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What Is the Future of Healthcare?

New advances in medicine and concepts that were once the futuristic topics of science fiction are gradually becoming a reality. Gene therapies, 3-D printing of human organs, liquid biopsies, robot-assisted surgeries, and voice-enabled personal assistants are now realities that are becoming more sophisticated as time goes on [165–168].


Shift from Volume to Value
Healthcare as an industry is transitioning from a volume-to patient-centric, value-based care in its pursuit of evidence-based, personalized medicine.
There is a shift in medicine in which pharmacology is not the first and only line of treatment; and there is a greater emphasis on the role of lifestyle as medicine for preventative and therapeutic purposes.
What Is Volume-Based Care?
Traditional payment systems in healthcare are grounded in rewarding providers for the volume of patients referred and treated. Volume-based care focuses on economies of scale. Providers receive a discrete amount of money for all of the services provided to patients for a set duration of time. Value for money, patient experience, and healthcare quality are subsequently secondary considerations for evaluation. Providers are incentivized by patient volume and the cost of the overall care provided to patients. Healthcare resource and cost pressures, exasperated doctors, and incentivized clinicians have unknowingly facilitated a "see as many patients as possible" approach that has driven disease management from a medication-first, patient-second perspective. Hospitals and clinicians are incentivized to see as many patients as possible, conduct as many tests as possible, and approach disease from a medication-first perspective.
Volume-based care models are typically modeled on financial metrics such as optimizing profit or minimizing cost per patient rather than health outcomes.
Volume-based care payment systems often penalize providers financially for keeping people healthy, error reduction, or complication reduction, as they are not a driving KPI. For instance, if you were to be diagnosed with type 2 diabetes, your healthcare team would typically do the following: • Be given information on how to manage blood glucose levels per current guidance.
• Progress on a treatment regime.
• Be given information about dietary choices.
In the United Kingdom, for example, if a doctor's surgery or clinic were to enable the patient to realize type 2 diabetes remission, the surgery or clinic would fail to receive the money it would have for the drugs no longer prescribed to the patient. Personal biases can also complicate matters. In the United Kingdom, for example, doctors involved in assessing which drugs should be prescribed to NHS patients were found to have received payments of up to £100,000 per year from pharmaceutical companies [169][170][171].
Volume-based care is focused on a finite amount of money for a population. With populations growing and resources stretched, the evolution toward value-based care has been expedited. Healthcare is now in a transition toward patient-based care, which is synonymous with value, rather than volume.

Patient-Centered Care
Tseng et al. defined the value in healthcare to be the quality of care, typically measured through healthcare outcomes [172]. A value metric also for consideration is patient experience or patient-centeredness. Patient-centeredness is an important but not necessarily dominant quality measure for value-based care.
Patient-centered care utilizes a multifaceted approach, revolving around the patient, their goals, and the wider family in decisions and evaluations. There are several domains of patient-centered care, which include patient-centeredness.
Patient-centered care is a collaborative and multifaceted approach involving delivering health and social care focused on patient experience and patient outcomes. Patient-centered care focuses on empowering patients with the knowledge and experience, skills, and confidence to manage and optimize their health. Care is compassionate, personalized, coordinated around the patient, and respectful of the patient's worldview. These goals may appear common sense for healthcare practitioners. However, they have not always been performed as standard practice. Healthcare has typically been reductionist in its approach, addressing a sum rather than the whole and acting for patients rather than with patients.
Organizations like Buurtzorg Neighbourhood Care in the Netherlands deliver patientcentered care. The organization delivers care to 80,000 patients a year with 10,000 nurses, 21 coaches, and two managers [173]. The collaborative approach focuses on empowering patients through relationships with caregivers. Over time, patients see improvements in health status while the total external care time required is reduced. Patients and families are enabled with the skills and confidence to look after themselves. Digital therapies are able to provide patient-centered care by their nature, tailoring experiences based on a user's data-profile, behavior, time, location, or otherwise-and provide remote monitoring capabilities to healthcare professionals at the same time [174].

Value-Based Care
Although two distinct philosophies, patient-centered care and value-based care are becoming synonymous. Definitions of success incorporating patient-centered outcomes, perspectives, experiences, and preferences are positively influencing quality and delivery of care.
Value-based care does not mean cheap or economy care. Indeed, care is evaluated through the quality of patient experience and outcomes; and the model reshapes how care is received by patients. Value-based care is redefining the economics of wellness.
Wellness and prevention are emphasized in value-based care. Lifestyle, behavior, and environmental factors account for 90% of disease risk [175]. This has been evidenced by the growing prevalence of type 2 diabetes across the world. With growing pressure on healthcare resources, lifestyle medicine has silently refocused efforts on the role of lifestyle as medicine. Evidence demonstrates that activity, nutrition, sleep, and stress all impact physical and emotional health markers [176].
Lifestyle medicine's focus on lifestyle and behavior enables healthcare professionals to deal with reversible, non-communicable diseases such as type 2 diabetes and obesity. Rather than treating the patient in a reductionist approach, the patient is provided with holistic, compassionate care. The prescription of lifestyle medicine is already taking place in healthcare, with digital applications prescribed to treat disease. Type 2 diabetes patients, for example, may be prescribed the Low Carb Program app by their physician to support patients to self-manage their condition and achieve weight loss and type 2 diabetes remission where possible [177]. Lifestyle interventions are typically safe, easy to implement, and scalable. Growing levels of chronic disease can be mitigated with a simple approach delivered in an engaging and effective setting.
Redefining the Approach to Care: Quality, Not Quantity Instead of reacting to events, there is a preventative, lifestyle-first approach to health in value-based care. The focus is on patient wellness, healthcare quality, and efficiency. This is where patient data, digital health, and AI hold a fountain of promise. The emphasis on efficiency ensures that the approach is scalable and effective, enabling providers to reduce healthcare costs and improve patient clinical health outcomes.
Prevention of disease through quitting smoking, nutritional change, lifestyle change, activity, sleep, and identifying genetic risk factors reduces the burden on healthcare resources. Wellness is becoming incentivized. It has become the best interest of insurance companies and healthcare providers to monitor their patients' health more closely.
Start-ups and digital technology companies are developing digital health tools that are disrupting the traditional doctor-patient relationship and have enabled providers to become third actors involved in facilitating sustained health. The goal of value-based care is to standardize healthcare processes through best practices and to democratize access and quality of care. Mining of data and historical evidence can determine which methods work and which don't.

