Textbook of Digital Health

Introduction

Digital technologies are now used across all areas of modern healthcare. They influence how clinical services are delivered, how patients and clinicians communicate, and how health systems are run. The term "digital health" captures this broad convergence of computing and data science with clinical medicine and public health. The changes involved go beyond the digitisation of paper records, raising questions about the relationship between technology and human wellbeing and about who benefits when healthcare becomes digitised. This chapter provides a foundation for understanding the historical evolution of digital health over the last 20 years, the key stakeholders involved, and the forces that drove its widespread adoption.

Defining Digital Health

The World Health Organisation (WHO) defines digital health as "the field of knowledge and practice associated with the development and use of digital technologies to improve health" (WHO, 2021). This broad description includes a wide range of technologies from electronic health records and telemedicine to artificial intelligence (AI) decision support systems and wearable health monitors. However, the scope of digital health continues to expand as new technologies emerge and find applications in healthcare. Understanding this growing field is important for healthcare professionals working in an increasingly technology-driven industry.

Digital health has become an umbrella term that draws together several overlapping fields. Health informatics, the oldest of these, is concerned with the acquisition, storage, retrieval, and use of healthcare information. It sits at the intersection of computer science, information science, and clinical practice, and has shaped the design of systems for clinical decision support and care coordination since the 1970s. The field of health informatics is often divided into healthcare specialities, such as medical informatics, nursing informatics, and pharmacy informatics. A somewhat newer term, electronic health (eHealth), refers more specifically to healthcare services and information delivered or enhanced through the internet, while in the late 2000s, as smartphones began to be used by clinicians and patients, the term mobile health (mHealth) extended this further by using smartphones and tablets to bring healthcare into patients' daily lives outside the clinic.

In practice, these distinctions have become somewhat academic. A modern telehealth platform typically manages patient data (health informatics), delivers services via the internet (eHealth), and is accessible through a smartphone app (mHealth). Patients and clinicians rarely think in terms of these categorical boundaries, and for good reason: the technologies have converged in ways that make the labels less useful than they once were. Government agencies and technology companies around the world have, since the mid-2010s, gradually adopted the more general term "digital health" to describe the field.

Digital health has since expanded to encompass artificial intelligence and machine learning, blockchain-based approaches to health data management, extended reality (virtual and augmented reality) for medical training and therapy, and the Internet of Medical Things (the growing network of connected clinical devices and sensors). What links these technologies is that they are designed to improve clinical outcomes, support patients and clinicians, and enable health systems to work more efficiently.

The rapid emergence of generative artificial intelligence and large language models (LLMs) since 2023 deserves particular mention. These systems are already being applied to clinical documentation by generating draft consultation notes from recorded conversations, as well as to clinical decision support and patient communication. The technology raises substantial questions about accuracy, liability, and the appropriate boundaries of algorithmic involvement in clinical care, but it is already changing workflows in healthcare organisations worldwide.

Historical Evolution of Digital Health Technologies

The journey towards digital health began several decades before the term existed. The 1960s saw the first experiments with computerised medical records, though widespread adoption was limited by computing power constraints, high costs, and resistance to changes in established workflows. Hospital information systems emerged in the 1970s, primarily focused on administrative functions such as billing and scheduling rather than clinical care, but they laid the foundation for more comprehensive systems to follow.

The 1980s and 1990s brought significant advances in medical imaging technology. Digital radiography, computed tomography (CT), and magnetic resonance imaging (MRI) generated larger quantities of digital data, necessitating new approaches to storage and transmission. Picture Archiving and Communication Systems (PACS) emerged to manage this digital imaging revolution, representing one of the first widespread deployments of digital technology in clinical settings. The success of PACS demonstrated that clinicians would adopt digital tools when they provided clear workflow advantages.

When the internet was commercialised in the 1990s, moving from military and academic use to people's own homes, it opened new possibilities for health information and services. Patients gained access to medical information previously available only to professionals, altering the doctor-patient dynamic. Early telemedicine programmes demonstrated the feasibility of remote consultations, though technical limitations and regulatory uncertainty constrained growth.

The 21st century saw a considerable acceleration in digital health adoption. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 (US Congress, 2009) in the United States, implemented to stimulate the US economy after the global financial crisis, incentivised the adoption of electronic health records, transforming American healthcare within a decade. The proliferation of smartphones enabled an explosion of health applications, while social media created new channels for health communication and community building.

More recently, the COVID-19 pandemic served as a major catalyst for digital health adoption. Medicare telehealth utilisation increased 63-fold during the pandemic (HHS, 2021) as healthcare systems rapidly pivoted to remote care delivery. Regulatory barriers that had constrained telehealth for decades were relaxed or eliminated within weeks. This forced acceleration demonstrated both the potential and limitations of digital health technologies.

