Today’s health sector is digital. This is a fact. From x-rays and magnetic resonance imaging (MRI) to computed tomography (CT) and ultrasound scans and much more, there is no question about it. In fact, virtually, all medical-imaging content today is high-tech and fully digitalized (x-ray film, e.g., is used less and less frequently). In 2012, research firm Frost and Sullivan predicted that, by 2016, medical imaging data in the USA alone will cross the one exabyte mark—one million terabytes (Frost and Sullivan 2012).

Biological analysis laboratories, equipped with robots, are generating increasing amounts of this digital information. For example, “the European Bioinformatics Institute in the United Kingdom, which is part of the European Molecular Biology Laboratory and one of the world’s largest biology-data repositories, currently stores 20 petabytes (20,000 terabytes) of data and backups about genes, proteins and small molecules. Genomic data account for 2 petabytes of that, a number that more than doubles every year” (Marx 2013).

Hospitals and other institutions account for another large piece of the overall digital picture, using information technology to manage patients’ administrative and medical files.

A report from EMC and the research firm IDC (Dec. 2014) offers a few imaginative ways at visualizing the health information proliferation, anticipating an overall increase in health data of 48 percent annually. The report pegs the volume of health care data at 153 exabytes in 2013. At the projected growth rate, that figure will swell to 2,314 exabytes by 2020. To paint a picture, the authors of the report suggest storing all of that patient data on a stack of tablet computers. By the 2013 tally, that stack would reach nearly 5,500 miles high. Seven years later, that tower would grow to more than 82,000 miles high, bringing you more than a third of the way to the moon! (Health Data Archiver 2015)

However, a question remains: “Is it digitally efficient?” That is the question we will now try to answer. But first, we need to set the scene, and try to understand the way all the different components fit together; like a cubist painting, everyone will see what they want to see, even if it is not the artist’s vision.

1 What Is Digital Health?

A number of concepts are used today to define the term “digital health,” or at least some aspects of it. Our intention here is not to provide an exhaustive list of definitions but, rather, to provide the most common ones, and determine if they have limits and what they are.

1.1 Historical Components of the “Digital Cube”

To make sure we have a common understanding, in this chapter we define the term “digital health” as, “the use of information technology/electronic communication tools, services and processes to deliver health-care services or to facilitate better health.”Footnote 1 When viewed in that light, it is clear the health-care sector has been using such components for a long time.

To understand what currently exists, we can follow a hypothetical patient, John Doe, in his care journey and see how he interacts, directly or indirectly, with various digital systems.

John is a 50-year-old male who works in an office and enjoys skiing occasionally. One day, he has an accident on the slopes and is rushed to the nearest hospital.

In the ambulance, John is connected to various devices that record his vital signs onto a portable computer. This recording, which is partly automated to avoid major errors, is known as an emergency medical record. In certain cases, this system can be connected to the hospital and information exchanged with the hospital’s back-end system.

Once he reaches the emergency room, John is registered and triaged and, because his condition is not immediately life threatening, he is put on hold to wait for the next available doctor.

This registration process is carried out using through the facility’s hospital information system. These are among the oldest computerized systems used by hospitals and clinics. They are the hospital equivalent of what other sectors would call enterprise resource planning or business process–management software, which is used mainly to manage accounting, human resources, and other non-medical functions.

John is diagnosed with a broken leg and is sent to the imaging department so that his doctor can gain a better understanding of the problem. The radiologist takes the requested pictures and writes an image report. The images and the report are linked together and recorded.

These images may be recorded in the hospital’s radiology information system, which itself may be associated with specific modalities (e.g., x-rays, CTs, MRIs). In addition to recording the images, the radiology information system may link them to metadata (image descriptions), as well as to the radiologist’s report.

Depending on the provider and the homogeneity of the modalities within the hospital, several radiology information systems may be used within the same institution. In some cases, the hospital may have a picture archiving and communication (PAC) system, which archives all of a patient’s imaging records in the same digital location.

