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Megatrends for this decade XXXI

The global wearable medical devices market is expected to grow to $67.2 billion by 2030, at a CAGR of 18.3% during 2020–2030. This forecast was made before the pandemic so it is probably underestimating the growth. Image credit: Prescient Strategic Intelligence

The evolution of healthcare in this decade will be sustained by the convergence of several technology areas, each progressing independently from one another under the pressure of demand from several markets:

  1. ambient, wearable and implantable sensors
    There are already millions of wearables in use that monitors some basic physiological parameters. The starting point was fitness but in the last three years data generated by wearable sensors have started to be processed to derive health indications. The Apple Watch has been the first mass market product to become certified for detecting a medical condition (atrial fibrillation) and the latest version can provide a simplified ECG that can be sent to the doctor for first analyses. Additionally trials are ongoing to detect movement disorders to get insights on the Parkinson disease. Wearable blood pressure sensors, position sensors, bio-sensors are becoming mainstream and will be a common sight by the end of this decade.
    In addition to wearable sensors, ambient sensors will become an important source of health data in hospitals, offices and, most important, at home. In addition to the issues that have to be addressed with wearable when applied to healthcare, ambient sensors raise privacy issues that need to be managed, particularly when used in public spaces, like hospitals and offices. As for any other healthcare related data security, ownership and privacy are crucial (see next point) and it is obvious that data gathered in a public ambient, often with people unaware of what is going on, present even more problems.

    A self-adhesive biosensor to automatically and continuously measure vital signs, body posture and step count, and detects falls. Image credit: Philips

    In the second part of this decade we can expect the rise of implantable sensors. Whilst the growth of health related wearable was driven by fitness that expanded into health, for implantable sensors the adoption will be driven by specific “need to have” like glucose monitoring for diabete patients. Implantable sensors are, obviously, invasive and require (limited) surgery. On the positive side they are fading away from perception and unlike wearables cannot be forgotten. Technology evolution promises extended life cycle through self-charging as well as creating sensors that once implanted in the body will work for a pre-defined period of time and then fade away by biodegrade in the body.
    In all cases data communication from the sensors to the monitoring application takes place via local wireless network that connects to the service provider. Some data processing usually takes place locally, with the smartphone playing an important role (also as gateway) although most processing will take place in the cloud, managed by a healthcare service provider.
    We can also expect that some medicine will embed IoT, sensor, to signal the “swallowing” whilst other biosensors will monitor the effect.

  2. EHR – Electronic Health Record
    Gaia X use case in the Healthcare domain. The diagram shows the management of data in the European Cloud. These data are generated by wearables, hospitals, medical testing and are used by the citizen via apps, by healthcare providers and by researchers. The whole framework ensures privacy and data ownership, supporting data interchange among players and the extraction of “information” at societal level. Image credit: Christian Lawerenz and Prof. Dr. Roland Eils, University Medicine Berlin

    Electronic Health Records are becoming widespread in terms of concept and even in terms of regulatory framework but actual implementation and interoperability are still an open matter. The European Union has an agreed framework for the EHR and member states are required to implement it. The recent Gaia-X initiative should support this data framework and provide the required security measures. There are now a number of use cases in the Gaia X framework, see figure, to apply the EU framework to the healthcare area, Different frameworks, although having the same objective, are being implemented in the US, in China  – Japan, Singapore, and many more Countries. Whilst some Countries are well on the way of full deployment and use of EHR, others are still in the early deployment phase (or haven’t started yet). By the end of this decade the penetration in the G20 Countries should be completed and interoperability shall be achieved. These Countries will steer the application also in the other parts of the world, although it is unlikely that a full deployment can take place within this decade. Based on a recent report from the WHO -World Health Organisation-, but based on 2016 data, the way to go is still long to have full coverage.
    At the same time the pandemic has put a big pressure on both accelerating the deployment and revising the framework for the EHR.
    The key point is the need to ensure an effective (accurate and prompt) flow of data across healthcare institutions (hospitals) across borders to detect early sign of an epidemic and provide effective control.
    This extends to the personal data sphere, since a healthcare “passport” is becoming a key to enable safe travel across borders.
    The EHR is also seen as an important tool to support research and to monitor drugs effects. By having a huge data set it becomes possible to use machine learning and data analytics technique to extract information from the global EHR. This has clearly important societal implications but it is also having huge business implication, hence the need to tackle data ownership issues.

