The Consumerisation of Healthcare — Flux Insights.co.uk

Emerging technologies such as Artificial Intelligence, Virtual Reality, Quantified Self and Consumerism, the Blockchain and Big Data. Are key enablers of the mobile health (mHealth) revolution.

Over the past few years, the adoption of mHealth apps has risen significantly with more than 325,000 mHealth apps available for Apple and Android smartphones.

Health, fitness and technology companies market approximately 40,000 of the health-related apps. According to the Healthcare Information and Management Systems Society.

There are a total of 325,000 mHealth apps available worldwide, in various app stores, with an estimated 3.7 billion downloads in 2017. The number of downloads in 2010 was 200 million an increase of approximately just over 1800% over 7 years.

mHealth apps widen access to healthcare information and have fewer temporal, geographical and organisational barriers.

The industry was valued at US30.2bn with a CAGR of 38.8% in 2020. It is expected to increase in value to US$236bn by 2026 according to Research and Markets.

mHealth is the practice of using smartphones to continuously monitor one's wellbeing. The overall utility of these apps can be defined by the 4P’s. Personalisation, prevention participation and predictive features and functionalities.

To aid in the delivery of non -traditional healthcare to users through the deployment of preventative health solutions. The improvement in the treatment of health outcomes and lifestyles, through the delivery of personalised solutions and the active participation of users.

Along, with the leveraging of data for predictive purposes to aid in the minimisation and elimination of chronic diseases.

mHealth apps enable the replacement of traditional healthcare delivery models, with a consumer and patient-centric care model.

That deploys a combination of older technologies such as videoconferencing facilitated by devices such a smartphones and tablet computers but also newer wireless technologies.

Such as wearable sensors attached to the body by adhesives to record such bodily functions as heart rate, respiration and physical activity levels.

Ingestible medications embedded with sensors that can send digital signals to armbands worn by patient and wireless stethoscopes that enable doctors to hear the heartbeat of patients remotely.

These technologies are at the heart of promoting remote patient monitoring. And are also deployed by the developers of mHealth applications targeting consumers.

Growth in the adoption of mHealth platforms is driven by the extensive penetration of smartphones globally and the recommendation of verified mHealth applications by healthcare professionals. Other key drivers are listed below.

Factors Driving the Adoption of mHealth Apps

Key Technologies in mHealth

Consumers perception regarding their health is changing, as they begin to take more responsibility for their health and collaborate with healthcare professionals.

mHealth providers, their apps and the devices consumers and patients use to monitor and track their health in real and semi – real-time. Will play a major role in empowering consumers to make the right health choices for themselves and their families.

In a survey, of 3,014 adults, created by The Pew Research Centre, in 2013, titled "Tracking for Health" 60% of the adults indicated that they used mHealth apps to track their weight, diet and or exercise.

Thirty-three percent tracked blood pressure, sleep patterns, headaches or other healthcare indicators. Twelve percent also tracked a health indicator for a loved one.

Many mHealth app developers are collaborating with HealthKit, a platform created by Apple.

The application provides a repository for sixty different types of data such as respiration rate, cholesterol, blood glucose, body temperature, weight, body mass index (BMI), oxygen saturation, sleep analysis and nutrition.

And also, enables the sharing of data between family, friends, physicians and research databases.

Consumers will have sovereignty over their data and can authorise, one-time access or continual access. Graphical representation of their healthcare data using statistical analysis will also be provided.

An example, of an app that could be created by app developers, might be an app that connects body temperature sensor via Bluetooth to the iPhone and transfers the date, time and temperature into the Health app.

This is all part of the on going quantified self movement, in which consumers can track and monitor key health indicators. Hence, derive insights from them and take the necessary actions.

Listed below are some of the established and emergent enabling technologies that will shape the mHealth landscape in the coming years.

Key Technology to Impact Healthcare in 2019

Emergent Technologies in mHealth

Artificial Intelligence: there are numerous fixed and mobile devices installed in the homes and personal body networks. Of consumers and patients, that facilitate the collection and submission of huge amounts of heterogeneous health data to healthcare information systems for their analysis.

Machine learning and data mining techniques are extremely important in the processing and analysis of health data. Several mobile applications deploy these techniques to support the development of apps directed at medical diagnosis and treatments of many health disorders.

Existing techniques used for processing health data can be broadly classified into two categories: (a) non – Artificial Intelligence (AI) systems and (b) Artificial Intelligence systems. The drawbacks of non-Artificial Intelligence systems are due to the inaccuracy and lack of convergence. Conversely, AI techniques are hybrid in nature and include neural networks (ANNs) fuzzy theory and evolutionary algorithms.

An example of the use of AI technologies for predictive purposes. Is in the analysis of mobile clinical data to predict heart failure in patients. The approach involved the analysis of the clinical data of 242 heart failure patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza).