PAC systems are designed to be “modality results consolidators,” providing a single unified view by patient. For example, to make a single diagnosis, a physician may order x-rays, an ultrasound, and a CT scan. As a result, the doctor expects to have all of the images available as a “single view,” that is, without having to access diverse systems.

Because the reason for John’s fall is not clear, further tests are requested by the doctor, including some blood analysis. The emergency department nurse takes a blood sample from John and sends it and the doctor’s requisition to the hospital laboratory. The laboratory analyzes the samples and records the results in their system.

Like the radiology information system, a laboratory information system captures the data generated by the biological analysis equipment. The system then consolidates the information into a single report where the laboratory biologist enters his or her interpretation of the results.

After reviewing the laboratory reports, the doctor determines that John collapsed due to hypoglycemia brought on by exertion, and diagnoses him with diabetes. The doctor records all of this information to build a medical history.

The digital tool used for this purpose is called an electronic medical record, which is the medical equivalent of an enterprise resource planning information system. This type of record enables a doctor to manage a patient’s medical file electronically. In each medical case, the doctor will be able to create “events”: stepping stones in the patient’s treatment.

An electronic medical record may include links to medical images and laboratory results. It can also enable the generation of drug prescriptions (e-prescriptions) and “e-referrals” to specialists. Usually, an electronic medical record communicates with the institution’s hospital information system so that invoices for any recorded medical procedure or treatment can be generated for the patient or public/private insurer.

As John leaves the emergency department after being treated, his doctor gives him a prescription. John brings the prescription to the nearest pharmacy, which the pharmacist reviews. The pharmacist then checks for possible drug interactions before giving John the medication.

A pharmacy information system enables pharmacists to better manage the dispensing of medication. For example, such a system can check for drug interactions and adverse events and manage the direct billing of insurers. It can also be linked with automated dispensing and packaging equipment and may include specific tools such as personalized drug management (e.g., for chemotherapy drugs).

John has also been told to make an appointment with his family doctor (general practitioner) so that a plan for his long-term care can be put in place.

During the appointment, John’s doctor uses a practice management system to record all of John’s medical events to keep track of John’s care.

Going through this story, you may have noticed something: few—if any—of these systems are connected (some hospitals have a degree of integration that enabled through products produced by companies like SAP, Epic, and Cerner).

Some countries have developed a very advanced concept known as the electronic health record. However, there is much disagreement about the definition of “electronic health record” versus “electronic medical record,” with some literature not making any distinction between the two.

To avoid confusion, in this chapter we are using the definition of electronic medical record as described earlier, that is, a digital record of a patient’s medical treatment.

On the other hand, an electronic health record is a consolidated patient dossier containing the data generated by all of the information systems (e.g., emergency medical records, general practice systems, radiology systems, and information systems used by laboratories and pharmacies) involved in the treatment of a patient—even for diverse pathologies.

Countries that adopt this model could, without special effort, ensure that all of the actors involved in a patient’s care have access to all of the information they need—according to access rights decided by the patient. This enables those caring for the patient to implement a “full care” plan.

Even if the concept of electronic health records is not well known, it will be soon, as this digital tool is about to be widely implemented.

1.2 The Need to Think Outside the (Hospital) Cube

Over the next few years, the health-care sector will be facing a number of challenges, including the following:

  1. 1.

    A decrease in the number of physicians:

    With a recent report from the Association of American Medical Colleges estimating the US could lose as many as 100,000 doctors by 2025, primary care physicians are already in short supply, particularly in rural areas, according to a MarketWatch report. Some 65 million Americans live in what’s “essentially a primary care desert,” said Phil Miller, of the physician search firm Merritt Hawkins. In fact, in about one-third of states, only half or less of patients’ primary care needs are being met. (Finnegan 2016)

  2. 2.

    An increase in the number of patients, mainly due to increases in life expectancy:

    Globally, life expectancy has been improving at a rate of more than 3 years per decade since 1950, with the exception of the 1990s. During that period, progress on life expectancy stalled in Africa because of the rising HIV epidemic; and in Europe because of increased mortality in many ex-Soviet countries following the collapse of the Soviet Union. Life expectancy increases accelerated in most regions from 2000 onwards, and overall there was a global increase of 5.0 years in life expectancy between 2000 and 2015, with an even larger increase of 9.4 years observed in the WHO African Region. (WHO 2016)

  3. 3.