Digital Health Enables a Patient-Centric, Value-Based Paradigm
Keeping people well reduces healthcare delivery costs and optimizes the use of resources. For instance, when managing a chronic condition such as type 2 diabetes, value-based care uses a collaborative and multidisciplinary approach to managing the disease to prevent diabetes-related complications.
Patients engage with one healthcare team that is aware of patient progress and health status. The healthcare team may contain the patient's diabetes nurse, dietician, behavior health coach, and other professionals to support patient progress.
The team would set patient-centered goals and help a patient with the following: • Maintain blood glucose control.
• Introduce a digital community of people with type 2 diabetes for support.
• Provide health mentoring and habit maintenance support.
• Facilitate an activity program.
• Use the latest evidence base to provide nutritional guidance.
• Deal with the psychological aspects of type 2 diabetes.
Incentivization is transformed. Take a hospital setting, for instance, in which rather than being paid on how many patients could be seen, the hospital is being compensated based on the opposite. The hospital is paid based on how many patients are in positive health status and how many beds are available. The focus moves from the point of hospital admission to predicting the likelihood of future risks and anticipating them with preventative solutions.
Aside from face-to-face delivery, ongoing patient support can be provided digitally, for example, with health coaching, delivering an exercise program, or help with mental health concerns through apps, wearable technology, or telemedicine. AI and predictive analytics enable healthcare providers to optimize the delivery of healthcare, focusing on preventing and treating disease.
Value-based care can utilize low-cost digital technologies to enhance and democratize care. The simultaneous collection of behavioral, demographic, health, and engagement data provides an opportunity for machine learning and development of novel AI, to rapidly improve, and learn from, user behavior and outcomes.
Managing healthcare operations and subsequently evaluating healthcare quality are complex. To assess a value-based care model, healthcare organizations must collect and analyze data, objectively assessing performance through the quality of care, patient health outcomes, and efficiency of cost. Providers can report and model preventative care metrics such as hospital readmission rates, error rates, disease progression, population health improvement, and engagement strategies.
Healthcare quality is defined by several metrics. Patient experience and satisfaction scoring is typically the first metric of evaluation. Metrics such as time spent per patient, patient engagement, medication savings, and adherence are examples of quality that relates to productivity.
Many applications of AI in healthcare are within a hospital setting. Hospital management platforms are delivering a return on investment by enabling organizations to prepare. Systems that predict times of peak patient traffic, predict readmission times, and use real-time data to cope with the demand of real-world healthcare are seen as long-term systems of value to healthcare providers.
Applications involved in improving clinical care demonstrate enormous potential, and long-term studies are required to evaluate the impact on disease management and population health.
Data linkages between primary and secondary care can enable tracking of a network of data points including medication consumption, hospital admissions, and A&E admissions which can be used to develop incentivized reimbursement models. There are lots of reasons that innovations don't work within the NHS, but when the only reason innovations aren't being adopted is a lack of funding, not only is it a frustration, it's to the detriment of patient and population health. Funding often halts a great vision in its tracks preventing even the lightest of explorations. What's more, it is a massive frustration for healthcare teams who invest time, energy and passion into curating projects even before a business case is written. And for the innovators and NHS teams involved it can be just down-right demoralising-how can we possibly influence positive change?
As we engaged with NHS localities, stakeholders rejoiced in the idea of scaling improved type 2 diabetes management and remission with a scalable, engaging platform. The patient health benefits and savings in de-prescribing were evident-but still, it was cumbersome to find a budget. And even if there was a budget, it would be too small to truly impact health at a population level.
As innovators, it's not just services we are innovating-it's pathways of delivery. Global healthcare is in a transition toward patient-based care, synonymous with value, rather than volume.
With this in mind and our experience in other countries, earlier this year we implemented our first "gain-share" model with an NHS CCG. The gainshare approach is a paid-on-results model, which, rather than requiring immediate upfront budget, can share in the success of innovation delivery. In our case, in sharing the savings of de-medication through patients who are de-prescribed medication as they improve their blood glucose control.
It works well for all as it requires much smaller investment in the shortterm, with the outcomes celebrated and shared in the long-term. It makes sense-if everyone is in it for the "gains", then there is complete involvement and greater level of collaboration. It's one team-rather than two-working towards shared goals.
The model reshapes how care is received by patients. What's more, with lifestyle, behavior, and environmental factors accounting for 90% of disease risk, it enables a scalable approach to population health.
As NHS innovators, it is fantastic to actualise the benefits of digital health. In particular, democratisation of access to education and self-management for people to live healthier, happier lives.

Evidence-Based Medicine
As shown in Figure 9 1, evidence-based medicine solves a clinical problem by integrating best research and clinical evidence with real-world clinical expertise and patient values. In the case of healthcare, this is deemed to be randomized controlled trials and increasing real-world evidence. Datafication and the IoT are contributing significantly to the evidence base.
Evidence-based medicine is underpinned by decision-making that is based on the latest, most reliable scientific evidence. And as we develop new models of healthcare, unbiased evidence is critical to understand and trust services and innovations.
David Sackett, a pioneer of evidence-based medicine, defined the concept as "the conscientious explicit and judicious use of current best evidence in making decisions about the care of individual patients" [178].

Figure 9-1. Evidence-based medicine
Randomized controlled trials (RCTs) are deemed as the most robust and reliable form of evidence for assessing the efficacy of a treatment. Evidence demonstrates that many factors can influence the reliability of RCTs, including methodological quality, reporting quality, and source of funding. "Big" companies fund the majority of clinical research that is undertaken and face an enormous conflict of interest. Studies have demonstrated bias in favor of research funded by industry, which undermines confidence in medical knowledge. History has shown some organizations have chosen profits over people. Coca-Cola paid researchers to downplay the links between sugar and obesity [179].
Real-world evidence comes at a critical juncture for evidence-based medicine [27]. Real-world evidence is disrupting traditional evidence-based hierarchies and approaches. Datafication of human experience through mobile phones, social media, digital communities, health apps, nutrition tracking, wearables, and health IoT has empowered patients to become their own evidence base and influenced healthcare academia and understanding. Patients, for example, can compare the results of different hospitals, or even individual doctors, to best choose their treatment providers.
Quite simply, by the time an RCT has started and finished, a digital health app may have hundreds of thousands of members. Other channels of validating digital healthcare and innovation as clinically safe and effective are required [16].

Digital Health Research Poses a Question of Validity
Most RCTs conducted within pharmacology are internally valid, ensuring an exact population is involved in the testing of a particular drug. For example, a trial for high blood pressure medication may focus on people who have high blood pressure only and no other comorbidities. In the real world, the comorbidities of high blood cholesterol and triglycerides may be common in people with high blood pressure. This approach means there is often a gap between what is reported in RCTs and what is reported by patients. This is where digital technology can help bridge the gap between pharmacological intervention and real-world patient experience by providing data to connect the dots.
Digital communities can provide real-world evidence on medication side effects. Community app Diabetes Forum provides medication side effect data back to pharmaceutical organizations through a mobile application in the form of analysis of patient discussion and reported side effects. The world's second most commonly prescribed drug for diabetes, metformin, has a side effect of diarrhea reported to affect one in ten people taking the drug. Real-world evidence from members demonstrates this is 400% more common and reported by over 48% of people with type 2 diabetes [181].
Real-world evidence has historically been viewed as anecdotal by the medical community. However, the datafication of patient lives now means that data is directly received from devices, wearables, and sensors, ensuring that data cannot be misreported. Real-world evidence is growing at a pace that healthcare is struggling to keep up with. Digital health platforms and the aggregation of data also call into question previous paradigms of medicine.

How Does Evidence-Based Medicine Keep Up with the Real World?
It is a necessity for evidence-based medicine to keep up with the developments of real-world evidence. As David Sackett said, "Half of what you learn in medical school will be out of date in 5 years of your graduation; the trouble is nobody can tell you which half. The most important thing to learn is how to learn on your own." Patients now use the Internet, themselves, and their peers as the evidence base; and ubiquity of real-world evidence and AI in healthcare means that healthcare professionals can no longer be ignorant to change in the evidence base and biased toward old paradigms.
This data is a wealth of opportunity for AI and machine learning. Healthcare can adopt a data-driven approach, utilizing a variety of data sources to improve the patient experience and reduce costs. The exploration of this data over the ensuing decades will significantly progress healthcare and facilitate precision medicine.

Personalized Medicine
Personalized medicine, also known as stratified or precision medicine, is an approach that stratifies patients into groups and makes informed clinical decisions for the delivery of treatment and interventions based on the patients' anticipated response. As the pinnacle of evidence-based medicine, personalized medicine approaches to health are tailored to the patient. Patient health is managed on an individual level to achieve the most optimal state of health possible.
Around 10% of disease risk is based on genetics [182]. Each person has a unique version of the human genome. Groups of particular patients may share common genome characteristics and thus the risk of disease. Our genetic variations determine how our bodies would respond to a particular drug or determine risk of disease. For instance, research demonstrates people of South Asian descent have a higher prevalence of type 2 diabetes compared to Caucasians [183]. One drug might not fit everyone's requirement. Two people taking the same dose of the same drug might respond differently. Using personalized medicine, a right combination of a drug and its dose can be selected for everyone.
This approach to medicine is not new, and healthcare professionals have been using this approach since the time of Hippocrates.
However, the hyper-personalization achievable from the use of the patient genome and its falling cost-and medical data from health records, wearable technology, and the health IoT-have roused the public interest once again. It has never before been possible to predict the risk of disease, how the human body will respond to a particular medication, or print treatments out of materials other than ink. The combination of these technologies will fuel an era of personalized care and healthcare innovation.
Predictive tools can be used to evaluate health risks and develop personalized healthcare plans to mitigate patient health risks, prevent disease, manage disease, and treat a disease precisely if it occurs. As healthcare becomes increasingly personalized for patients in both treatment and service delivery, it is critical that access is widened to ensure participation from all groups of society.
Diagnostic tests, such as blood tests, are typically used to identify appropriate treatments based on a patient's physiological analysis. The selection of optimal therapies will be increasingly personalized to the patient genome. Healthcare providers will be able to diagnose current illnesses, predict future risks of diseases, and identify predicted responses to treatments and subtle traits within an instant. Genetic testing has started to make an impact in personalized medicine; DNA results are limited to being imported into services that can personalize treatment regimes, diet plans, or education to a patient. Currently, DNA test results take days to receive. We will soon be in an era of instant, inexpensive genetic testing that will empower patients to make informed choices about treatment, services, products, medications, and outcomes.

