AI-generated summary
Nearly a decade ago, the Bankinter Innovation Foundation’s 2015 report on “Digital Health” forecasted a transformative future driven by innovations such as digital avatars, big data, artificial intelligence (AI), and machine learning. The report highlighted how digitalization would revolutionize healthcare by enabling personalized, preventive, and participatory medicine. Concepts like detailed digital health representations integrating genetics and lifestyle were expected to improve diagnosis, treatment, and early intervention. Genetic data was seen as pivotal for advancing preventive medicine and tailoring healthcare strategies. Moreover, the emergence of big data and AI promised to enhance medical decision-making, optimize treatments, and predict disease outbreaks, while new business models were anticipated to adapt to demographic shifts and chronic disease management.
Fast forward to 2024, many of those predictions have materialized, accelerated notably by the Covid-19 pandemic. Digital health has evolved into a practical reality, with sophisticated platforms delivering personalized care through genomic and lifestyle data. AI and machine learning have become essential tools in diagnosis, treatment, and managing health crises, exemplified by their role in analyzing Covid-19 data. Business models have shifted toward prevention and chronic disease management to meet changing societal needs. Despite initial challenges like technological adoption resistance, data privacy concerns, and potential inequities in access, these barriers have largely been addressed. Digital health today enhances healthcare accessibility, efficiency, and personalization, heralding a promising future where technology fundamentally improves patient outcomes worldwide.
From the predictions of 2015 to the realities of 2024: a journey through the digital transformation in Health
Almost a decade ago, the Bankinter Innovation Foundation’s Future Trends Forum report on “Digital Health” outlined a promising future full of innovations and emerging technologies. Among the thirty world experts on the subject, there were such relevant figures as William Haseltine, Esther Dyson, Joseph Kvedar, Francisco Curbera and Gautam Jaggi.
Today, in 2024, is the right time to reflect on those predictions and analyze the current digital health landscape.
Key innovations, technologies and trends
Digital health has undergone a remarkable transformation in recent years, evolving from a futuristic concept to a palpable reality that directly impacts the lives of millions of people. This change has been driven by significant technological advancements and a renewed focus on healthcare efficiency and personalization. Below is a summary of the highlights that were analysed in 2015 and how they have evolved, marking a before and after in the field of digital health:
Digitalization of health
The digitalization of health represented a radical shift in the healthcare paradigm, driven by several key innovations and concepts: first, digitalization promised to restructure and improve health systems. This involved adopting new technologies, business models, and approaches to patient care, displacing old structures and creating new opportunities for more efficient, patient-centered care.
At the heart of digitalization was the idea of the “digital avatar” – a detailed, personalized digital representation of an individual’s state of health. This concept anticipated the use of personal health data, integrating information from genetics to lifestyle habits, to create a holistic and dynamic model of the individual’s well-being. This approach was expected to facilitate more accurate diagnoses and treatments, and allow for more accurate predictions of health risks and needs for preventive intervention.
The report also highlighted the growing role of genetics in preventive medicine. With the advancement of genetic sequencing technologies, genetic information was expected to play a crucial role in identifying disease predispositions and personalizing prevention and treatment strategies. Preventive medicine, driven by genetic data, promised an era of earlier, more precise interventions, reducing the risk of chronic diseases and improving long-term health outcomes.
In summary, the analysis of the digitalization of health in 2015 anticipated a future in which technology would improve the efficiency and effectiveness of medical care, and transform the relationship between patients and their health, promoting an era of more personalized, preventive and participatory medicine. The evolution of digital health in recent years has been significant and transformative, accelerated in part by the Covid-19 pandemic, which rapidly boosted the adoption of emerging technologies. One of the most notable developments has been the maturation of personalized medicine. “Digital avatars,” which were initially theoretical concepts, have evolved into sophisticated platforms capable of using genomic and lifestyle data to deliver highly personalized advice and treatments.
Revolutionary tools in digital health
In 2015, several technological tools stood out that were expected to have a revolutionary impact on digital health. These tools not only promised to improve health care, but also to transform the way we interact with health and wellness:
Big Data in health: Big Data was identified as a key element in the digital health revolution. The ability to collect, store, and analyze large volumes of health data was expected to unlock new possibilities in medicine. This included everything from optimizing drug research and development to personalizing treatments and predicting disease outbreaks. Big Data would allow healthcare professionals to gain deeper, evidence-based insights , improving decision-making and healthcare efficiency.
Artificial intelligence (AI): AI was emerging as a fundamental driver in digital health. It was anticipated that its ability to analyze complex data sets at unprecedented speed and accuracy would revolutionize areas such as diagnosis, clinical decision-making, and treatment management. AI promised to improve diagnostic accuracy and personalize treatments to each patient’s individual needs.
Machine learning: Machine learning, a branch of AI, stood out for its ability to learn and improve from data without being explicitly programmed. In the context of digital health, this meant systems that could be continuously adapted and improved, offering more accurate and effective solutions for diagnosis, disease prediction and treatment optimisation.
These revolutionary tools would improve the technical aspects of healthcare and foster a more holistic and patient-centered approach, opening up new possibilities in the prevention, diagnosis and treatment of diseases. These technologies, which were avant-garde almost a decade ago, have today become fundamental tools in medicine, improving diagnoses, optimizing treatments and managing chronic diseases more efficiently, with a marked focus on personalization and efficiency. A clear example of this acceleration is the use of AI for the rapid analysis of large volumes of data related to Covid-19, which has helped to identify infection patterns and develop more effective treatment strategies.
Innovative business models
Global trends that would influence digital health were identified, including new business models, investment opportunities, and the social impact of innovations:
Global trends such as an aging population, the rise in chronic diseases, and changes in consumer behaviors were identified, which would directly affect digital health. These trends would drive the need for business models that could adapt to an ever-changing health landscape, focused on prevention, long-term management of chronic diseases, and personalization of care.
A significant increase in funding was also expected from startups and companies developing innovative solutions in digital health, from wellness and fitness apps to health data analytics platforms and connected medical devices.
Business models in the field of digital health have undergone significant evolution, proactively responding to global trends such as population aging and the increase in chronic diseases. These models have begun to focus more on prevention and long-term management of health conditions, adapting to the changing needs of society. The Covid-19 pandemic, in particular, has emphasised the importance of such models, highlighting the need for more resilient and adaptive health systems.
Challenges and opportunities
In 2015, in addition to identifying innovations and technological advances, a series of challenges and opportunities that would accompany this transformation were highlighted. First, and despite optimism about new technologies, it was recognized that there were significant barriers in terms of technological adoption. These barriers ranged from resistance to change by healthcare professionals and patients, to challenges in integrating new solutions with existing health systems.
Health data security and privacy were also identified as key concerns. The increasing digitization of health records and the accumulation of large volumes of personal health data posed significant risks in terms of privacy and security.
Finally, the opportunity of digital health to improve access to health care, especially in remote or underserved areas, was highlighted. However, the risk that existing inequalities in access to technology could exacerbate disparities in health care was also recognized. It was essential to address these inequalities to ensure that the benefits of digital health were spread equitably across different populations and regions.
The barriers initially identified in 2015 have been overcome, in a process that has been accelerated by the pandemic. Today, aspects such as improved accessibility, efficiency in care, and personalization of treatment are tangible realities, marking a new era in healthcare and promising an even brighter future for digital health.