    An increase in health costs associated with the need to decrease the health care budgets: “PwC’s Health Research Institute projects the 2017 medical cost trend to be the same as the current year—a 6.5% growth rate” (PwC 2016).Footnote 2

One proposed solution to these challenges is to provide health services remotely, supported by ICT. However, as requirements and technologies have changed over time, the original concept—of connecting two points through one secure electronic channel—has been replaced with the idea of providing and receiving multi-channel data from and for multiple profiles (medical and non-medical).

This multi-channel view will require more than one digital solution. The following list is not intended to be exhaustive; rather, it is meant to take into account the most widely used and widely known concepts.

If we want to talk about the precursor to “remote health care,” we need to talk about telemedicine, which is a method of providing medical services remotely using ICT solutions. Generally, this kind of approach assumes there is a medical doctor at one end who is able to provide a tele-consultation. At the other end is someone who has a minimum medical background, although not necessarily.

This system is usually structured as a point-to-point communication using a private network (for security) and a proprietary protocol (to increase the quality of sound and images). However, it is not necessary for the remote health-care practitioner to have visual contact, or even to formally speak to the patient. (For example, vital signs can be obtained without seeing or listening to the patient.)

Therefore, due to the limitation of the system, a number of Web-based digital services have rapidly developed. These are known as eHealth (electronic health) services. In this category, we find services like the following:

  • e-prescriptions, where physicians prescribe drugs using an electronic system that is also available to pharmacists;

  • e-referrals, where physicians can use an electronic system to refer a patient to a specialist (in this case, the specialist could be initially identified and referred through a private channel); and

  • e-discharges, where the hospital uses an electronic channel to send the discharge summary to the patients, family doctors, or other actors.

In all of these cases, there is no specific solution or product associated with the service; rather, it is a way to operate differently using technology that can be located either inside an organization (intranet) or outside it (Internet or extranet).

As a result of further technological evolution and innovation, a new set of mHealth (mobile health) solutions has been created that use mobile devices (e.g., smart phones, tablets) and software (applications or “apps”) to provide secure access to health-care data. Such technology is not limited to end users located in different geographic regions; it can also be used by medical staff navigating between services or wards within a single hospital (or other institution), providing them with mobile solutions that enable them to have continuous access to authorized data without having to change devices or workstations.

Until now, virtually all of the health-related technologies that have emerged in the areas of telemedicine, eHealth and mHealth still require human interaction through a graphical user interface. Another set of technological solutions, however, commonly known as the Internet of Things (IoT), refers to the addition of network connectivity to objects, buildings, devices, and other items.

In the health-care sector, for example, IoT solutions can collect data from sensors spread over a specific territory, from a single residence to a large geographic region. Given that external parameters such as temperature, air quality, and humidity can have an impact on health, these sensors can be used to provide information about the factors affecting an individual’s environment. Connected to automatic alert mechanisms, these systems can, for example, be used to warn someone with a certain medical condition when critical factors are reached. “The many uses of the systems and products that connect to the Internet of Things (IoT) are changing business in numerous industries. Patients and providers both stand to benefit from IoT carving out a bigger presence in health care” (SearchHealthIT.com 2016).

1.3 The Overflowing Cube

Based on what we know is already happening, it is easy to understand that a major problem currently facing health-care professionals is the sheer volume of available data. Therefore, the question is, “How can we process this volume without losing any vital information?”

The answer is the concept of digital health, which also takes into account a new reality that has followed in the wake of the sector’s technological revolution. In effect, the fundamental paradigm has evolved from one that is doctor-centric (where everything was done for the doctor or medical staff, while the patient was considered secondary), to patient-centric. Notably, that paradigm does more than make the patient the principal focus; it also asks patients to be active participants in their own health management.

As a result of that shift, health-care professionals found that clinical information alone is inadequate, and that other types of information—including genetic, social, family history, psychiatric—are needed.