Personalization of Medicine Raises Ethical Issues
Raising serious ethical issues, the decision to have a genetic test and the implications of its results deserve careful preparation and thought. For providers, it provides an opportunity to fully engage in personalized, value-based care, with the quality of patient experience paramount. For patients, it can pinpoint genetic mutations that predispose a person to disease. The physical and mental consequences of such knowledge can be profound and have caused controversy among the medical establishment and general public.
A natural division among all people, regarding specific issues, is to be either for or against the topic of discussion. Progress is not without moral consequence, and legislation must endure the speed at which first-time problems are occurring. People are conflicted about having a lot of information about themselves.

Personalizing Medicine with Data
Wearables and the IoT hold the key to realizing personalized medicine, also known as smart medicine. Data captured by sensors in wearable contraptions are playing an increasingly powerful role in healthcare, facilitating the development of patient-centric healthcare systems.
Many factors are accelerating the acceptance of wearable healthcare solutions, particularly their use in clinical trials and academic studies to monitor patient health and lifestyle factors. Studies can record participants' health vitals using an Android Watch, Apple Watch, Garmin, Fitbit, or another smartwatch device. Participants use apps to record their lifestyle habits, nutrition, activity, and medication adherence and to monitor medication side effects among others. Wearables and patient data are beginning to be used by insurance companies to incentivize wellness.
Insurance companies have historically targeted products toward digital-savvy buyers, offering the latest gadgets to incentivize health improvements. Incentivized insurance products will become commonplace for wider populations, proving particularly effective in the combat against non-communicable diseases.
The delivery of connected, digital healthcare opens an opportunity to monitor, manage, and reverse disease, broadening the risk portfolio and creating a longer-living, reduced-morbidity population. The cost of ill-health is just too high for bill payers to ignore.

Rich, Sensor-Driven Data
As sensors become faster, smaller, and more capable, patient health profiles will eventually comprise detailed sleep analyses, details of continuous blood glucose monitoring, heart rate, blood pressure, and approximate calorie burn. A smartwatch will combine a variety of diagnostic tools, able to monitor blood pressure, heart rate variability, blood glucose, ketones, and more. Health sensors will become embeddable, biodegradable, and constantly connected, playing critical roles in such tasks as patient care. Behavior economics will play a larger insight into our everyday lives. Table 9-1 briefly describes some examples of connected medicine devices, explaining what device can be used where and why it can be useful. Some of the wearables and embedded sensors involved in connected medicine include the following:

• Stress bands
Stress tracking and calming of the mind are two key markets for wearables. Wearables currently monitor breathing and heart rate, detecting signs of tension to improve breathing and the ability to reach a calmer state of mind. Fitness trackers offer mindfulness functions, and there are headbands that deliver waveforms to the brain [185].

• UV sensor
Northwestern University has developed UV sensors that precisely monitor a person's UV light exposure. The wearable sensors, which are small enough to fit on a human fingernail, are waferthin. As UV is a known and potent carcinogen, this has the potential to reduce overexposure to UV and prevent complications such as heatstroke and melanoma [186].

• Smart tattoos
Smart tattoos place the sensor in the skin with significant strides made by researchers at MIT and Harvard [187]. Smart tattoo ink reacts with the biochemical composition of the interstitial fluid to indicate the status. Developments will allow for tattoos to degrade, lasting for as long as required and even only displaying in particular lights. For instance, high blood glucose could turn a blood glucose smart tattoo red, whereas low blood glucose may turn the tattoo blue.

• Smart medication
There are many directions to which medication is being made smart. Smart medication can tell a patient and their respective healthcare team as to the amount of medicine consumed, the time it was taken, and provide timely reminders if it looks like it may be missed. Today, Bluetooth-connected bottle caps and pill packets provide reminders to patients to encourage medication adherence. Bluetooth insulin pens and pen caps that log the time, amount, and kind of insulin injected typically push data from the device to the cloud, with users engaging in a digital app interface to query their data. Smart asthma inhalers, for instance, can identify an oncoming attack before the wearer recognizes the symptoms [188].

• Smart insulin
A form of smart medicine, smart insulin is next-generation insulin that responds automatically to changing blood glucose levels. The lower or higher blood sugar levels are, less or more insulin is released, respectively. The University of North Carolina researchers reported that their smart insulin patch, which rests on the outside of the body, will use a system of micro-needles to automatically detect high blood glucose levels and administer insulin through live beta cells appropriately. Because the beta cells are kept within the patch on the outside of the body, there is zero danger of them being rejected by the immune systems of people with type 1 diabetes [189].
Patients immediately turn to Dr. Google at the first sign or symptom of potential illness. It's become a running joke that online searches and symptom checkers eventually tell you, regardless of symptom, that you have cancer [190]. Patients are best placed to understand what is happening to their body, now visiting the doctor more prepared than ever. More informed, more aware, and more healthconscious patients now attend appointments, with their engagement used to personalize their healthcare plans. However, this also causes varying degrees of mental anguish and hypochondria.
Patients must not forget that qualified healthcare professionals provide a much more complete assessment and explanation of health, particularly if up to date with the evidence base. This is perhaps a contributing factor for why the medical profession has not always welcomed a patient-centric approach. A 2019 survey reported 87% of patients had a better relationship with their healthcare professionals as the result of using the platform, with 75% reporting a better understanding of their condition. When this survey was conducted in 2015, the same number reported a better understanding of their condition, but only 20% of patients reported improved relationships with their healthcare team [191].
Connected medicine provides a wealth of opportunity in solving novel health problems, particularly with disease management and elderly care.
In 2015, the United Nations approximated 1.2 in 10 people were over the age of 65. Projections estimate that this will jump to 22% by 2050 [192]. Healthcare expenditure on the elderly is a mounting concern, as it accounts for a higher share of healthcare expenditure compared to other age groups.
Non-communicable diseases such as type 2 diabetes and obesity are pandemics, with the WHO estimating 600 million people will develop type 2 diabetes by 2050 [193]. About one in four people suffers from two or more chronic conditions [194].
With rising pressure on governments, payers, and manufacturers to reduce healthcare costs, elderly and long-term care require solutions that are prepared for the impending rise in numbers. The Internet of Medical Things (medical IoT) demonstrates a tremendous potential to accelerate value-based care through providing remote monitoring and predictive and diagnostic capabilities.

Applications of Personalized Medicine
Driven by rich data, machine learning is transforming healthcare, enabling it to be personalized in a growing number of domains.

Disease and Condition Management
Heart monitors can detect heart rate variability and alert the healthcare professional in real time. The same concept is liberating for parents of children with type 1 diabetes using smart blood glucose monitors. Blood glucose measurements are sent directly to the cloud where parents can access their children's blood glucose in near real time and be alerted to potential hypoglycemic events. Digital apps can be delivered to reverse type 2 diabetes, manage epilepsy, and improve quality of life.
Novel utilization of technology can minimize the risk of health concerns and their subsequent healthcare costs. Elderly care can be delivered through digital apps. Risks such as falling, for instance, can be mitigated through fall detection and emergency assistance.
Accelerometers found in phones or watches can be used to detect falls or seizures, triggering notifications to carers of an emergency, its details, and location as they emerge. Future technology could also contain safety equipment. Wearable adult airbags can be used by those with mobility problems. Adult airbags deploy if they detect the wearer is falling to prevent injury [195]. Technology can help save the cost of avoidable elderly care and substantial healthcare-related costs.
However, the true value of the technology, especially connected assistants and wearables, in the provision of elderly care is realized in the predictive analytics that can be generated from the data collected. Deviations from daily routines, for example, could indicate cause for concern and emerging physical or mental health concerns and predict risk of illness. As sensor data and evidence expands, so too does the application of wearables in detecting signs of disease. Atrial fibrillation, for example, can be predicted from heart rate variability collected through an Apple Watch [196,197].

Virtual Assistants
Virtual assistants are not just entertaining; they can accompany those who live alone and provide support to the aging and elderly. Virtual assistants can empower people by answering questions, teaching new skills, setting verbal reminders to perform tasks such as reminding to take medication, and even controlling the home by performing tasks such as picking up the telephone or making a call. As homes become increasingly connected, virtual assistants will assist people in every aspect of life and interface with all aspects of the home.
Virtual assistants could call the emergency services on behalf of a patient, citing exact details and location. Connected cameras can track movement to detect falling, triggering the virtual assistant to reach out for help. Not confined to the realms of speech, virtual assistants in the form of robots can assist in particular situations within elderly care, performing roles such as helping people get out of bed, into the bath, or into a wheelchair. AI within such virtual assistants could even learn patterns, for example, when people may want to visit the bathroom.
Interaction through voice and touchscreen enables communication and will increasingly empower aging patients with greater autonomy and reduce the burden on elderly care. MiiCare, a company from the United Kingdom, provides remote monitoring through device-driven virtual assistants to look after aging populations, measuring patient temperature and monitoring movement, hydration, and medication adherence.
As the capabilities of virtual assistants improve, so too will their ability to determine the emotions of their master, whether it be happy or sad, excited or depressed. Virtual assistants will interpret the syntax, semantics, and tone of the conversation to check for signs of mental or emotional health concerns.
A similar opportunity exists for virtual assistants to provide useful assistance to people with disabilities. Apps can be used to control lights, music, heating, food orders, and lifestyle. Nominet, a UK Internet organization, developed an open source prototype called Pips to cater to people with sensory or cognitive impairments [198]. People are empowered with audio and visual prompts, nudges, and reminders to go through their daily routines and to develop the skills and confidence to navigate their environment. The prototype is built with a low-energy Bluetooth controller that sends and receives notifications between devices. The devices can be used to guide people through tasks such as washing their face, taking a bath, or taking their medication.

