User:Jytdog/MedThings

COPIED from https://en.wikipedia.org/w/index.php?title=Draft:Internet_of_Medical_Things&oldid=863329798 in case it gets speedied. This is fixable with a bunch of work

Technologies are able to reduce cost, boost productivity and increase overall quality in a variety of industries. Within the medical sector, new technologies enable practitioners to reduce cost, but also prevent and help manage chronic illnesses. These devices constantly monitor health indicators, auto-administer therapy or track real time health data. This empowers patients to manage their own therapy and provides a wealth of information to practitioners. These new technologies are enabled by high speed internet and the application of state-of-the-art apps to manage the patient’s needs, all these new breakthroughs are part of the Internet of Medical Things (IoMT). . They make it possible to track patients in real-time, identify potential incorrect drug use or diseases without the need of a practitioner, data collection for research, monitoring during rehabilitation and remote elderly care .

The underlying phenomena of IoMT is the broader concept of Internet of Things (IoT), which is a network of physical devices, ingrained with electronics, sensors and network connections, which enables objects to collect and exchange data with each other. However, this is only a partial definition, as IoT is also seen as a result of a global network of interconnected smart objects enabling new businesses and markets to establish themselves. One of these markets built upon the IoT foundation is the earlier mentioned IoMT. Therefore, IoMT can best be described as the application of IoT, and all that it entails, for medical and health related purposes.

Enablers
For IoT and IoMT to exist, there are several components that enable the seamless functioning of all the objects within the network, being: hardware (sensors and communication hardware), middleware (storage and computing tools), and visualization (dashboards and apps). Radio Frequency Identification(RFID) are microchips made for wireless data communication, both active and passive. Passive RFID tags act as an electronic barcode, sending the chips ID to the required reader. Active RFID chips (alias NFC chips) are more interesting for IoMT since they are able to not just send, but also to write information and communicate with each other. Wireless Sensor Networks (WSN) is a combination of low power integrated circuits, wireless communications and high-end RFID chips. It makes it possible to construct a network of intelligent sensors that are able to collect, process, transfer and analyse valuable information in different environments. Addressing schemes is the ability to identify and control millions of unique devices. All devices have a unique IP address and all new connected devices need to have a unique one as well: this is made possible by a new IPV6 (a network layer protocol) addressing scheme. An addressing scheme makes it possible to identify and distinguish the devices, operate them individually and use the data on a personal level. Cloud computing makes it possible to perform large scale, complex and flexible computing. Cloud computing is performed without expensive hardware, software and dedicated space. This technology is fundamental for the storage and analysis of data in the big data paradigm. The next step in data storage and transportation is edge computing, which stores, analyses and pre-processes data in a distributed manner closer to the source. Visualisation is critical for IoT and especially for IoMT, since it allows a smooth interaction between a person, be it a patient or practitioner, and the application. New visualization tools, 3D screens, make it possible to provide more meaningful information, enabling users to convert data into knowledge.

Characteristics
The IoMT has particular characteristics that distinguish this new type of digital technology from traditional ones. The following characteristics make Io(M)T distinctive:

Homogenization and decoupling
Because of homogenization & decoupling, digital devices can use similar digital systems to connect easily with other kinds of platforms or applications. Being able to remove these tight couplings between information types and their storage, transmission, and processing technologies has significantly improved possibilities within health care in various ways. Medical devices are now all capable to help in monitoring, diagnosing, treating diseases, and other health related conditions.

Connectivity
Connectivity is “the degree to which things are interconnected”. Connections can be with other users, other applications and between firm and customer. Currently, as the medical devices are equipped with Wi-Fi, they can communicate from machine to machine, or with doctors and patients as virtual assistance, changing the way devices and services connect on all these three levels. These medical devices and applications used to be offline singular networks without the opportunity to connect to other devices, users or networks. Thus, the digitalization has brought enormous possibilities to IoMT.

Reprogrammable and smart
IoMT gives medical devices the opportunity to have a digital interface connected to Wi-Fi. Therefore, the devices could be reprogrammed with new or better updates, or even reboot completely when a device fails, instead of having to buy new expensive medical devices. On top of this, medical devices can be programmed to become smart: “AI in health represents a collection of multiple technologies enabling machines to sense, comprehend, act and learn, […] health AI today can truly augment human activity”. This shows that reprogrammable and smart devices will provide seamless opportunities.

Digital traces
Medical devices with IoMT technology can store and transmit all sorts of data. Interactions with customers, between devices, or with the main data mainframe will create some form of digital trace. These digital trace data are defined as “evidence of human and human-like activity that is logged and stored digitally”. As these medical devices have their own personal addressing schemes the data can be specified and analysed in depth.