  3. personal digital twin
    The data mirroring and recording the healthcare status/history of a person are part of the EHR of that person. New studies are ongoing to extract local intelligence from those data giving rise to a Personal Digital Twin -PDT- serving the healthcare space.

    Framework for using PDT in the context of the monitoring and control of an epidemic. A PDT act as a gateway separating the person from the context, thus preserving privacy whilst ensuring awareness on societal obligations. A PDT can autonomously interact with other PDTs representing persons that are within an epidemic risk radius, as shown in the lower part of the graphic. The local intelligence can therefore extend to a cluster of local intelligences and the emerging information is shared with the relevant institutions.

    A PDT in the Healthcare space is connected to its physical twin in a variety of ways. First a framework can be defined (a regulatory framework could be used as background) and made available through a healthcare service provider. Insurance companies may have a role if state owned or private owned healthcare institutions are not stepping in. Personally, I think that in Western Countries private companies are the ones most likely to set in motion the adoption of PDT in the healthcare space. A company that is providing healthcare insurance or healthcare services may start by requesting access to the EHR of that person and add to it specific information that is collected at the service subscription time. Furthermore, this first set of data mirroring the person’s healthcare data is going to be extended as new data from exams, visits, prescriptions, monitoring devices are becoming available. This mirrored image of the person is used in the customisation of services and is made accessible, for the relevant parts, to any medical doctor / healthcare provider that needs to interact with that person (duly authorised by the person that remains the owner of the data).By the end of this decade we can expect that the genome sequence will become part of the PDT.
    Monitoring devices, wearables, implants… will continuously providing data that enrich the model and keep it in synch with the physical twin.
    Applications embedded in the PDT leverage data creating a local intelligence. Additionally these apps (or other) interact with the environment through API (this is important to keep the PDT data separated from the environment, private, and to allow the interoperability of a PDT with its environment, including other PDTs, independently of the model / framework used in the PDT itself). Data values are shared on a need-to-know bases (hence a doctor can access certain sets and certain attributes, whilst a researcher may access a different subset and a healthcare institution a different one – as an example a researcher does not need to know my identity, a doctor does not need to know where I have been, only that I have been some places where a given risk exist, a public institution in general does not need my identity if the point is correlating proximity data…).
    A crucial role of the PDT, as a bridge between its physical twin and the context is to create “context awareness”, i.e. to inform its physical twin of threats and of the appropriate countermeasures (behaviour). It is also the PDT that has to inform here and now it physical twin of the regulatory framework (like “since you have been diagnosed as positive to Covid-19, you cannot go out) and in case the physical twin does not comply it should raise a red flag. Notice that this is not a violation of the privacy, nor a Big Brother incarnation. The PDT is actually preserving the privacy of its physical twin provided the latter conforms to the regulation. It is like the blackbox on a rented car that will not disclose my wandering / behaviour as long as I stay within the allowed framework, or speed trap that will not release information on cars unless they overspeed.
    We have already seen (partial) implementation of PDT in China and other Far East Countries where during the pandemic each citizen had to have a digital passport to move around, a passport that was recording data, symptoms, test results.
    PDT at stage V will be able to act proactively by analysing the context in cooperation with other PDTs and Digital Twins of healthcare services, resources shifting, as foreseen in this Megatrend, a significant portion of Healthcare processes to the cyberspace.

    more to follow


About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the New Initiative Committee and co-chairs the Digital Reality Initiative. He is a member of the IEEE in 2050 Ad Hoc Committee. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.