The Digital Health Ecosystem

Consider what happens when a patient uses a smartphone app to monitor their blood glucose levels. The data flows into their endocrinologist's electronic health record, triggering an alert prompting a medication adjustment. The insurance company receives a claim for the remote monitoring service. Somewhere, a startup's data scientists analyse aggregated patterns to improve their algorithm, while regulators review whether the app's diagnostic suggestions require medical device approval.

At the centre of this ecosystem are healthcare providers caring for their patients. Clinicians have to make digital tools work within often chaotic clinical realities. When a physician spends more time clicking through an electronic record than examining a patient, it can affect the quality of care and can reinforce negative attitudes toward future digital innovations. Yet when technology genuinely streamlines workflows (such as an AI system that drafts clinical notes from recorded conversations), providers become strong advocates for adoption.

Patients, meanwhile, are increasingly expected - and increasingly expect - to play an active role in their own care. People accustomed to the convenience of online banking and real-time delivery tracking are often frustrated by healthcare systems that still rely on posted letters and telephone-only booking. Patients managing chronic conditions tend to be among the most engaged users of digital health tools, and the data they generate can be valuable both for their own care and for broader research.

The digital health technology sector has developed into a diverse range of companies and organisations. Established healthcare IT vendors such as Epic and Oracle Health (formerly Cerner) have built substantial businesses around electronic health records, accumulating deep knowledge of regulatory requirements and clinical workflows, though they are sometimes criticised for being slow to innovate. Startups, by contrast, move quickly but often underestimate the complexity of healthcare; consumer-inspired designs do not always withstand the rigours of clinical reality. Large technology companies such as Google, Apple, Amazon, and Microsoft have all made significant investments in healthcare, drawn by the size of the market, though they have often scaled back initiatives after discovering that health data carries expectations and obligations quite unlike those in their core businesses.

Healthcare funders (public and private) and regulators exert considerable influence over what is adopted and how quickly it is adopted. For example, when Medicare in the United States decides to reimburse remote patient monitoring, it can drive increased adoption among clinicians; if the FDA clarifies that a particular wellness app does not require medical device approval, it may attract new investment from private funders to expand. In the United Kingdom, NICE technology appraisals and NHS England procurement decisions play an analogous role. The relationship between innovation and regulation is a recurring theme in digital health: regulators must balance patient safety with the risk that overly cautious rules delay access to technologies that could genuinely do good.

Table 1.1: Digital Health Stakeholders

Drivers of Digital Health Adoption

By 2013, Japan had crossed a demographic threshold that no large nation had faced before: more than a quarter of its population was over 65 years old (World Bank, 2024). The implication for healthcare was that demand for services would surge precisely as the workforce available to provide them shrank. Japan responded by investing heavily in robotics for elder care, remote monitoring systems, and telemedicine infrastructure. Japan's experience illustrates one of the major forces driving digital health adoption worldwide: the rising demand for healthcare and the increasingly constrained capacity to meet it.

Across developed nations, similar demographic pressures are mounting. Populations are ageing, chronic diseases are multiplying, and healthcare workforces struggle to keep pace. A patient with diabetes, heart disease, and early dementia might need continuous monitoring, regular specialist consultations, and careful medication management. Traditional healthcare models deliver these services through face-to-face encounters that consume considerable clinician time. Digital health offers an alternative: remote monitoring that flags problems before they become emergencies, AI systems that handle routine tasks so clinicians can focus on complex decisions, and telemedicine that extends specialist expertise to underserved areas without requiring patients or doctors to travel.

Economic pressures can compound these demographic trends, and healthcare spending continues to consume an ever-larger share of national budgets. In the United States, administrative costs alone account for an estimated 30–34% of total healthcare expenditure (Himmelstein et al., 2020), a figure that has remained persistent despite decades of digitisation (Sahni et al., 2023). Much of this spending reflects inefficiencies that better information systems could, in principle, reduce. For example, duplicate investigations were ordered when another provider's results were unavailable, or hospital admissions that might have been avoided with a remote monitoring infrastructure in place. Whether digital solutions such as automated prior authorisation, AI-assisted clinical coding, and health information exchanges will deliver the savings their proponents anticipate remains to be seen, but the economic case for trying is difficult to dismiss.

Another driver is the shift in patient expectations. People accustomed to ordering food, booking travel, and managing finances through smartphone apps bring those expectations into healthcare settings. Many practices now offer online booking and patient portals, but where scheduling a GP appointment still requires a telephone call during working hours, or where test results arrive by letter a fortnight after the blood was drawn, the contrast with other areas of life is conspicuous. This expectation gap creates real pressure on healthcare organisations, particularly in systems where patients have some choice of provider.