The mentioned characteristics above show why IoMT can be considered as a digital technology. They are applicable for digital health services and have several consequences for products and services using the IoMT technology.

Consequences for new products and services
This part will further build on the characteristics explained earlier, by focussing in depth on the consequences for the products and services. The medical devices using technology of IoMT can be grouped under three types of products and services: stationary medical devices (eg. MRI scanners and X-ray’s), implanted medical devices (eg. Pacemaker) and wearable external medical devices with connection to health related aspects (eg. FitBit). These devices will be further used to explain the consequences for medical products and services as a whole. To two particular consequences of homogenization and decoupling are noteworthy. First, the low marginal costs: as all machines can now transmit, store and compute digital data, this data can be sent and stored with cloud computing. Cloud computing is a low cost, flexible and fast way of doing so because of usage-based pricing. Supported by Moore’s law, which states that computing power exponentially increases, costs decrease for all medical businesses software and hardware. This greatly impacts the medical sector as the high costs of stationary medical devices will decrease over time. Secondly, homogenization and decoupling enables a convergent user experience, which is all about bringing together previously separate user experiences. The convergence is not only between a user and a company, but can be within whole industries. Moreover, interoperability is a combination of multiple characteristics of IoMT technology. Because medical devices are interconnected and have easy data sharing, they can communicate with each other. Thanks to decoupling and medical devices a standardized and open interface is possible because all data from medical devices can be stored together creating one connected system. Implanted medical devices could be connected to the whole system so the customer can see data from their home, while it also goes to the company and doctors who can track patients’ progress. As all data gets shared to a connected system, these devices can learn in a smart way to improve patients’ plans or decrease errors. In this way, IoMT technology combines digital and physical device to create new possibilities for the medical industry. With this interoperability, it becomes easier to combine all data and customers together and thereby increasing the value of IoMT. This phenomenon is called network externalities and that value can be described as “a user’s benefit from using a good increases with the number of other users of the same good”. When the complementary goods are important, these network externalities also arise. All medical devices connected will therefore have value for the network. Possibilities for all sorts of medical devices using the IoMT technology are therefore enormous and can be brought to an entire industry. With all that data and interoperability, new emerging products and functionalities arise. Medical devices used to be extremely hard to update. With the medical sector evolving and modernising so fast, it is easy to get outdated devices. Medical devices can be reprogrammed or become smart, therefore products can be upgraded for a longer period. In the healthcare industry and insurance industry, companies started using these characteristics to converge with products and services.

Impact of IoMT on healthcare industry
For the past years, IoMT transformed the healthcare industry, by using technology to their advantage, until developing what is now called ‘Smart Healthcare’ or “healthcare IoT”. The introduction of IoMT in the medical sector led to the creation of a digitized healthcare system, made possible by the connection of available medical resources and healthcare services with IoT. Equipped with Wi-Fi, medical devices are now capable of machine to machine communication, considered as the basis of IoMT. In 2015, there were 4.5 billion IoMT devices in the world, and this number is expected to reach between 20 and 30 billion in 2020. This expansion of IoMT is driven by the over-65 population needs, supported by the anticipation that demand for personal healthcare application will strongly increase. It is also noteworthy to mention that between 2007 and 2050, the over-60 population is expected to double until 2 billion people, while the growing and aging population is expected to reach 9.7 billion people in that same period. IoMT is now permitting doctors, patients and others involved (i.e. guardians of patients, nurses, families, etc.) to be part of a networking system: the records are now digital and saved in a database, allowing doctors and the rest of the medical staff to have access to the patient’s information. Moreover, IoT-based systems are patient-centered, which involves being flexible to the patient’s medical conditions : these new user-based care models emphasize the services quality and sociotechnical coordination.

Physical components support and drive the disruption in the healthcare industry, such as: “wearable devices, remote patient monitoring, sensor-enabled objects, medication-tracking systems, medical supply and equipment inventory tracking”, and much more. First of all, sensor nodes are able to connect in-home monitoring devices to hospital-based systems in order to systemize medical processes. For example, aging individuals can benefit from heart rate monitoring through IoT ultrasound-based technology, or use wearable devices like skin patches, glucose monitors , biometric stamps reporting the user’s vitals, and smartwatches for heart monitor rate and movement tracking. Moreover, the use of mobile devices to support medical follow-up led to the creation of ‘mhealth’, used “to analyze, capture, transmit and store health statistics from multiple resources, including sensors and other biomedical acquisition systems”. These components – and this is what makes IoMT such disruptive in the healthcare sector -, were developed to process information in different environments, such as hospitals and households. Specific IoMT examples in this industry are: “remote patient monitoring of people with long-term or chronic conditions; tracing patient prescription orders and site of patients admitted to hospitals; and patients’ wearable health devices, which can transmit information to hospitals”.