The determinants of health are defined as, “The range of personal, social, economic, and environmental factors that influence health status” (ODPHP 2016).Footnote 3 Today, we can clearly say that such data (McGovern 2014) can be divided into three categories:

  • Clinical: These data are currently managed by the systems described earlier (hospital, laboratory, and radiological information systems; mHealth; telemedicine; etc.) Such data represent only 10% of the determinants of health, about 0.4 terabytes (400 gigabytes) of data per person per lifetime.

  • Genetic: These data are starting to be used in the field of “personalized medicine,” although information about usage remains largely anecdotal.

Today, personalized medicine, informed by a molecular understanding of disease, has brought new classification systems as well as more effective preventive and therapeutic interventions. Personalized medicine is ‘a form of medicine that uses information about a person’s genes, proteins, and environment to prevent, diagnose, and treat disease’ (National Cancer Institute 2011). (Offit 2011)

These data represent 30% of the determinants of health or, in technological terms, six terabytes of data per individual, per lifetime.

  • Exogenous: The remainder of an individual’s or patient’s data represents 60% of the determinants of health; in other words, 1,100 terabytes of data per person per lifetime.

Therefore, it clearly appears that digital health is the new technological concept that will need to deal with the new challenges the determinants of health will present. In particular, how do we manage this huge amount of data? Or, in other words, how will we manage the “big data” paradigm in health care?Footnote 4

To better understand this concept, Gartner analyst Doug Laney posited a theory in 2001. In a MetaGroup research publication, Laney introduced the concept of the 3Vs. “3 V data management: Controlling data volume, variety and velocity.Footnote 5” Currently, the literature has increased the model up to 6 Vs (Normandeau 2013), but the consensus appears to be that digital health strategies should be aimed at finding technical solutions that are based on a 4Vs vision:

  • Volume: Big data implies enormous volumes of information. At one time, all data was created by people; now, data is also generated by machines and networks, and human interaction with sensors or systems like social media. Therefore, the volume of data to be analyzed is massive.

  • Variety: This refers to the many sources and types of data, both structured and unstructured. We used to store data from sources like spreadsheets and databases. Now, data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio files, medical reports, medical image metadata, etc. This variety of unstructured data creates problems in terms of storage, mining, and analysis.

  • Velocity: The velocity of big data refers to the pace at which data flows in from sources like business processes, machines, and networks, and from human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous. If researchers and businesses can handle the velocity, this real-time data can help them make valuable decisions that can provide strategic competitive advantages and return on investment.

  • Veracity: This refers to the biases, noise, and abnormalities in big data. This raises a question, “Is the data that is being stored, and mined meaningful to the problem being analyzed?”

2 Is it Just a Question of Disorganization?

Due to the structure of the sector (lot of verticals), the amount of available technologies (including emerging ones arriving almost every day), and the amount of generated data, the meaning behind the title of this chapter becomes clear: In effect, the global picture looks very much like a portrait from the cubist period where all the elements are there, but not in their expected place.

Earlier, we reviewed some of the basic definitions that can help us better understand the digital environment in the health-care sector. However, even if there are some complex processes and a vertical structure, it seems that complexity is used more as an excuse than an insurmountable problem. Therefore, we will try now to understand why, for this specific sector, digital transformation looks like cubist painting.

2.1 Health Care: A Big World in a Small Word

Like the overuse of the word “green” in recent years, everyone these days is constantly referring to “health” or “healthy.” A car becomes a healthy car, food is tagged as (at least) healthier than before (e.g., a new recipe). Thus, as an individual, if you want to understand exactly what “healthy” means, it becomes a bit of a nightmare. How, then, do we make it more understandable? The answer was alluded to earlier in the discussion of the theory of the V4s. However, the purpose of this publication is not to describe every element of the health-care sector from A to Z. Better to focus on determining what kind of data should be considered for inclusion in the digital health paradigm.