Underpinning all of this is the technology itself, which has matured considerably in the last decade or so. Ingestible capsule cameras can now transmit images from within the gastrointestinal tract. Machine learning algorithms have been developed that can detect diabetic retinopathy from retinal photographs with accuracy comparable to trained ophthalmologists (Gulshan et al., 2016), and subsequent real-world deployments, such as Google Health's ARDA system in screening programmes in Thailand and India, have demonstrated that such performance can be sustained outside research settings (Ruamviboonsuk et al., 2022). The smartphone, now carried by billions of people, serves as a platform for health applications that would have required dedicated clinical equipment not long ago. As the evidence base for digital interventions grows, so does the justification for adoption - though, as later chapters discuss, the evidence remains uneven across different technologies and clinical contexts.

Barriers and Challenges

Imagine a hypothetical patient, whom we will call Margaret. She manages three chronic conditions (heart failure, type 2 diabetes, and chronic kidney disease), each treated by different specialists. Despite everyone using electronic health records, her cardiologist cannot see the blood test results ordered by her nephrologist. Her diabetes management app does not communicate with her hospital's patient portal. After a recent emergency department visit, her primary care physician learned about it only when Margaret mentioned it during a routine appointment two weeks later. Margaret's care is digital but fragmented. Digitising information is not the same as connecting it.

Interoperability (the ability of different systems to exchange and use information) remains digital health's most persistent challenge. The technical standards exist; organisations like HL7 have developed sophisticated frameworks for health data exchange. The barriers are often commercial and organisational rather than technical. Health systems may view their data as a competitive asset, reasoning that patients are less likely to switch providers if their records cannot easily follow them. Vendors may design systems that integrate smoothly with their own products but create friction with competitors. Even well-intentioned efforts at data sharing often founder on inconsistent implementation of standards, with one system's patient identifier failing to match another's. The result is that digital health's promise of comprehensive, coordinated care remains unrealised for patients like Margaret.

Privacy and security concerns add another layer of complexity. Health data is highly sensitive and valuable. A stolen credit card number can be cancelled, but the damage from a leaked HIV diagnosis or psychiatric history cannot be undone. Healthcare organisations have become prime targets for cyberattacks, with ransomware incidents disrupting hospital operations and data breaches exposing millions of patient records. Each high-profile incident erodes public trust, making patients more reluctant to share information that could improve their care or contribute to research. The regulatory response, including HIPAA in the United States and GDPR in Europe, creates essential protections but also compliance burdens that can slow innovation and make smaller organisations hesitant to adopt new technologies.

There is also a risk that digital health will widen rather than narrow health disparities. The communities most in need of healthcare improvements, such as rural populations, low-income groups, and older adults with limited technical experience, are often those with the least access to the technology and the digital literacy needed to use digital health solutions. This "digital divide" and strategies for addressing it are explored in detail in Chapter 10: Patient Engagement and Digital Tools.

Regulation presents its own challenges, particularly for innovators trying to bring new technologies to market. Is a smartphone app that suggests medication doses a medical device requiring FDA approval, or is it a wellness product that falls outside regulatory scope? The answer often depends on specific features and marketing claims, creating uncertainty that can delay development and discourage investment. Regulators face the difficult task of maintaining safety standards while avoiding rules that would stifle beneficial innovation. Finding this balance requires ongoing dialogue between regulators, developers, clinicians, and patients. These relationships take time to build and maintain.

Finally, there is the workforce. Medical education has historically given little attention to informatics, emphasising the biological sciences, pharmacology, and clinical reasoning. Many practising clinicians completed their training before electronic health records became widespread and have had to adapt on the job, with varying degrees of enthusiasm and success. Digital fluency across the health workforce is consequently uneven. Addressing this requires attention to how digital tools fit into clinical workflows, to the change management processes that support adoption, and to the legitimate frustrations of clinicians who find that technology has, in some respects, made their work harder rather than easier.

Table 1.2: Barriers to Digital Health Adoption

Ethical Framework for Digital Health

Digital health technologies have raised significant ethical questions that require systematic analysis. The Beauchamp and Childress ethical framework (Beauchamp & Childress, 2019) that has become central to biomedical ethics is equally relevant for digital health, though digital technologies present novel challenges that extend and sometimes strain these principles.

Autonomy concerns respect for patient self-determination and informed decision-making. In many ways, digital health can enhance autonomy by providing patients with access to their health information, enabling informed participation in care decisions, and supporting self-management. However, digital technologies also pose threats to autonomy. Algorithmic recommendations may constrain choices, complex privacy policies may make informed consent more difficult, and power imbalances between technology providers and users may limit self-determination.

