and devices have been developed and introduced, which are increasingly used in healthcare delivery: imaging-based test equipment, monitoring devices, and surgical and life-support equipment are increasingly used to support diagnosis, treatment and monitoring. We refer to this development as Healthcare 2.0.
Parallel to the development of IT systems, electronic medical records have been implemented to manage patient care in all units and departments of healthcare organisations, with a major impact on clinical and operational processes. Many manual processes have been computerised and digitised and, using computer networks, remote assistance and telemedicine have become possible. At the same time, electronic visits through patient portals are beginning to replace some face-to-face meetings.
This has led to revolutionary changes in healthcare delivery and we classify this revolution as Healthcare 3.0.
An integrated cyber-physical system equipped with IoT
Today, the healthcare delivery process is becoming a cyber-physical system equipped with IoT, radio frequency identification (RFID), wearable devices, smart sensors, apps, medical robots and so on. This system is integrated with cloud computing, Big Data analysis, AI and decision support techniques to obtain intelligent and interconnected healthcare delivery.
Not only healthcare organisations and facilities are connected, but also all equipment and devices as well as patients. Through AI techniques, proactive treatment, disease prediction and prevention, personalised medicine and enhanced patient-centred care can be envisaged, leading to the Healthcare 4.0 paradigm.
Data collection and processing through sensors and devices
If one considers Healthcare 4.0 as a subset of Industry 4.0, the related solutions should always be based on the six design principles of Industry 4.0: interoperability, virtualisation, decentralisation, real-time capability, service orientation, scalability. A seventh principle, that of safety, protection and resilience, should however be added to these, since healthcare infrastructures are vital to our well-being. Let us therefore see which are the main enabling technologies related to Healthcare 4.0.
The first group includes technologies for collecting and processing data through sensors and devices on or near patients. They include, for example, smartphones and wearable technology (such as smart watches, bracelets and rings and more), the connection of these devices with IoT technologies (and their extension into Internet of Everything – IoE technologies) and data processing through cloud computing, edge computing or fog computing. These sensors and devices collect large amounts of data which can be used to monitor patients and perform interventions.
Heart rate, time spent exercising or sleep time could, for instance, be relevant indicators obtained through technologies of this group.
Adapting the home and work environment
The second group of technologies is aimed at adapting the home and work environment for both patients and caregivers. Such technologies include, for example, Mixed Reality (XR and AR/ VR), which enables the creation of new environments in which physical and digital objects coexist and can interact in real time, and Ambient Intelligence (AmI), which incorporates sensors and actuators into the environment without the patient having to be aware of their existence.Â
XR technologies can be used, for example, for the training of social workers, or for the remote care and therapy of mentally ill patients. AmI technologies can be used to automatically adjust the light or temperature in a home or hospital, depending on the emotional state of the patient. AmI technologies typically involve the use of IoT for communication between sensors and actuators and data processing via cloud or edge computing.
Technologies enabling a global impact on the community
Finally, the third group of technologies relates to those enabling a global impact on the community.Â
They include, for instance, mobile health applications (mHealth), as well as Big Data and AI technologies, which rely on sharing large amounts of data collected from numerous patients to develop realistic models and algorithms. Regarding mHealth, it is important to make sure that the apps patients will use have been clinically tested to ensure their effectiveness. Big Data and AI technologies have great potential to improve the diagnosis, treatment and monitoring of health problems.Â
In the case of mental health, for instance, studies using these technologies typically employ patients’ electronic health records (EHR), mood rating scales, brain imaging data or activity in social media platforms as predictor variables for their models, with the aim of predicting depression schizophrenia, suicidal ideation or attempts. As more and more data are collected and shared, predictive models can be refined to improve diagnosis and start treatment as soon as possible.Â
Effective, reliable and sustainable ways of recording and communicating information between patients, professionals, clinics and service providers will form the basis for further innovations. At the same time, health care will become increasingly personalised, gradually moving away from standard solutions. Patient assessment and intervention will be undertaken after gaining a better and deeper understanding of the condition and environmental context of the individual case.
Source: Controllo e Misura by Publitec