Remote Monitoring
Connected devices can provide support for caregivers and healthcare professionals to keep people out of a hospital and reduce their visits to the doctor. Apps and virtual assistants can provide medication reminders; fitness trackers will monitor user health and movement, alerting care providers if deviations from a routine were to occur.
Connected devices can alert family and care providers of events such as using a fridge, having a bath, or opening the front door. Aspects of routine demonstrated in the data would be learned and respective accounts notified if there were to be a deviation from the norm. Emergency buttons can alert the relevant services, ensuring that the elderly or those with long-term conditions can receive medical intervention when required most. The combination of connected devices, apps, and virtual assistants makes the home smarter and more conducive to mitigating risk, ensuring the healthcare provider/clinic is up to date with the latest overview of patient health while empowering the patient.
Gro Health, a lifestyle and behavior support app, co-designed a real-time remote monitoring dashboard with a team of a dozen physicians to provide real-time care to patients in British Columbia, Canada. The platform alerts clinicians should patients not adhere to treatment plans or disengage from the platform [199].

Medication Adherence
Forgetting to take medicine is easy enough, but the implications can be a problem. More than 50% of prescribed medications are not taken as directed [200]. Missed doses can result in slower recovery time, exasperate illness, or more serious consequences. A study by Benjamin et al. showed that getting patients to take their medications appropriately could prevent approximately 125,000 deaths per year in the United States alone [200].
There are many apps and digital services that remind patients when to take their medication, but medicine is going beyond even that. Smart pills with ingestible sensors seek to help doctors improve clinical health outcomes through notifying their respective app on confirmation that a medication has been consumed [201]. The notification is triggered when the sensor comes into contact with the stomach fluid. This enables doctors to measure treatment effectiveness and optimize the patient pathway.

Accessible Diagnostic Tests
People with chronic conditions and aging populations typically need more diagnostic tests than those who are healthy. Instead of turning up to a clinic or pharmacy to have urine and blood tests conducted, smart sensors in portable devices allow the test to be conducted in the comfort of the user's home. Results can then be wirelessly shared to their healthcare team.
Tests for cholesterol, cardiac function, HbA1c, fasting blood glucose, vitamin D levels, and insulin improve the convenience of performing repeated diagnostic tests, enabling quicker treatment, reduced risk of complications, and reduction in avoidable healthcare spending.

Smart Implantables
Implantable devices that are connected to a smartphone, healthcare provider, and wider networks will be used to monitor and treat disease in real time. The possibilities from this data are huge, as wearables are external implantables that have the potential to collect metrics on biological markers from organs and tissue.

Telemedicine
Telemedicine, which can be traced back to the 1900s, refers to the clinical application of electronic technologies to support long-distance patient care, patient and healthcare professional education, and health administration. The terms telehealth and telemedicine are synonymous. More recently, the term digital health has replaced telehealth as the reach of medicine has moved from the telephone-based devices to connected devices.
Consultations through Skype of video-conferencing facilities, EHR remote monitoring, digital health education, and transmission of scans or images among other applications are all considered part of telemedicine [202].
The world is truly mobile, and digital tools aim to deal primarily with minor primary care issues through electronic means. More serious issues would be escalated to the relevant healthcare professional, provider, or team. Digital health enables healthcare professionals to anticipate healthcare concerns rather than waiting for symptoms to present.

Digital Therapeutics
A very exciting, emerging field of digital health is digital therapeutics. Digital therapeutics is an enhanced form of telemedicine that brings together digital and genomic technologies with health, lifestyle, and human factors to deliver personalized medicine to patients enhancing the efficiency of healthcare delivery.

Which Health App to Trust?
There are more than 150,000 apps focused on health and wellness in the Apple App Store, which have been downloaded by over 50 million people [203]. Digital health tools allow patients to take a proactive approach to their health and wellness, looking to influence aspects of human behavior for the purposes of improving health.
The only problem is there are too many apps-of 327,000 health apps currently available to download, 65% haven't been updated for over 18 months, and only 15% of reviewed apps meet minimum standards. Only the quality of clinical evidence can differentiate the quality of vendors and services in the marketplace [204].
As more of our healthcare interventions are focused on chronic rather than acute diseases, behavioral therapy is surpassing pharmaceutical intervention as first-line therapy. Digital health and wider telemedicine have applications in a plethora of health conditions and are enabling healthcare and treatments to change rapidly.
Patients diagnosed with epilepsy can be prescribed an app to manage seizures, patients with asthma can be given a connected inhaler app, and patients scheduled for bariatric surgery can be prescribed a digital health app to assist in weight loss in preparation for surgery [205].
Measures of performance for digital health will be based on patient health outcomes, quality of care, and the number of users using tools and services over the long term.

Augmenting Traditional Healthcare
Less than one in ten patients with diabetes in the United Kingdom attends face-to-face structured education within 12 months of diagnosis [206,207]. Research demonstrates offline education benefits typically decline 1-3 months after interventions cease, suggesting that learned behaviors change over time and further support is required to ensure lifestyle changes can be maintained. Engagement with offline diabetes education courses is limited.
COVID-19 meant that in many areas, face-to-face education was virtually nonexistent.
Digital health provides a perfect opportunity for awareness, education, and health self-management. Digital health doesn't replace traditional healthcare; it augments it. Even with the endless digital health technology at our fingertips during COVID-19, it did not stop a surge in excess deaths not directly occurring because of coronavirus [208].
Digital education is engaging by its very nature; it is available at any time and on any device at the patient's convenience. Digital education can additionally be personalized to users through the data exchange between user and provider with the presentation, and analysis of real-time patient data subsequently encourages personalized, goal-focused practice and sustainable behavior change.

Patient Centered by Design
Education can be made patient centered and person led rather than the traditional teacher-learner environment. Through use of user data and third-party services, therapies can be made to be precise.
Digital therapeutics and self-management mobile apps provide a substantial opportunity to empower patients with the knowledge, skills, and resources required to manage and optimize their health [16].
Lifestyle changes such as stopping smoking, moving more, and losing weight can all be tackled through behavioral interventions, simultaneously targeting the major chronic diseases. Chronic diseases and their management are behavior mediated [91].
Digital health tools enable treatment to be personalized, democratized, and scaled. Real-world evidence has largely demonstrated the efficacy of digital tools. More evidence and randomized clinical trials are required to demonstrate causation. One area that is still unclear is who pays for digital therapeutics. If a doctor should prescribe a digital therapeutic, who should pay? The insurance company? The healthcare provider? The employer? The patient?
Regardless, digital innovation is revolutionizing disease management. Patients are increasingly demanding autonomy, wishing to be educated about their health, aware of the latest evidence base, and engaged with their health.