After explaining the IoMT physical products disruption, there is also another consequence, namely industry convergence. Incumbents are starting to get new competition from other industries. For instance, Google, which is a data analytics company, is starting to launch healthcare products for patients, but also for practitioners and manufacturers. Researchers from McKinsey state that applying big data strategies to make data driven decisions could generate to a $100 billion in value annually in the US-healthcare sector alone. Google leverages their data analytics capabilities, something that traditional healthcare players cannot match, to benefit from this multi billion market. Another example is Qualcomm, a US based chipmanufacterer, which is investing in IoMT based healthcare solutions, indicating that other industries are seeing good potentialin the healthcare industry for potential growth.

Opportunities
IoMT changed the whole dynamic of the healthcare industry, by introducing a win-win situation between patients and healthcare professionals. Concerning the patient side of the win-win situation; almost 60% of the medical industry is currently using IoMT devices and observed improved patient care. Thanks to the data collected, the reporting generated is more reliable and improves diagnoses, while the automation of the processes decreases human error and fraudulent reporting. It emphasizes the importance of the data involved in the process. The wearable devices in particular, provide useful information which allows the patient to manage their own health, and receive help in emergency cases via their mobile. Regarding the healthcare professional side, wearables help researchers and medical practitioners to better understand and treat their patients. In addition, wearables are useful to healthcare workers who may have to handle many patients. As a consequence, users benefit from better personalized healthcare. This is due to easier access to health information for practitioners and thereby improving the patient experience. With a level of care personalized like the patient’s DNA, IoMT is expected to provide a ‘precision medicine’, “with predictions and interventions dependent on the accumulation and analysis of data”. For example, ‘Seymour’, by CellScope. This is a mobile application which provides a personalized assessment of a child’s health issue with an in-app guidance and the possibility to upload photos. Consequently, a professional doctor will give their opinion in less than two hours for only $10. This concept of interconnectivity made patients’ lives easier. Indeed, it is providing the necessary remote monitoring patients need, while at the same time increasing the quality and efficiency in the field of healthcare. By using different technologies such as Bluetooth and near-field communication, IoMT transformed the preventive medicine landscape and improved the lives of patients, especially the ones suffering from chronic illnesses. It also aims to reduce healthcare costs for patients by, for instance: eliminating the visits to the doctor’s office, reducing the lengths of hospital stays, and speeding up the diagnoses.

Challenges
The main challenges encountered by the healthcare sector with the introduction of IoMT are the security problems and the patients’ privacy. Indeed, medical information is the most critical data that should be kept safe. These innovative technologies and standards should address privacy and security features, for the network, the users, the data and applications. This concern is supported by the fact that, in 2014, about 70% of the IoT devices were defenceless to cyberattacks, especially with cloud-based data collection. To illustrate this point, we can mention the baby-monitor feeds publicly viewed online, due to hacking. In fact, each IoT device contains on average 25 vulnerabilities to this kind of attacks. Moreover, healthcare providers have to be able to analyse the big amount of patient data. Consequently, they have to find a solution to set up processes, acquire the right technologies to handle various data types generated by different devices, and use this patient’s information across multiple channels. Another concern of the IoMT for the healthcare industry is at a dual-level: the individual acceptance (i.e. patient’s willingness to commit to the technology) which is crucial for the organizational adoption, and the use of the IoMT within the sector. It has been observed that the medical industry, although based on scientific innovation, is slow in the digitization process: doctors are reluctant to this change, and professionals and technology providers are disconnected, causing the slow adoption of digital healthcare processes. As an example, only ⅓ of U.S. practitioners are willing to share the medical data with their patients. Thus, it appears essential that providers educate both patients and practitioners to adapt to the doctor-patient relationship change. To conclude, the conventional healthcare system has shown its limits and has become increasingly dysfunctional, with issues such as cost pressures, fragmented systems and disconnection among providers, patients and payers (public and private). Within the traditional medical system, the hospitals were considered as the main centre for patient care; the doctor’s office was home to most of the diagnoses, treatments, and prescriptions. Following this conventional healthcare system, there was no possibility of mobility for patients, as they were reliable on certain locations and practitioners. Thus, IoMT is to be considered as a service focusing on “early disease detection, and homecare rather than the exclusive clinical one”. Moreover, sick care cannot be the only focus anymore for health providers: they must educate patients to manage their own wellness. In the future, new IoT devices will make their appearance within the medical sector, such as implants under the skin or on internal organs, which will be able to adjust medicine dosages accordingly to multiple variables recorded for example. This is one of the various examples which shows that the future of healthcare is oriented towards the patient convenience and needs, rather than only depending on traditional clinical settings.