Beneficence means acting in patients' best interests and promoting their well-being. Digital health technologies should demonstrate genuine benefit in addition to technical capability. This principle means that we should look for evidence that digital interventions improve outcomes that matter to patients (not just commercial companies or providers). It also requires attention to implementation, since a beneficial technology poorly implemented may fail to deliver benefits or may cause harm. Beneficence in digital health extends beyond individual patients to consider population-level benefits, though this could create new tensions when individual and collective interests diverge.

Non-maleficence is the obligation to avoid causing harm. This can take on new dimensions in digital health. Direct harms include privacy breaches, misdiagnosis by algorithms, and physical injury from device malfunction. Indirect harms include automation bias (over-reliance on algorithmic recommendations), deskilling of clinicians, and the psychological impacts of constant health monitoring. Novel harms can emerge from AI systems that may perpetuate or amplify existing biases, causing systematic disadvantage to already marginalised groups. Assessing harm in digital health requires considering both the immediate effects and the long-term consequences and systemic impacts that may be difficult to anticipate.

Justice requires fair distribution of benefits and burdens. Digital health raises justice concerns at multiple levels as access to digital health technologies is unequally distributed, with the digital divide threatening to widen existing health disparities. The benefits of health data often accrue to technology companies and researchers while risks fall disproportionately on data subjects. Algorithmic systems may systematically disadvantage certain groups through biased training data or design choices that fail to account for diversity. Justice in digital health means that we must pay proactive attention to equity by actively designing for inclusion and monitoring for disparate impacts.

Beyond these four principles, digital health ethics must address novel challenges, including data ownership and governance, the appropriate role of artificial intelligence in clinical decision-making, the transformation of professional ethics as technology reshapes clinical practice, and the global dimensions of digital health development, in which benefits and harms may be distributed across national boundaries.

Throughout this textbook, ethical considerations are integrated into each topic area. Every design choice, every implementation decision, and every policy embeds ethical assumptions that warrant examination.

Global Perspectives on Digital Health

When Estonia regained independence in 1991, it faced the task of rebuilding its infrastructure nearly from scratch. Rather than replicating the paper-based systems of established nations, Estonian leaders made a deliberate choice: they would build digitally from the start. Today, Estonians access their complete health records through a unified national portal, with each diagnosis, prescription, and test result available digitally. Patients can see who has accessed their data and when, with blockchain technology ensuring the integrity of those access logs. Establishing this system required decades of investment, cultural change, and political commitment. However, Estonia demonstrates what becomes possible when a nation treats digital health as a national strategic priority.

High-income countries such as the United States and the United Kingdom start from a harder position because decades of investment in older systems have created technical debt that makes transformation slow and expensive. A hospital that spent millions implementing an electronic health record system five years ago cannot easily abandon that investment when superior technology emerges. The complex stakeholder landscapes in these countries, with powerful professional associations, entrenched vendors, and fragmented governance, can slow consensus-building and implementation.

Low- and middle-income countries face different constraints and opportunities. Many lack the reliable electricity, internet connectivity, and computing infrastructure that digital health solutions need. Yet these limitations can sometimes enable innovation. In Kenya, where traditional banking infrastructure was sparse, mobile money services like M-Pesa leapfrogged directly to phone-based finance. Similar leapfrogging is occurring in healthcare, with mobile-first solutions delivering health information, appointment reminders, and even diagnostic support to populations that never had access to the technologies these innovations replace. Community health workers equipped with smartphones can collect data, receive decision support, and connect patients to remote specialists, extending healthcare's reach to areas that might never see a traditional clinic.

International organisations have recognised digital health as a development priority. The World Health Organisation's Global Strategy on Digital Health (WHO, 2021) provides a framework for national planning while emphasising that effective solutions must be adapted to local contexts rather than imported wholesale. The key insight is that digital health is a sociotechnical challenge, requiring alignment between technology, policy, culture, and resources that varies considerably across settings.

Countries that have made the most progress share certain characteristics: committed leadership that treats digital health as a national priority; sustained investment over years rather than one-off projects; careful attention to governance, standards, and interoperability from the start; and genuine engagement with the clinicians and patients who will use these systems. Singapore's integrated health information system, Denmark's patient-centred digital services, and Israel's data-rich environment, which enables rapid research, all reflect these principles in different ways. Their experiences offer lessons for others, though the specific solutions must always be adapted to local realities.

Summary

Digital health is changing how healthcare is delivered and experienced, though the pace and extent of that change vary considerably across settings, technologies, and patient populations. Understanding both the potential and the limitations of digital approaches is important for healthcare professionals, policymakers, and researchers.

This introductory chapter has established the foundation for exploring specific digital health domains in subsequent chapters. We have defined digital health and its component disciplines, traced its historical evolution, mapped the stakeholder ecosystem, examined the forces driving adoption, acknowledged persistent barriers, and considered global perspectives.

The chapters that follow examine each major area of digital health in greater depth, covering the technologies, the evidence for their use, and the practical considerations involved in implementation.