DIGITAL HEALTH CARE: CAN WE AVOID THIS REVOLUTION?

Dr. Mukul Chandra Bora

(Director, Dibrugarh University Institute of Engineering and Technology. He can be reached at drmukulcbora@gmail.com.)

The concept of “Industry 4.0” was first coined by the German government to describe a wave of technological advancements in manufacturing, aimed at maintaining Germany’s global industrial competitiveness. This shift has necessitated significant changes in various professions, requiring individuals to adapt to new tasks and utilise cutting-edge technology, which now plays a central role in their work lives. This paper aims to provide an overview of Industry 4.0, encompassing its key components such as automation, data exchange, and emerging technologies like cyber-physical systems, the Internet of Things, big data analytics, and additive manufacturing. Industry 4.0 facilitates the integration of intelligent machines, human operators, physical objects, and manufacturing processes, creating agile and interconnected value chains.

Beyond its roots in manufacturing, Industry 4.0 has evolved to impact diverse sectors, including healthcare, ushering in a new era characterised by increased connectivity, automation, and data-driven decision-making. However, the full extent of integrating Industry 4.0 principles into healthcare remains partially understood, with limited consolidation of its benefits and challenges. This paper delves into the intersection of Industry 4.0 and healthcare, highlighting nine key applications such as augmented reality, autonomous robotics, and big data analytics, along with identifying ten benefits and nine challenges within the realm of Healthcare 4.0. These benefits range from improved diagnosis and treatment to financial efficiencies, while challenges include issues like data fragmentation, complexity, and privacy concerns.

The notion of a new industrial revolution has been brewing for decades, gaining momentum since the late 20th century, about 30 years after the advent of the third industrial revolution. Unlike previous transitions, which took centuries to unfold, the shift to Industry 4.0 has been notably swifter, with the third industrial revolution lasting only around four decades.

Experts and practitioners concur on the profound impact of Industry 4.0 across various domains, notably healthcare, which stands as one of the most robust sectors today. Healthcare’s significant share, often exceeding 10% of GDP in developed nations, underscores its importance, with global spending projected to hit nearly $9 trillion by 2021. Amid escalating costs for both patients and providers, coupled with a growing demand for patient-centric care, the imperative for a digitized healthcare landscape intensifies. This evolution promises streamlined processes, enhanced physician efficiency, and a prioritization of preventive measures.

The adoption of Healthcare 4.0 solutions yields manifold benefits, including heightened surgical precision, fortified medical data security, optimized resource allocation in hospitals, and enhanced patient comfort. Central to this paradigm shift is robust data management, given healthcare’s status as a prolific data producer. Biomedical data, electronic health records (EHR), and physical records underscore the significance of healthcare data analytics in unlocking organizational advantages. Market projections suggest a compound annual growth rate of 18.2%, with revenues reaching $81.3 billion by 2030, underscoring the burgeoning demand for Healthcare 4.0 solutions. Despite progress, standardization of key definitions and concepts remains an ongoing endeavour.

The advent of digital health heralds a transformative era in medical practice akin to technological revolutions witnessed in other industries. Anticipate a paradigm shift in medical practice over the next 5–10 years, propelled by digital health tools, and advancements in computer science, machine learning, automation, robotics, virtual reality, wearables, and allied fields. This digital health transformation should cover every aspect of medicine and the patient’s journey, as well as medical education and research. Artificial intelligence algorithms are helping clinicians diagnose and manage a variety of medical conditions; major changes to the functionality of the electronic health record; improvements in workflows primarily assisted by smart systems, increasing efficiencies; and reducing repetitive tasks humans do right now that could be performed by computers and digital applications. Digital health will help empower patients to monitor their medical conditions better, assess their response to medications, and better know when and where to seek medical help versus exercising self-care. This revolutionary transformation could be a decisive moment in the history of medicine, but it also comes with some risks: the risk of dehumanising medicine, the risk of amplifying existing biases and healthcare inequities or creating new ones, the risk of making medical errors multiplicative, and many other potential disastrous scenarios.