Even if that list of data will never be fully completed, it would still need to address how to manage, in an era of digital transformation in health care, the following data categories:

2.2 Data from Doctors and Medical Staff, Including Hospital and Mental Health Workers

This set of information is composed of the various types of data coming from medical devices or medical applications, which breaks down into two commonly accepted categories: “Structured data,” provided mainly by applications and medical devices (representing 20% of the total available data), and “unstructured data” (coming from sources such as doctors, nurses, and radiology and laboratory reports, and representing about 80% of the available data, not all of it in electronic form).

2.3 Data from Social-Health Services and Affiliates

Almost all of the information used in the social-health sector is unstructured and composed of two types. In the first, we find rules, recommendations, and regulations applicable to specific, defined profiles. The other type contains information about the individual, some of which (mainly personal information) are electronic and structured, while the majority is composed of unstructured data in the form of reports and analyses.

2.4 Data from Utility Providers (e.g., Water, Electricity)

The information from these providers is important for the health-care sector for diverse reasons. First, they inform on the safety of the consumption of the service provided (e.g., polluted water). They also provide information on the status of the individual. For example, in some countries, such as France, an energy provider can know when a patient is undergoing hospital-type care at home better than the hospital itself (unless the hospital is overseeing that in-home care). They can also provide information on potential risks based on the service consumption patterns. For example, in an experiment in Bolzano in Italy, researchers were able to detect when some residents appeared to be in the early stages of Alzheimer’s disease by analyzing their water consumption (which was more erratic than the “normal” pattern). The data from such operations are structured (mainly numbers), but need to be filtered or analyzed to reveal useful health-care insights.

2.5 Data from Mobile Devices, Apps, and Sensors Set Up by Medical or Social Staff

The information collected through these channels faces various challenges or inhibitors. The first such challenge relates to their structure. The data captured is mainly structured, with some unstructured information (mainly comments) stored in the solution back-end, which is not broadly accessible. The second challenge relates to the veracity of the data, as the information is captured through ICT components that are easily obtainable on the Web and lack any medical controls.

2.6 Actors’ Roles and Strategies

As we have already said, the health-care sector is composed of different actors who do not always have the same objective; sometimes, they may have opposing goals, with each actor developing their own strategy aimed at reaching their particular objectives. However, it is interesting to note that all of these actors, at a certain point in time, are using more or less the same data to achieve their strategy. Therefore, we can create a basic list of actors as follows:

2.7 Patients

Before being a patient, the targeted person is an individual with loved ones. Therefore, his strategy in life is centered on his own interests, meaning (from a health-care point of view) he has a need to receive the best advice to ensure that he stays healthy, preferably with a minimum of constraints, no cost, and dedicated services. Some individuals may be willing to pay a little bit more to reinforce this healthy quest. However, even if that is true of most people in general, at an individual level, goals and preferences will vary. Some will prefer more constraint, but less cost. Others will prefer the reverse. Some will prefer to ignore all advice or warnings. To summarize, we would say that the patient’s (individual’s) strategy is very self-oriented (with the exception of concerns aimed at family members), which means they will be looking for a minimum of constraint in terms of treatments, institutional spending, and rules (private or public).

2.8 Doctors and Medical Staff

In the health-care ecosystem, there are a lot of different actors labeled “doctors and medical staff.” At this stage, it is important that we not focus on their primary mission and strategy, which is “taking care of the patient and caring for him anywhere, at any time.”

Rather, we are looking at this group as falling into two main categories: the public sphere (public servants) and the private for-profit sector. Public servants deliver a service to patients that, strictly speaking, can be provided without adding additional value. Conversely, private sector actors may propose unnecessary services to patients solely to generate more profit.

2.9 Pharmaceutical Companies

These companies are key, as they provide the raw material to cure patients. They have a high economic impact on the market and can even influence public policy, such as when a government decides not to add a manufacturer’s drug to the official/approved formulary, which means it will not qualify for reimbursement. Such companies also decide which diseases they will research and develop drugs for, and which diseases they will ignore (e.g., rare diseases).

2.10 Medical Manufacturing Companies

From electrocardiogram machines and insulin pumps to prosthetics, many medical devices have been available in the marketplace for a long time and, as a result, are a common and accepted part of the system. But now, new devices are emerging that do not fit into the traditional definition of a medical device. As a result, they often end up falling into a legal desert that regulatory agencies are only now starting to look at.

