Trend: Digitize me and my health

A trend indicates a direction of change in values and needs which is driven by forces and manifests itself already in various ways within certain groups in society.

Digitalisation is bringing changes to society that offer both opportunities and challenges for health. Digital technology can take advantage of Artificial Intelligence (AI) in the development of applications that support health. There are wearable technologies and remote monitoring devices, telemedicine (e-health) and communication tools, as well as AI-supported diagnostic devices that are providing better care. These will improve prevention, diagnosis, treatment, monitoring and the management of health-related matters. Online services (‘telemedicine’) can reach those who would not, or could not go to a clinic for a physical or mental health consultation.

Devices in the health and wellness sector that use AI - such as phones and watches - have an aspect of internet connectivity (‘Internet of Things’) and are likely to increase in number in the coming decades. The ensuing medical data can suggest personalised care options and feed into innovation for new products. Ultimately, AI promises benefits for patients, including a better understanding of behaviours through ‘Real World Evidence’, i.e. combining big data from sources ranging from environmental to patient data. It offers tailored interventions, timely responses to patients and personalised communications. While AI research in the area of digital health is growing, many questions remain associated with issues surrounding data use, privacy, security and ownership. Digitalisation raises ethical questions; those related to data ownership and those due to known algorithmic biases. Future skills, as well as the autonomy of patients and clinicians is another challenge.
 

This Trend is part of the Megatrend  Shifting health challenges

Manifestations

Developments happening in certain groups in society that indicate examples of change related to the trend.

AI and big data

Advances in science and the use of AI and ‘big data’ could speed up drug discovery and make medicine more accessible and more effective. AI and big data can be used for disease prediction and prevention, to digitise patient records and to understand specific risks (e.g. co-morbidities affecting the severity and aetiology of COVID-19). There are increasing opportunities to collect and analyse big datasets related to health from national health systems and government agencies, large research projects and from devices such as watches, social media and supermarket loyalty cards. Such health data provides a vital resource to help save and improve lives and reduce health inequalities. COVID-19 exemplifies the importance of health data for tracking a pandemic.

Machine learning could aid in the development of new drugs. It could allow an earlier and more precise diagnosis. By modelling which combinations of molecules are feasible in a study, it can reduce the number of laboratory experiments necessary to assess their medical use. Some applications have already been shown to outperform clinicians in the diagnosis of specific medical conditions, though the development of standards and reference systems are necessary before widespread use.

While the demand for using big datasets is growing, questions about control, quality, the analyses, storage, ethics and security present challenges. For e.g. the (potential) diagnosis of a disease that a patient did not seek, is unaware of and did not seek care for raises the questions of “should they be informed, and by who”?  The General Data Protection Regulation addresses the protection of personal data in the EU. However, its implementation has brought barriers for research, which affects patients in the end too. New data access initiatives are underway, but they will need good implementation across Member States to be most effective. 

Signals of change: JRC, EASAC, EC, JRC, EC

Robotics

There are an increasing number of applications of robotics in the health domain, including robotic care and socially-assistive robots, rehabilitation systems and robots that give training to workers. Robots have been used in labs and factories for a long time, but robot-assisted medical procedures and surgeries are increasing and becoming more accessible. Robotic technologies are being used to disinfect hospital rooms, or when in-person contact needs to be reduced due to infectious diseases, such as during a pandemic. In such instances, robots can reduce serious risks for patients and carers.

Wearable robots are human-body-worn devices that can be used for the purposes of augmentation, assistance, or substitution of human motor function. Such human augmentation ultimately leads to ‘cyborgs’, where biological and mechanical parts are integrated.

Humanity is beginning to trust and accept robotic systems more and they offer possibilities for addressing problems such as the ageing population, however, widespread integration of robotic interactions with humans will take time and not everyone will accept ‘robot care’. In addition, they cannot manage all care, for e.g. some physically or mentally disabled care.

Signals of change: EP, Spexor, Cambridge

Small wireless connected devices (Internet of Medical Things)

Wearable monitoring devices, applications and sensor technologies are increasing, driven by the miniaturization of sensors, the ageing population and the increasing trend of internet-of-medical-things (IoMT)-connected devices. The IoMT connects medical devices and sensors in computer networks to each other and to ‘cloud’ data. (Cloud data is not stored directly on a personal device e.g. phone or laptop, but is stored elsewhere and made accessible via the internet). IoMT aims to create a more 'connected health' system and enable better care outcomes. It could help to address health inequalities in the future.

The IoMT can be used for remote patient monitoring, chronic disease management, medication dosing, for following patient adherence to treatment, or collecting data from wearable devices, amongst others. Devices allow the tracking of health parameters and related behaviours, generating ‘Big Data’ fundamental for informing disease prevention, making precise diagnostics, and therapeutic strategies. For e.g. well-known devices that are increasing in use include physical activity trackers, fidelity cards and social media. There is no formal control over much of this data; it is often stored on databases in servers of unknown location, i.e. in ‘the cloud’. The physical environment is often owned and managed by a hosting company in the US or China.

Signals of change: Digital Health Europe, Cordis, Movecare, Businesswire

Interesting questions

What might this trend imply, what should we be aware of, what could we study in more depth? Some ideas:

• What if there is a cyberattack that disables health services, blackmails for ransom, or if personal records are used inappropriately?

• What if a trove of genetic data from a DNA testing service is leaked or sold to an insurance company? What if others get access to your genetic data?

• What if data privacy issues are not considered when there is a need for epidemiology or rare disease studies?

• What policies could increase or reduce risks related to e-health?

• Will health-related data be secure and fully controlled by citizens, including input, monitoring and access? Will it be easily accessible, reliable and understandable? What if data is not comparable, or networks apt for the data amount?

• How can access to open, better information - and using that data more effectively - lead to creating a better overall health system? 

• How can we organise the interoperability needs of personal health databases? 

• What will the future robots look like and will they transform our lives? What if they do - do we need to design new ethical agreements now?

• Where do we draw the line between human and robot/human enhancement?

• What if a robot or AI makes a mistake, or attacks? Who is responsible?

• What if elderly do not cope with IT tools? Will they be left behind?