Impact IoMT on insurance industry
Although this sector based their success on a data-driven business model, the industry has been slow to adopt digital technologies, and in particular IoT. The health insurance industry is heavily dependent on understanding and analysing risk by gathering and observing data. Until recently, this usually involved finding correlations between frequency and severity of insurance claims and finding data describing insurable assets. The Io(M)T affects the insurance industry by providing access to better and new types of dynamic information. This includes sensor-based solutions such as biosensors, wearables, connected health devices and mobile apps to track customer behaviour. These new types of data brought forward by the IoMT have several consequences for the health insurance sector.

Opportunities
Firstly, IoMT could lead to more accurate underwriting, which is a central function for insurers. Underwriting is the process of assessing risk and ensuring that the cost of the cover is proportionate to the risk involved. For underwriting, the insurer needs to uncover as much data as possible and then make sense of it through finding correlations, in order to identify the most attractive risks. Previously, this was done based on backwards-looking claim data and historical risk studies. However, the IoMT now delivers real-time data and thus, a more accurate risk assessment. This technology causes a shift in the underwriting process from risk avoidance to proactively identifying risk. A company that currently leads the market in predictive analytics for IoT enabled biometric data is EVŌ : this U.S. based SaaS company helps insurers to figure out how healthy their clients actually are. Secondly, which is related to improved underwriting, IoMT could revolutionise how health insurance is priced. IoMT makes it possible to improve knowledge of behaviour-related risk and sanctioning higher-risk profiles. The technology enables to assess risk on the basis of data about specific consumers, rather than the general population. This could, in turn, lead to personalized and dynamic pricing. For example, the U.S. based health insurance company Oscar Health started with financially rewarding people for a healthy lifestyle, already in 2014. Through a partnership with wearable device company Misfit, Oscar Health linked customer biometric information to their health insurance. If a consumer completed the daily physical challenges, a financial reward could be earned. Thirdly, IoMT offers the potential to deliver non-insurance services. The IoMT and the wealth of data it delivers can help conventional insurance companies to make a shift towards a “service” provider. The IoMT enables insurers to move from a low-frequency transaction-based model to a much more interactive model built on services to provide prevention, advice, and rapid assistance and support. For example, the U.K. based VitalityHealth gives its customers advice on the best way to eat and exercise. The transition from insurance company to “Health Coach” is a fundamental change in positioning for insurers, but it has the potential to generate new sources of revenue and the possibility to reinforce customer relationships. Summarising the potential positive implications, IoMT allows insurers to access dynamic real-time data and a variety of data. Moreover, the technology facilitates predictive modelling instead of past-based. The insurers can also leverage the IoMT to deliver a dynamic and customized value proposition, such as dynamic pricing and product configuration. Lastly, the IoMT allows firms to diversify and move beyond the conventional insurance industry by offering services based on prevention and coaching.

Challenges
However, in order to capture value from the previous implications, insurers need to overcome some challenges. The first one is that insurers need to redefine their customer experience. Traditionally, it has not been a strong point of the insurance industry. This is a natural consequence of a relationship that is based on human contact only when something unpleasant happens. About half of the customers of insurance companies did not have any interactions with the firm for 18 months. Compared to other industries, the interactions of consumers with insurers is indeed extremely low.

Consumers demand a simpler and more direct relationship with their insurer. Also, the online experience of insurance companies lags far behind other sectors. The growing online customer expectations due to other online experiences are currently not being fulfilled by insurers. The IoMT allows insurers to interact more often with their customers and to offer new services based on the data collected. However, this is a challenge for an industry that used to delegate customer relationships to agents or brokers. Moreover, one of the advantages mentioned was market segmentation and pricing. However, this also embodies ethical concerns. Micro-segmentation and pricing would penalise people suffering from unfavourable genetic predispositions and who cannot reduce their exposure to risk by simply changing their behaviour. Insurers, therefore, need to be extremely cautious with applying models to certain populations to avoid excluded consumers from an insurance at affordable costs. Another challenge of the IoMT is to further reach acceptance by users. In order to do so, insurers need to define valid security, privacy and trust models suitable for the IoT application context. Security, data anonymity, confidentiality and integrity need to be guaranteed. Regarding privacy, both data protection and users' personal information confidentiality have to be ensured, because devices may manage sensitive information. Finally, trust is a key issue to further reach acceptance by users. The fourth challenge relates to (dis)economies of scale. IT projects traditionally benefit substantially from economies of scale. However, increasing the size of IoT projects can lead to diseconomies of scale. Unlike traditional IT projects where variable costs are extremely low, maintaining a growing network of connected devices leads to ongoing maintenance costs. Besides the increased costs, the wealth of data the IoMT creates needs to be analysed to obtain meaningful insights: however, it provides more data than companies traditionally can handle. Health insurers must make their business future proof by updating their IT infrastructure for huge data inflows To conclude, the IoMT has the potential to both enhance and threaten health insurers’ business models. In order to benefit, insurers need to redefine their value proposition in terms of offering and customer experience. Moreover, insurers need to be prepared for a shift toward prevention oriented assistance services and need to invest in the IT infrastructure to deal with the increasing amount of data.