2.11 Payers

Like medical staff, health-care payers also fall into two categories: the public sector (i.e., mainly national “single payer” insurers covering the reimbursement of drugs and medical activities), and private insurers that provide up to 100% of what the public insurer does not cover.

Public payers, because they decide what drugs and medical procedures will be covered, also play a regulatory role. (Exactly what procedures are covered can vary wildly between countries; gestational surrogacy, as just one example, may be reimbursed in one jurisdiction and banned in another.)

The private payer’s approach is based on risk management, which can look a lot like gambling. For example, the payer might assume (bet) that among 10 insured people, only one will need reimbursement, and that person’s claim will total only one third of what the 10 have paid in premiums. The problem with such a model is sustainability. As the population ages, fewer people are available to become new subscribers, while the risks (insurance claims) are increasing. Where this model is still active, a new approach will need to be found to continue providing everyone with equal access to health care.

2.12 Governments

In addition to their role as both service provider and payer, governments are the legal entities that create or alter laws to support health-care practices that are beneficial and ethical, and respectful of human rights. Their targeted goals are to ensure a healthy population, limit/reduce health-care costs, and support improved health-care practices. To accomplish this, governments need to put mechanisms in place that are capable of measuring whether or not their goals are being realized.

2.13 Differing Perspectives

As we have shown, one of the major problems within the health-care sector is there are a large number of actors with very different—and even opposing—perspectives. Thus, there is no consistency of approach; each participant acts according to their own objectives and particular point of view and level of knowledge. Each creates their own definition of continuity of care, and how such care will be provided.

One of the best examples of knowledge disparity came into sharp relief in 2015, when a solution provider for a national insurer/payer happened to notice that health-care providers were referring to ICD-9 or ICD-10 codes. “We should get some information about that,” he said. Of course, virtually anyone working in health care understands that ICD stands for the World Health Organization’s International Classification of Diseases, which has been the standard diagnostic tool for epidemiology, health management, and clinical purposes (World Health Organization 2016) for decades. ICD-10 (i.e., the 10th edition of the standards) was first published in 1992. In other words, more than 20 years later, some decision-makers are unaware that such widely accepted health-care standards exist.

The fact that one person lacks basic knowledge does not, of course, mean that everyone in an entire sector is similarly oblivious. However, there are still many basic “disconnects” between the various actors. For example, one European country (that will remain nameless) received several grants to help modernize its health-care system: One for e-prescriptions, one for e-referrals, and one for modernizing the national insurance system. The problem is that each was developed and presented as separate projects, with no communication between them.

Unfortunately, such “silo” funding is not uncommon; funding models around the world use a similar approach, i.e., they fund a specific project, not an integrated piece of a bigger picture. In the absence of governments with the right knowledge (and the will), major health-care projects—from systems integration applications to user portals—will remain separate and distinct, implemented again and again in difference places, using the same disconnected components.

Some countries, however, are finding their way toward a holistic approach to digital services. Estonia, for example, has developed a true e-government infrastructure where services are provided using shared technology and existing components are reused or adapted as needed. This came about because Estonia chose to take an “enterprise architecture” point of view. This involves starting with an initial “frame” that is bigger than necessary, which makes growth that much easier: every time a new service is required, the first step is to understand what can be used/reused from within any part of the current architecture. Only then are decisions made about what needs to be built or added, and what standards need to be in place to deliver the required service to citizens.

In this type of model, any usable infrastructure components that may have been developed for a previous project (say, a technological upgrade to the tax collection system) can be repurposed for any other type of need, such as digital health services.

2.14 Toward a Common Utopia

Because of the number of actors and (often opposing) perspectives at play in today’s health-care marketplace, the entire process is reactive: an event occurs, a person is affected, and care is provided and paid for.

Now, imagine a system where, instead of reacting, we anticipate. A system where personal health forms part of an overall picture that is constantly adjusted throughout a person’s lifetime. A system where the right services are provided for an appropriate price that is decided by facts, not by placing bets on people’s health.