Digital Therapeutics: A Long-Term Case Study
At the time of joining, case study "B" is a 72-year-old male diagnosed with obesity and type 2 diabetes in 2015. B lives in the state of Hertfordshire, England, United Kingdom. He was diagnosed with type 2 diabetes at 67 years old. B is no longer clinically diagnosed with type 2 diabetes at the end of the study. B was referred to the Low Carb Program by his NHS physician.
B followed the information presented in the application and reduced the amount of carbohydrates in his diet. B engaged in the intervention 3-4 times a week averaging 20 minutes a week for the first 16 weeks of platform use. B used the web platform and respective iOS mobile application, completing 10 of the core 12 education modules. B posted seven times in the community. B used the app to keep track of his food diary. B's average intake of carbohydrates as measured through the app in the first week of joining was 253 g/carbs per day at baseline.
At 16 weeks, the average intake of carbohydrates was 102 g/carbs per day. At 15 months, B's average intake of carbohydrates was 109 g/carbs per day. Most frequented food items in B's food diary were cauliflower mash, omelettes, sardines, mackerel, and poached eggs.
At 15 months, B engaged an average of 9 minutes a week with the platform. At 15 months, B had reduced his HbA1c to below the threshold of type 2 diabetes diagnosis. B lost a total of 36.29 kg and reduced HbA1c by 76 mmol/mol. Average blood sugar levels had dropped by 2.2 mmol/L. B was confirmed to be in type 2 diabetes remission at 11 months.
B was interviewed as part of the case study report. B states: "In June 2018 I was told by the doctor that he was going to have to start moving me onto insulin injections as my metformin wasn't working. This frightened me and I said I'd be happy to do anything. I used to wake up during the night struggling with my breath, but the thought of injections was my main driver. I went back to my doctor in December and he was very surprised." B reflects that his diet now contains significantly less cereal and rice. "The biggest thing was realizing the overall sugar content of carbs, particularly cereal and rice, and how much they affect blood sugar. It seems a small thing to cut out. When you sit down and see what you're missing, I don't regard it as very much. I was surprised it had such a huge effect." B advises "keeping a record of weight and recording it at the same time each week helps you to see your progress, which is a motivation in itself." B confirms "meal planning and printing meal plans out for the week" help adherence to eating habits.

Incentivized Wellness
Ill-health is expensive. To employers and the economy, physical and mental health issues cost the United Kingdom more than £77 billion [209].
Employees lose an average of 30 working days each year due to illness or underperformance as the result of poor health. The global economic impact of chronic conditions such as cancer, type 2 diabetes, and mental illness could reach over $47 trillion by 2030 according to the World Economic Forum [210].
Individual lifestyle choices and physical and mental health all impact work performance. There is a significant challenge mounting for providers such as insurers and employers in improving absenteeism and presenteeism. To do this, providers are realizing the importance of holistic health-comprising physical health, mental health, and social and human interaction, both in and out of the workplace.
As healthcare interventions focus on chronic conditions rather than acute illnesses, behavioral therapy is typically becoming first-line therapy over pharmacological intervention.
Wellness programs, personalized by available data and tailored to the user, provide a convenient mechanism by which employers and other bill payers can support health and wellness. Previously, incentivized wellness meant encouraging individuals to visit the gym and take more steps-by taking the stairs or walking to work. Employers often promoted healthy behaviors associated with employee well-being and reduced healthcare costs. Today, wellness programs and digital tools go beyond signposting and screening. Immersive, engaging programs that are remotely accessed empower individuals to modify their behavior with the goal of achieving positive clinical outcomes and better health [91].
Behavior change in the form of therapy, behavior coaching, biomedical data feedback, and sensors enable wellness to be truly patient centered.

Where Else Can AI Be Used in Medicine?
AI is a collection of technologies rather than one, and many AI technologies have relevance within medicine. Personalized medicine provides fertile ground for AI's application and machine learning models to be developed. AI seeks to place the doctor, or more specifically the healthcare team, in our pocket. Among other things, AI has applications in disease prediction, cost reduction, efficiency improvements, automating manual tasks, appointments, and promoting us to alter our health. And AI can do some things better than humans [211].
As data volume and variety increases, the capabilities of AI models will become more precise, more probing, and more contentious.
AI will transform what it means to be a doctor. AI's integration into medicine is making better doctors and saving lives. From waiting times to prioritization or finding evidence to maintaining productivity or supporting decisions, AI will assist doctors and healthcare professionals in making informed decisions.
Some applications of AI technology are described in the following.

Mining the EHR
Mining of healthcare information systems holds tremendous potential for medicine. Systems hold considerable information about patients and their health, prescriptions, doctor's notes, and more. Healthcare data can be used to improve the quality of healthcare, reduce costs, mitigate mistakes, and improve and democratize healthcare quality. Knowledge discovery from the data contained in such systems is currently challenging due to variations in complexity, vocabulary, and standardization. Data mining provides an opportunity to extract relevant information from both textual and image-based archives. Mining massive data like EHRs enables the discovery of patterns from data that can be used to build predictive models.
As discussed in Chapters 3 and 4, unsupervised learning is known for feature extraction, whereas supervised learning is suited to predictive modeling. Mining of records is beneficial for both the patient and provider. Mining can be used to identify high-risk patients or those with chronic conditions for whom a personalized intervention could be delivered. Record mining enables providers to find best practices and treatment to reduce claims and hospital admissions. Through comparison of symptoms, treatments, and positive and adverse effects, healthcare providers can analyze the pathways that improve outcomes for patient cohorts, enabling clinical best practice and standards of care.

Conversational AI
Conversational AI refers to systems that can talk. Rather than a user interface based on text or code input, individuals engage with conversational AI systems with voice. Users are increasingly using chatbots to communicate with products, brands, and services. Voice-driven AI such as Amazon's Alexa has apps, or Skills, that can synthesize natural language to provide recipes or exercise tips, order products, find out the calories in a food, or call a cab. Conversational AI is catching on: one in five Americans owns a smart speaker, and there are over 100,000 Facebook messenger chatbots [212].
As AI chatbots develop, so too do their capabilities. Intelligent personal assistants will act as healthcare assistants. By virtue of being voice controlled, there are immediate applications for those less abled, where only the voice needs to be used to perform tasks or instructions.
Within healthcare, conversational AI enables simple questions that do not need the attention of a doctor to be answered. For instance, new parents could bombard a conversational AI with questions without fear of embarrassment or consuming a healthcare professional's time. Questions such as what temperature a baby should bathe in, how often a baby should sleep, or whether there are developmental milestones taking place can all be instantly answered by an AI speaker.
Many individuals turn to search engines to find the answers to their questions. However, most patients who query symptoms are unaware how to discern research quality and may come across conflicting evidence in addition to misleading information (or fake news) that can leave patients confused.
With the assistance of a medical AI chatbot, patients can receive immediate assistance. Continuing the preceding example, if a child's new parents had a medical question or were concerned by a symptom (say, a chesty cough), it would be burdensome for them to visit the doctor for a response to every question. However, there is a need for medical confirmation. This cannot currently be detected through algorithmic means; and hence, the evolution of conversational AI in healthcare will see them used as a digital health interface into healthcare teams and the world around them.
As conversational AI develops, cognitive systems will analyze conversation to detect early signs of mental, physical, or neurological illness. Voice-enabled devices such as Alexa will one day be able to identify symptoms of Asperger's syndrome, anxiety, psychosis, schizophrenia, and depression from conversational tones. This will assist doctors to predict better and monitor and track disease. Doctors may one day be able to predict the risk of depression in a patient's voice as they engage in a remote consultation.

Making Doctors Better
AI will enable healthcare professionals to stay up to date with the latest evidence base. PubMed, for instance, has over 30 million papers within its archives [213]. AI that was to mine such articles and present the most relevant evidence would facilitate more up-to-date healthcare professionals and enable professionals to practice medicine to the best of their knowledge and evidence base. AI can assist healthcare professionals in administrative tasks, performing roles of cognitive assistance.
AI can help mitigate mistakes, including those made by humans. Record mining, in particular, will enable doctors to improve over time. Digitalized data regarding patients, their treatment, healthcare professionals, and clinics can be mined to identify errors in treating certain conditions and avoid unnecessary hospital admissions. Zorgprisma Publiek, a Dutch organization, currently does precisely this: using IBM Watson to mine data to identify repetitive mistakes [214].
Suboptimal processes waste time and resources. Scheduling systems for clinics, doctors, and patients enable healthcare to be as efficient as possible. An AI system that was to schedule patient appointments and streamline communication would lighten doctor burden and focus time on more pressing matters. If a patient presented an urgent matter, for instance, an AI could evaluate and prioritize patient appointments based on risk.

Diagnosing Disease
Professor Geoffrey Hinton, a pioneer of neural networks, famously stated that it is "quite obvious that we should stop training radiologists" due to the sophistication of image perception algorithms, which could soon be more advanced than humans.
AI can currently be used to examine medical scans, identify symptoms of type 2 diabetes, identify retinopathy and cardiovascular disease risk, and spot signs of breast cancer. AI will perform better as algorithms are trained on more data.
Algorithms to detect disease are showing great promise, with several already in medical practice. However, that does not mean all AI diagnostics are ready for implementation. Many AI tools are developed without peer review and its academic rigor. Essential details require verification, such as the algorithm's code, training, and validation datasets; the data to which it is compared; how performance is evaluated; and how neural networks come to their conclusions.

Making and Rationalizing Decisions
Doctors make challenging decisions daily. It is vital that these decisions are as informed as possible. The use of AI, data mining, and predictive analytics is enabling clinicians to rationalize decisions and develop evidence-based treatment options. AI does not have to make the decision but can present the most reasonable opportunities to proceed.