CHALLENGES IN DIGITAL HEALTHCARE:

SOCIETAL FACTORS

The integration of technology into digital health systems is crucial, but its impact goes beyond just technological advancements. Effective digital health solutions must also address affordability and usability for a population that is both growing and aging. Adoption of new technologies in healthcare is often slow, influenced by regulatory uncertainties and concerns about accountability in the commercial sector. Moreover, navigating the complexities of a global digital health market and diverse healthcare systems presents additional challenges. A significant barrier lies in the low levels of digital and health literacy, particularly among older adults, which impedes widespread acceptance and use of digital health innovations.

ETHICAL CHALLENGES

The increasing digitization of healthcare and the growth of mobile and IoT devices as data collection tools raise many ethical issues. One commonly recurring theme relates to the exact nature of the role of consumer tech companies, such as Amazon, Apple, Google, Facebook, or Samsung, who have all entered the digital health domain. In particular, such companies offer solutions for collecting, storing, and analysing health data, which raises issues relating to privacy, data protection, and informed consent. The nature of health data is also changing; we are now collecting more private user-generated data, particularly data harvested from social media and through wearable technologies, than ever before.

INCREASED CONNECTED HEALTH SOLUTIONS

The main focus of digital health is to enhance data sharing among patients, devices, and clinicians, promoting smarter and more timely information exchange. This connectivity aligns with the principles of predictive, preemptive, and personalized healthcare. During the COVID-19 pandemic, connected health solutions have been pivotal. However, they raise concerns about safety and security. As medicine evolves towards personalized and preventative care through digital health applications, concepts of patient safety must adapt. Rapid technological advancements also bring safety challenges, with limited evidence-based research on new technologies’ health benefits. Demonstrating effectiveness remains a significant hurdle.

ROLE OF ARTIFICIAL

INTELLIGENCE

Artificial intelligence can utilize data generated in digital health systems to help with aspects of medicine, such as improved diagnosis, selecting treatments, and predicting clinical outcomes. The presence of AI solutions in digital health intensifies challenges surrounding safety, explainability, and fairness. In regard to safety, AI systems are held to higher perceived safety standards than humans; i.e., it is less acceptable for AI to make errors. Moreover, the risk to human life of AI-based systems is, currently, not well-studied, and there is a lack of standards for the verification and validation of such systems. There are also generalization issues associated with AI models, reproducing promising results, made on “limited” training sets, on real-world data. A recent systematic review of deep learning solutions in medical images found that only a minimal number of studies in this field were

THE POTENTIAL

OF GENOMICS

Technological advances and reduced costs have led to a growing number of people opting for genetic profiling, though outside of specific cases like rare disease diagnosis and cancer screening, genetic information isn’t widely integrated into routine medical care. Genomics holds promise for personalized healthcare, but achieving this potential requires further development of genetic risk scores relevant to broader clinical contexts and better interpretation of genetic variants. The interpretation challenge is compounded by millions of variants with no standardized definition. Addressing these issues involves facilitating data and computational resource sharing, which raises ethical concerns already explored in the article.

CONCLUSION: The COVID-19 pandemic has profoundly tested traditional healthcare systems, prompting a critical role for digital health solutions. These technologies are pivotal in reshaping medical care during and after the pandemic. However, their development and implementation face significant challenges stemming from pandemic-related issues and broader obstacles to digital health advancement. Five years ago, various challenges in digital health were identified, including multi-disciplinary approaches, big data in public health, MedTech innovations, self-management and personalized care, mHealth interventions, data sharing dilemmas, and the impact of social media on health knowledge and behaviour. These challenges remain pertinent today, augmented by new concerns about the role of digital health in managing infectious diseases like COVID-19. Addressing these challenges is crucial for advancing digital health research and fostering multidisciplinary efforts to tackle ongoing and emerging issues in this dynamic field.