Firms’ reactions to IoMT
The previous chapters introduced the IoMT and its characteristics, and its disruptive potential for the healthcare and insurance markets. This chapter will look at firm-level dynamics, will explain how incumbent firms can respond to the IoMT and how new players exploit the opportunities of this digital innovation.

Incumbents' responses
It is often stated that incumbents generally suffer in the face of radical innovations, due to commitment to current value networks and technological paradigms , thereby giving new entrants a higher chance of success. However, some incumbents have successfully adapted and responded to radical innovations in the past and either preserved or recaptured their previous market leadership. As a matter of consistency, the following part uses examples from the insurance industry and healthcare sector to substantiate the arguments. There are multiple responses for incumbents to survive disruption in their industry. However, before deciding on their response, incumbents need to calculate the value of winning: managers of established firms should first assess whether their industry, or a sub-segment of their industry, is still an attractive place to compete. Moreover, incumbents are encouraged to leverage existing capabilities. Established insurance firms have a vast array of capabilities that newcomers to the market cannot match. Therefore, managers should analyse how their existing capabilities can be used to slow or delay the onset of disruption. If current capabilities can be used or extended, it may make sense to expand into a new market. But most important is that innovation has to become a top priority for incumbents. There are three ways how incumbents in the healthcare industry and health insurance sector can boost innovations.

Skunkworks
Incumbents can set up a structurally differentiated business unit (e.g. skunkworks or spin-off) to explore the IoMT. This new unit, unburdened by the existing customer base’s insatiable demand for betting performing products and the traditional stage gate process, can freely pursue the disruptive opportunity. . For example, insurance giant Aviva has opened 'digital garages' in Singapore and London to explore new technologies. In the healthcare industry, Siemens rebranded their healthcare division to Siemens Healthineers in 2015 and went public in 2018, to construct a individually operating entity with its own organisational structure to better withstand the coming paradigm shifts. Since 2015, Healthineers has been working on their Digital Ecosystem to connect healthcare providers, manufacturers and patients.

Internal venture-capital
Incumbents can invest in start-ups by setting up an internal venture-capital arm. Incumbents often have the resources to buy into start-ups to learn more about new technologies. For example, France-based AXA, Italian based Generali, and U.S. based MetLife and MassMutual all launched their own venture capital fund ranging from €100 million (MetLife) to €1.25 billion (Assicurazioni Generali). Another example from the healthcare industry comes from General Electric, that launched a venture capital subsidiary called GE Ventures in 2013. The portfolio of GE Ventures consists of more than 30 companies. One of the companies that received funding is Chrono Therapeutics. This firm develops a digital health solution, enabled by the IoMT, that combines a wearable drug delivery system with real time behavioural support delivered via mobile phone. The system is specifically tailored and provides the right amount of drug at the right time to maximize effectiveness.

Partnerships
Incumbents can invest in strategic partnerships. It is important that incumbents start to think in ecosystems and team up with players in other industries to create new solutions together. Strategic partnership can be a good solution if incumbents do not have the resources to pursue innovation internally. AIG, for example, has formed a partnership with IBM to boost its capabilities in risk analytics and cybersecurity. Within the healthcare industry, Philips also used the partnership strategy to leverage the new IoMT technology. The company formed three strategic partnerships to leverage IoT for the creation of their Digital health platform: Healthsuite. Healthsuite is positioned to be the next generation's digital enabler of personal health, with a cloud based platform build for medical practitioners, patients, researchers and manufacturers. Philips formed a partnership with Salesforce in 2014 to build the cloud based platform with Amazon Web Services to enhance connectivity and with the startup Validic to expand Philips’ portfolio of connected devices