Drug Discovery
Developing a new drug is an expensive business, and only one in three new drugs will make it to the market. Every new drug brought to the market costs pharma an average of $2.7 billion [215].
The consequence of failed drug trials can be damaging: from reduced share price to closure of worksites and reduced staffing. Hence, the pharmaceutical and life science industry is increasingly turning to AI to facilitate drug research and development.
Pharma isn't looking to AI to replace humans. Instead, it is looking to see why the rate of failure is so high through using innovative AI. AI systems are being developed that can identify new therapies from information on existing medications; this could improve efficiency, lead to drug development success, and accelerate the route to market for new drugs. In cases of pandemics, such as Ebola or swine flu, an accelerated route could save numerous lives.
There are approximately 10 60 compounds that have drug-like characteristics [216]. The chemist of the future will be empowered through leveraging data: historical data, experimental data, inferences, and trends. AI enables the earlier identification of promising drugs and biomarkers.
AI also has application in clinical trials. Clinical trial patient stratification allows the identification of patient groups most likely to benefit from the associated drug. This enables clinical trial operators to find the right patients for trials rather than patients who are unlikely to respond.

3-D Printing
Innovation in 3-D printing is beginning to disrupt traditional paradigms as the technology becomes more affordable and accessible. As the name suggests, 3-D printing involves printing three-dimensional objects from a digital model, using additive processes to build the object.
Also known as additive manufacturing, 3-D printing adds iterative layers, one on top of the other, to assemble the model. This allows precise modeling and reduces error. Although 3-D printing solutions are still in infancy compared to other technologies, the potential for its applications is exciting. Many AI experts consider it feasible that in the future, humans will adopt robotics and bioprinted material into their bodies. Soon humans may be able to print just about everything, from prosthetics and pills to bioengineered replacement body parts and organs. The technological advance of 3-D printing has life-changing implications for patients [99].

Personalized Prosthetics
The World Health Organization states that approximately 30 million people require prosthetic limbs, braces, or mobility tools and only 20% have them [217]. 3-D printing enables prosthetics to be custom built for each person. Through capturing a patient's measurements, prosthetics can be tailored to ensure comfortable, custom-fitted devices. Prosthetics will also become more comfortable as multi-material, 3-D printing assists with ensuring a better integration with the human body.
Casts could also be made more comfortable through the same, personalized approach. 3-D printing facilitates the swift and cost-efficient creation of personalized products. Innovation in 3-D printing is combining integrated sensors and machine learning algorithms to support more natural, fluid movements.
Predictive movement algorithms will evolve to mimic more natural movement, and humans will be able to use their brain and body to control them. 3-D printing technology enables prosthetics to be modeled and constructed in a little under 24 hours; importantly, printed prosthetics cost a fraction of conventional prosthetics.
Kidney, liver, and heart transplants could soon be a thing of the past. 3-D bioprinting marks the beginning of an era where transplant lists are a thing of the past. 3-D-printed organs can be built using the same techniques as 3-D printing, but instead use stem cells as the printing material. As well as printing cells, bioprinters typically output a gel to protect cells during printing.
In the future, once these organoids are printed, they will be able to grow inside the bodies of patients. Princeton University has developed a bionic ear from 3-D printing tools, which can hear frequencies beyond the human range. Printable and customizable implants from a patient's cells reduce the potential risk of rejection from the body.
One use case for bioprinting is the 3-D printing of human skin. Burn and acid victims, for example, have limited options for their disfigured skin. A team of Spanish researchers developed a 3-D bioprinter that was able to produce human skin cells. Within 30 minutes, a biological ink containing human plasma and other materials was able to print 100 square centimeters of human skin [218]. The opportunities bioprinting technology enables are truly life-changing.
There is no doubt a cosmetic market for bioprinting. It is feasible that in the future, face printers could be used by people to apply a model of someone else's face to themselves or even store a digital model of their face and regularly reprint their face for perpetual youth.
Alongside nanotechnology and genetic engineering, bioprinting may prove a tool in the pursuit of extending life spans.

Pharmacology and Devices
3-D printing disrupts traditional pharmacology and device manufacturer approaches. For instance, the efficacy of drugs and other treatments can be tested on replicated human cell tissue. Through this, 3-D printing enables the printing of medications that are personalized for the patient.
By reducing drug variability with this approach, there is potential for 3-D printed drugs to increase medication efficacy, reduce adverse effects, and improve adherence. The same concept applies to device consumables. Blood glucose test strips, for example, could be 3-D printed for use by diabetes patients at home. The possibilities are endless.

Education
Alder Hey Children's Hospital in Liverpool, England, uses 3-D printing for teaching and familiarizing stakeholders with heart surgeries that are to be performed [219].
Every cardiology patient is unique; and for those undergoing operations, 3-D models are printed to enable a variety of uses. Firstly, it enables surgeons to demonstrate surgeries that will be performed to often anxious and concerned parents using textures and colors that closely represent human tissues. Secondly, the 3-D models are used by staff and surgeons to familiarize themselves with the most important part of an operation-the child's heart. Finally, 3-D models are used to train students.
3-D printing also enables surgeons to prepare before surgery through evaluating a precise model of the patient's body or organs. This mitigates the likelihood of errors, improves surgeon accuracy, and reduces the amount of time the patient is required to be on the operating table. 3-D modeling is also useful in modeling bodily organs without the need for invasive procedures.

Gene Therapy
Just like organ transplant lists, genetic diseases could soon be a thing of the past. The use of nanotechnology, which refers to working with tiny particles to manipulate cells and alter DNA, enables the editing of gene expression at a cellular level. Gene editing has been successfully used to modify human immune cells to resist HIV infection [220]. With 1 in 25 children born with a genetic condition, gene editing has the potential to treat or eliminate disease [221]. It could be used to correct defective genes in embryos and personalize the child's immediate healthcare treatment plan. It is feasible that genetic conditions like cystic fibrosis, sickle cell anemia, and muscular dystrophy could be treated through editing the DNA in patient cells.
Gene editing is not without ethical implications; many people have moral and religious concerns about the use of human embryos for research or the editing of genes.
There is a concern that nanotechnology and gene editing will only be available to those who are wealthy, which will exasperate the disparity in healthcare and interventions. Nanotechnology will significantly improve treatment and recovery time, with the potential to prevent humans from developing disease at all.

Choose Your Reality
In the not too distant future, surgeons performing CT scans will be able to layer their scans over a patient's body via augmented reality (AR); medical students will use virtual reality (VR) to explore the inside of the heart, and burn victims will be virtually transported to a snow-covered mountaintop as a form of pain relief therapy. Digital health app Gro Health provides guided immersive mindfulness to users in 360-degree video, demonstrated to help people decenter [222].
Immersion into a completely digital environment, virtual reality, has mainly been used for games. Innovation in augmented and merged reality means that we can now entwine the virtual and physical worlds and manipulate both environments simultaneously.
Virtual, augmented, and mixed realities are increasingly being implemented in a wide range of medical applications. It is already evident that as it is incorporated further into healthcare, it has the potential to change the way many healthcare services are delivered.

Virtual Reality
Virtual reality (VR) is typically associated with gaming. In virtual reality, the user's reality is replaced by an immersive, entirely digital environment. This is currently achieved through a headset and handheld sensors, which enable interactions within the environment.

Augmented Reality
Augmented reality (AR) overlays a digital or 3-D environment in the form of objects, video, or data into the user's environment. The user's real-world experience remains the focus and information is added to the user's existing reality to enhance their experience. Information is distinctly digital and does not seek to emulate real-world objects.
AR does not necessarily require additional hardware and can be achieved through technology such as the mobile phone. AR's popularity was confirmed by the Pokemon GO app phenomenon. The game, downloaded more than 800 million times, overlaid 3-D characters within the user's real-world environment through the user's GPS location and inspired a wave of AR innovation [223].

Merged Reality
Merged reality seeks to emulate digital objects that can be interacted with. This requires additional technology such as a headset. Separate sensors track hand gestures and movement. In a mixed reality, the user can manipulate both the real-world and digital environments.

Use Cases of Immersive Reality in Healthcare
Virtual and augmented reality is largely used to augment traditional healthcare pathways and provide treatments and education.

Pain Management
Research demonstrates that virtual reality environments can be used to reduce the amount of pain a human feels vs. a control distraction condition [224]. The somatosensory cortex and insula, found in the brain, are linked to pain. Hence, through immersing patients in a virtual reality, VR will be used to enable patients to endure painful surgery.
Amputees often report pain in their amputated, missing limbs. VR environments can be used to immerse the patient within an environment to better cope with their phantom pains [225].
VR also works as a distraction. Children and adults alike will be given a VR headset from their doctor when receiving an injection to distract them from the impending prick. Hermes Pardini Laboratories and Vaccination Centres piloted the use of a VR headset that immerses the patient into a fictional, gamified environment [226]. The patient is distracted in a virtual world to the point that they are often unaware they have received their injection.