A predictive model was created using a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier deploying Bernoulli distribution. The outcome was that the predictive models were shown to significantly reduce false alerts.

Virtual Reality (VR)

This is an emergent technology that alters how individuals interact with computers. VR is a fully three-dimensional computer-generated “world” which is interactive. User immersion in a synthetic environment is a distinctive characteristic of virtual reality (VR) and distinguishes VR from interactive computer graphics or multimedia.

The hardware utilised for the delivery of the VR experience is a head-mounted display (HMD) helmet, fitted with wide-angle television screens placed in front of the eyes and stereophonic speakers placed over the ears.

The isolating helmet eliminates outside noise. Hence the user is only subjected to the sights and sounds generated by the computer. In medicine, the isolating helmet has been supplanted by 3-D glasses or helmets. Users view content by looking down rather than only at the screen in the helmet.

There are several alternatives to HMDs such as the Cave Automatic Virtual Environment, Developed at the Electronic Visualisation Laboratory of the University of Illinois, Chicago. The system uses stereoscopic video projectors to display images on their surrounding walls and the floor. Participants wear glasses with LCD shutters to view 3D images.

The DataGlove ™– a glove that works like a joystick and appears as a hand in the virtual world. Users traverse the world, by pointing in the direction of travel. They can also pick up and, manipulate objects by making grasping motions with the glove.

Telepresence surgery and image directed surgery involves the manipulation of an environment in real-time. However, whilst telepresence surgery enables the operation of equipment at remote sites. Image-directed surgery maintains a direct connection with the real world, rather than total immersion in an artificial data space.

Partial immersion, or composite reality or augmented reality. Is a hybrid of digital and real environment spaces. And is applied predominately to

Telemedicine enables the real-time or near real-time two-way transfer of medical information between places of greater or lesser medical capability and expertise. Medical professionals see and treat patients, at a distance through the deployment of telecommunications, high-resolution graphics, imaging, and video.

However, telemedicine does not use virtual environments. In contrast to Telepresence systems which use a full virtual environment for the user interface.

The Benefits of Virtual Environments in Healthcare

Quantified Self and Consumerism of Healthcare

The quantified-self movement has morphed into the algorithmic body. This is the process of gathering data on individuals to compile digital representations of them. These “data doubles” are increasingly becoming a requirement for participation in modern society.

This is demonstrated, for example by the role they have played in legal cases and insurance programmes.

““85% of your neurotransmitters are in your colon, not in your brain. There’s a reason we say, ‘follow your gut’.””

— Anon

Human beings are increasingly becoming reflections of themselves in digital mirrors. That is divided into increasingly granular parcels of data to render the individual observable, recordable and ultimately knowable.

The surveillance of individual health, using wearable devices is shaping how consumers perceive themselves, are represented to the wider public and constituted as modern subjects.

The use of software and algorithms that is automated and utilised to understand the body, record and subtly adjust individual behaviour to bring them in line with cultural and consumerist attitudes.

This is born out using data obtained from wearable applications in the realms of the law and insurance.

Legal experts, for example, have used data from healthcare analytics companies to go beyond the simple recording and reporting of information provided by wearable device companies.

To comparing the key performance data points such as distance walked, distance run, calories burned, heart rate variations. All observable metrics that are of interest. from one individual with the wider population (a global average) to determine for example how the Fitbit data of their client compares with the global average.

This was used to demonstrate that the physical activity of a client of the legal team, a former personal trainer. Was below the average compared to others due to an injury the client sustained

Big Data & the Consumerisation of Healthcare

Big data analytics is a combination of two branches of computer science; big data and analytics to deliver an approach to data management.

The establishment of big data has driven the need for the development of emergent technologies and tools. Capable of holding, storing and analysing huge amounts of structured and unstructured data such as biometric applications, medical and text reports, and medical and research images.

Analytics involves the processing and investigation of huge amounts of data, coming from various source sources of data in different data formats, to provide useful insights to drive decision within and between an organisation's ecosystems.

Primarily to provide solutions to important and undiscovered problems.

Healthcare Applications in Virtual Environments

The Internet of Things in Healthcare (IoT): Is an arrangement of multiple physical, electronic and various sensor-based devices connected to each other. Enabling the storage and exchange of data.

The internet of healthcare follows this same principle, through the consumerisation of various wearable devices, that enables the personalised monitoring of consumers or patients health.

Factors Driving the Adoption of Big Data Analytics and the IoT in Healthcare.

The Internet of Things in Healthcare (IoT): Is an arrangement of multiple physical, electronic and various sensor-based devices connected to each other. Enabling the storage and exchange of data.

The internet of healthcare follows this same principle, through the consumerisation of various wearable devices, that enables the personalised monitoring of consumers or patients health.