Physical Therapy
VR can track human movement, allowing patient movements to be monitored and analyzed. VR gyms have been opened in San Francisco and Ohio [227]. Rehabilitation will become gamified, for instance, through kicking a virtual ball or catching a ball.
Recovery exercises can be delivered and tracked in a VR environment and retold to the patients if they were not to get it right.

Cognitive Rehabilitation
The application of VR and AR in fears and phobias is apparent. Performed as a medical treatment, patients can be gradually exposed, known as graded-exposure therapy. Data from sensors will be assessed to ensure patient safety and develop best practice.
Cognitive function can be improved for patients struggling to perform everyday tasks. Through monitoring the performance of patient tasks in a VR environment, a doctor could determine declining memory loss and identify areas of concern or priority. Similarly, patients with injuries to the brain or those who struggle with tasks can have digital environments created to represent real-life scenarios.
Patients can practice tasks and regain or develop cognitive function. Patient engagement can be monitored and analyzed to observe areas of difficulty or reduced attention. There are few randomized controlled trials that have been conducted into VR in cognitive rehabilitation. However, some applications of VR are effective in treating cognitive deficits in people with neurological diagnoses [228].

Nursing and Delivery of Medicine
VR and AR will develop to become part of standard medical training. Both types of reality can be used to improve outcomes by enriching the information available to the individual. Alder Leys Children's Hospital in England uses a VR headset and 360-degree video to train student doctors how to deal with stressful situations. A case study from Alder Ley is available at the end of this book.
Critical decision points can be reviewed and analyzed with peers and staff. Virtual reality will be used more to learn medical specialties such as performing operations and anatomy.
Operations can be practiced with expert monitoring and giving feedback in real time. The Royal London Hospital conducted the world's first VR surgery, enabling viewers to watch in 3-D 360-degree video [229].
The learning experience is unparalleled and has the potential to disrupt medical training, particularly for countries that have limited healthcare resources.
Doctors will use augmented and mixed reality to enrich their environment. Surgeons will have access to critical information in real time through mixed realities, delivered to their vision through glasses.
Nurses could use augmented or mixed reality to identify veins in the arm for a blood test. The Interventional Cardiology Center at Tufts Medical Center in Boston uses VR to introduce the facility to prospective patients with anxiety before a procedure [230].

Virtual Appointments and Classrooms
As the cost for virtual and merged reality devices becomes more affordable, virtual appointments will become commonplace. Virtual appointments remove the inconvenience of attending a clinic, save time and environmental resources, and focus healthcare professional time to where required.
Virtual appointments will become as familiar as webinars, enabling stakeholders to be present without traveling. As well as one-to-one appointments, virtual and merged realities provide an immersive and engaging experience suited for learning.
Virtual reality will become a staple tool for education and training-for both healthcare professionals and patients alike.

Using the Blockchain in Healthcare
Transitioning of traditional patient health records to the EHR is considered to be enormous progress for healthcare. The digitalization of patient records mitigates some of the traditional risks of centralized data stores. This model, however, still places the medical records in the hands of the provider. Blockchain technology, popularized by the bitcoin cryptocurrency, has the potential to revolutionize data access, privacy, and trust. Currently, blockchain is yet to be deployed for mainstream healthcare.

What Is the Blockchain?
Blockchain, fundamentally a collection of data records, is a piece of software formed by the combination of several preexisting technologies that provide blockchain with its characteristic features: an immutable, distributed public ledger whose authenticity can be verified by anyone. Those who validate the data on the ledger are rewarded with value, which helps create trust in a trustless environment; distributed peer-to-peer control, which provides a high level of security; and the ledger can be programmed to trigger automatic transactions in the form of smart contracts, allowing for a widespread application of this technology.
PayPal, Visa, and Mastercard, for instance, act as central authorities for financial transactions: trusted institutions that act as intermediaries. The land registry acts as the trusted store for details on homeownership. These centralized databases are subject to being hacked or manipulated.
Blockchain technology aims to solve data management, privacy, and security issues, improving interoperability and easing the flow of data between doctors, hospitals, healthcare systems, and insurance providers through the use of a decentralized, immutable database.
Patients are demanding increased access to their medical health records. Data from EHRs, IoT, wearables, and devices can be used within the ledger, in a trusted, secure, transparent, and interoperable environment.

Tamper-Proof Security
The blockchain is a database which cannot be edited, stored, and maintained by all those using it. Each new transaction or piece of data is encrypted and then approved by a particular proof-of protocol (consensus, work, stake) by other nodes on the networks that authenticate the transaction or verify the stored piece of data.
Each node on the network has an identical copy of the blockchain; and thus, the transaction is permanently recorded and linked to previous records. The links, known as hashes, are traceable back to the very first block in the blockchain. Therefore, any attempts to tamper with a block in the present would require the transaction and all related blocks to be also altered, on all records, distributed among the nodes holding a copy of the ledger simultaneously. The longest chain of events is considered valid.
Blockchain implements crypto-economic and game theory techniques. Any attempt to create a rival chain would need to be created faster than the current version of the truth to be accepted. This would require tremendous computing, energy, and resource commitments. Miners compete to validate blocks. This good behavior is expensive regarding electricity and computing power, and so miners are incentivized to validate blocks, rewarded with bitcoin (BTC; in the BTC scenario).
Through creating a common, distributed, immutable database of healthcare information, doctors and healthcare providers have the potential to access medical data from any system, with improved security and privacy, less administration, and better sharing of results.
• Single points of failure are eliminated by decentralizing and encrypting the data.
• The blockchain is democratized. Anyone can contribute or store a version of the truth.
• Through ensuring consensus on events, the most likely version of the truth is held.
• A transparent and auditable ledger of events is provided through time-stamping.
• Game theory, crypto-economics, and hashing incentivize good behavior and ensure the events are without censorship.

Use cases
Blockchain technology has many applications in healthcare, with most technology currently in pilot or proof of concept stage in the following use cases.

Verifying the Supply Chain
Blockchain can verify the supply chain. As the blockchain is created in a chronological and auditable manner, it can act as a means of verification for components of every link in the supply chain. The journey of materials or treatments, for instance, can be logged onto a blockchain ledger, which could be used to identify fraudulent activity, anomalies, error, or a break in the chain through data entry or IoT devices. The cold delivery chain of delivering insulin from manufacturer to pharmacy could be confirmed to be untampered and appropriately cooled. This is currently being used in developing countries to combat counterfeit medication. It is also being developed to enhance genomic data protection, addressing the privacy challenges of big genomic data.

Incentivized Wellness
Blockchain technology could be used to incentivize wellness: through the use of a cryptocurrency as a digital token of value. Engaging people with health services or a healthier lifestyle could save the global economy a tremendous amount of money in healthcare costs.
Health providers or employers typically see the cost benefit of this in the form of savings or profit, and this is rarely passed to the individual. By utilizing blockchain technology, tokens could be created and distributed to patients through the blockchain to share the value of the savings and be treated as a tradable currency.
Individuals could earn tokens through behaviors such as going to the gym, reaching their step goal, attending education sessions, engaging in mindfulness, completing a particular sports event, or adhering to medication or digital therapeutics. The ecosystem rewards positive behaviors with an asset or token of value. The value of the token could be fixed.
Extending this concept, positive health behaviors could be extended to the point where patients have health token savings accounts that could be used to transact in hospitals.

Patient Record Access
Patients are demanding access to their health record. This poses a significant challenge as to how to best share sensitive medical data with unknown third parties. For third parties, there is also a challenge to verify the integrity of the data while ensuring privacy for the patient.
Presenting patient records on an appropriately permitted blockchain would give cryptographic assurance on data quality without any need for human involvement. Providers or consumers uploading health data would generate transactions. Users provide a signature and timestamp, alongside a private key to access data. By using digital signatures, all records stored on the blockchain can be identified and used to create a comprehensive patient health record.
The use of digital signatures and cryptographic encryption ensures data travel securely and are accessible only by those with the relevant public keys. By using blockchain technology, each addition to the EHR can be logged, with an immutable, auditable trail of transactions, while ensuring the most current version of the record is used. Patients would be able to verify all attempts to access or process data.
Blockchain's decentralized structure enables any approved stakeholder to join the ecosystem, without the need for data integration or manipulation concerns.

Robots
Like other fields of AI, the use of robotics is changing healthcare. Robotic technology is not yet widely affordable or implemented, and it is unlikely robots will ever entirely replace humans in the medical setting. Hospitals and healthcare systems are unable to meet the cost of the technology until proven; and robotic assistants have not replaced, and some would say cannot replace, human contact.
Over and above its use in the supply chain, robot technologies have potential in the following areas.

Robot-Assisted Surgery
Robot-assisted surgery enables enhanced vision, improved precision, and dexterity. Currently limited in reach, robot-assisted surgery will become commonplace as barriers to entry such as the cost of hardware and training are reduced. In the future, healthcare professionals may have to learn how to use an instrument as well as how to perform the surgery. Robot-assisted surgery blurs the lines of liability, which could prove to be a barrier to adoption.

Exoskeletons
Robotic exoskeleton technology focuses on enabling patients to perform tasks through the use of an external, integrated device. Ekso Bionics is an organization that provides a wearable exoskeleton that assists spinal cord injury patients to stand and learn to walk [231]. In the future, exoskeletons will become a standard form of rehabilitation and aid human mobility in more activities and in more environments.

Inpatient Care
Inpatient care can be enhanced through the use of robots for streamlining tasks. Robots could be used to automate tasks such as collecting mail or delivering blood. Delivery robots will deliver medications or important material autonomously.
Robots are already being used to disinfect rooms, where the risk to humans is minimized through using a robotic agent [232]. Soon ingestible agents and smart pills will be able to monitor a patient's internal reaction to treatment; robotic nurses will become commonplace for automatable tasks, such as taking blood. A robot will take your vitals and draw blood by identifying the correct vein with greater accuracy than a human nurse. Robots will be used to support care for the elderly and those with long-term conditions.

Companions
Virtual assistants are becoming more humanlike with developments in natural language processing. As discussed, humans are already forming bonds with robots. Robots can be used to provide companionship or treat loneliness in cases of mental health, elderly, and long-term care.
As robots evolve in their capabilities, they will be able to perform increasing duties such as bathing patients, transporting patients, and so on. Robots will be able to monitor vital signs through IoT and wearables and aid the patient in real time.

Drones
Drones have the potential to alter the way medicine is delivered. Drones can be used in hard-to-reach areas, places of conflict, and remote populations to deliver medication, vaccinations, and diagnostics. Drones will be used to deliver medications from the pharmacy, just as they are being used in shopping. Time-sensitive items such as blood, bodily fluids, and organs can travel shorter distances to go directly to where they need to be, within campuses or across larger distances. Drones were used in the transfer of humanitarian aid during the COVID-19 crisis in Wuhan [233].
Drones can also be used to locate and identify, particularly in remote locations. One use case for drones is as a flying medication toolbox in cases of emergency. Critical care in the minutes after a stroke, trauma, or heart failure is essential to accelerate recovery and prevent death. An ambulance drone prototype, developed by TU Delft, combined a heart defibrillator, medication, and two-way radio that could be dispatched to a patient to speed up the response before the first responder [234].
Current restrictions in the use of drones are reducing their potential. It may be some time before flight restrictions, legislation, and functional issues such as drone battery life and drone load are resolved.

Smart Places
Intelligent homes, hospitals, places, and things promise to change the way we live. Ubiquitous connectivity and an expanding sensor base provide a wealth of opportunity to people, patients, and providers alike. Improvements in patient lifestyle and healthcare have contributed to growing numbers of centenarians over the past 30 years. In the United Kingdom alone, the number of people living to 100 increased by 65% [235].
These breakthroughs have come at a cost to healthcare; and smart places, and smart things, enable scalable opportunities to improve health and social care-reducing costs per capita while personalizing the experience to the patient. Rather than going through the process of connecting and integrating apps, sensors, or devices, connected places will use automated sensors that do not require the user's constant attention, collecting tremendous amounts of real-time data.
Facial recognition, voice recognition, responsive notifications, and data-based suggestions will become the norm. Aggregation and analytics platforms will streamline and simplify decision-making processes in a rapidly changing environment. These devices can notify patients and healthcare professionals in real time: forget your Big Brother, this is Big Doctor.

Smart Homes
There will be many ways that homes will become smart in the pursuit of improved patient and population health, which are best illustrated by example. On waking, a wearable sleep monitor will assess the quality of your sleep while you perform mindfulness through your connected watch. Your sleep monitor will even tell you when to go to bed for optimum recovery. After brushing your teeth with a toothbrush that assesses whether you are hydrated or not, you drink your morning coffee, which controls the dosage of insulin released for absorption in your bloodstream. Your fridge will inform you of out-of-date food and ensure none of your potential allergens are found in the food you purchase.
Smartphone apps will augment the calories and nutritional values of foods; and 3-D printers will enable the printing of safe-to-dispense, at-home pharmaceuticals. Virtual assistants and even mirrors will assess for signs of anxiety and depression and assess health biomarkers in natural language. Should you develop a cough, your virtual assistant will tell you that you're coughing more than usual; and with the change in body temperature detected from your smartwatch, your assistant calls the doctor on your behalf and arranges an appointment.
As a closed environment, the smart home allows for AI to accommodate for the highly individualized needs of the user. The most important element of smart anything is the nuance when scaling: to understand the context and prioritize local activity.
The future is not as far as it seems. Bolzano in Italy is already working with IBM and various partners to empower aging people to age safely at home [236]. Safety and security are the main priorities, with the project using sensors installed into homes to monitor environmental factors such as temperature, carbon monoxide, and water leaks. Data is pushed to an off-site control room where, depending on the individual need, family members, volunteers, emergency response staff, and social services are notified.

Smart Hospitals
A smart hospital is one that is connected, much like the smart home. The objective of a smart hospital is to provide clinical excellence, an efficient supply chain, and superb patient experience-facilitated with technology. Smart hospitals will use a continuous learning ecosystem, ranging across many areas including electronic data collection and health records, digital technology, robotics, 3-D printing, unstructured data, and robust analytics. Somewhat ironically, mass use of technology and AI will enable patients to become more actively involved in their treatment decisions. Nonemergency consultations will take place on the Internet, with AI prioritization enabling the right doctor, with the right skills and training, to treat the patient. Treatment will be blended between offline, physical care and digital care-where adherence and accountability can be quantified and maintained. Growing data and large-scale analytics will continue to personalize digital treatments for patients.
On attending a hospital, an automated and streamlined admission reduces patient waiting time. On admission, patients will be tagged with a clinical-grade wearable to track vital signs through the inpatient stay. Metrics are all sent wirelessly to a dashboard visible to your medical team. Any anomalies or causes for concern are detected and prioritized.
Hospitals and surgeries will become hubs of data, with hospitals working with businesses to extract value from the data-regarding improving efficiency, minimizing mistakes, and improving treatment and device decisions. Healthcare will be delivered as a service, with patients incentivized with cryptocurrency to maintain sensible and healthy lifestyle choices. All relevant data will be anonymized and accessible to digital health partners and internal departments to enable continuous learning from each patient's experience.

Developing Whole AI
Reasoning is reductionist. The basic premise of reductionism is that by breaking down or simplifying complex biological or medical phenomena into their many parts, one is much more likely to understand a single cause and devise a cure.
For instance, a mouse can be divided into a skeleton, a circulatory system, a nervous system, a digestive system, and so on. It fits a model-a pattern, theory, equation, formula, or some form of common structure. AI systems also follow the same, reductionist approach, reducing the problem to a defined discipline. For AI to truly develop artificial understanding, the key will be to address humans from a perspective of holism.
As the world continues to immerse itself in digital technology, healthcare AI models are becoming quicker and more accurate, with patient-based care and lifestyle medicine placing the focus back on holistic health. The effect of lifestyle factors, sleep, movement, nutrition, environment, and genetics on health is well known. We also know that the world and people, mind and language, can be unpredictable.
As AI develops, problems of irreducible complexity will require solving where the reductionist approach cannot work. The focus is on value, on the patient, treating patients as people and focusing on the patient's wider health, relationships, and goals.

Conclusion
Artificial intelligence has come a long way since its definition as a field in 1956. Machines have come so far they can beat humans in games that are hundreds of years old and tell us about signals that are unidentifiable by human computation.
As AI grows, so too does the requirement to not lose the human touch. Collaborative academic research, clinical evidence, and policymaking must shape the domain of AI and healthcare to ensure humanity's best interests are protected from those that develop intelligent software.
Whether you are a pessimist about the impact of AI on society or look forward to it with baited breath, one thing is for certain-with more data now than ever before, the most promising era for healthcare is yet to come.