Digital Transformation as a Solution for Global Healthcare Problems, de Patrick Eugster

9 setembro 2021

Digital Transformation as a Solution for Global Healthcare Problems, de Patrick Eugster

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Demographic and social changes are the leading causes of public healthcare spending, a significant issue that affects countries worldwide (WHO,2019). One solution to increasing costs in healthcare is innovation in the form of digital transformation. Digital transformation in healthcare consists of several different digital technologies such as virtual reality tools, wearable medical devices, telehealth, 5G technology, and AI-powered systems (Hermes et al., 2020). The technologies mentioned above enable streamlining physicians’ work, optimizing systems, improving patients’ outcomes, reducing human error, and lowering costs in general (Hermes et al., 2020). Therefore, this article structures digital transformation technologies in healthcare into the Internet of Things (IoT), big data, and artificial intelligence (AI). At last, the adverse effects and an example of digital technologies in the fight against COVID-19 in Brazil are analyzed.

The Internet of Things definition is: “the proliferation of sensors and actuators embedded in everyday things, coupled with the wide availability of high-speed Internet” (Bougouettaya et al., 2021, p.86). The trend of wearable medical devices strongly supports this technology. Heart rate sensors, exercise trackers, sweat meters, and oximeters are just a few examples of new wearable devices. Further, wearable devices enable medical technology companies and public health institutions to track better the well-being of individuals (Haghi et al., 2017). Additionally, patients are encouraged to focus on the prevention of diseases and maintenance of their well-being. Furthermore, the data collected from wearable devices and IoT can help personalize the healthcare experience and provide incentives for individuals.

In the next step, the data collected through IoT flows into the trend of big data. Big data in healthcare commonly refers to electronic health data, which are highly complex and hard to manage. Healthcare data can originate from different sources in the healthcare industry, for example, from clinical data, prescriptions, medical imaging, laboratories, pharmacies, insurances, and many more (Raghupathi, 2014). Healthcare data volumes have grown exponentially within the last years, and the growth is unlikely to slow down (Lv et al., 2020). The effective use of the data available can lead to benefits on an individual level, such as detecting diseases at earlier stages and on public health level: managing population health more quickly and efficiently (Raghupathi, 2014).

One application of big data in healthcare is artificial intelligence (AI), which has a broad scope of application. Contrary to common belief, AI will not replace the work of healthcare staff but rather facilitate and enhance their day-to-day work. The most common applications of AI in healthcare are to improve administrative workflows, image analysis, robotic surgery, virtual assistance, and clinical decision support (Marr, 2018). Moreover, there are even applications to find genetics-based solutions, opening many new opportunities to find genetically based treatments.

The digital transformation opens many new possibilities, ranging from technological advancements such as wearables to collect data to extensive data analysis, which structures the data for further applications. Finally, AI creates actual value for patients and public healthcare. These digital technologies are just a few more that can fundamentally change and improve healthcare from an individual and a public healthcare perspective.

Regardless of the many benefits, digital technologies bring along, there are also downsides to the digital transformation in healthcare (Lupton, 2014). One possible risk is data security and the mishandling of personal patient data. However, the data created and used within the healthcare ecosystem is precious to patients, public health institutions, and commercial companies (Mazanderani, 2013). Another downside to digital transformation is the lack of access for patients without access to the internet and other digital technologies (Lupton, 2014).

For instance, digital technologies have enabled the public healthcare response to the COVID-19 pandemic in countries globally. Accordingly, Brazil introduced several digital initiatives to overcome the COVID-19 pandemic and its effects. A coronavirus app, a chatbot, a WhatsApp helpline, and telemedicine are just some of the many solutions introduced by public healthcare institutions (Celuppi et al., 2021). Further, public healthcare institutions introduced initiatives to collect and access data more efficiently, which is fundamental for efficient application.

In conclusion, digital transformation in healthcare has vast potential and can help overcome some of the most challenging public and global healthcare issues. Nevertheless, it is crucial to enable these technologies to everyone as their effect is dependent on the number of users and data. Therefore, digital technologies will play an essential role in the healthcare industry and have to be adjusted to social and local circumstances to enable the best solution for everyone.


Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.

Bougouettaya, A., Sheng, Q. Z., Benatallah, B., Neiat, A. G., Mistry, S., Ghose, A., Nepal, S., & Yao, L. (2021). An Internet of Things Service Roadmap: A blueprint for leveraging the tremendous opportunities the IoT has to offer. Communications of the ACM, 64(9), 86–95.

Celuppi, I. C., Lima, G. D. S., Rossi, E., Wazlawick, R. S., & Dalmarco, E. M. (2021). An analysis of the development of digital health technologies to fight COVID-19 in Brazil and the world. Cadernos de Saúde Pública, 37.

Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: scientific research and commercially available devices. Healthcare informatics research, 23(1), 4-15.

Hermes, S., Riasanow, T., Clemons, E.K. et al. The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Bus Res 13, 1033–1069 (2020).

Lupton, D. (2014). Critical perspectives on digital health technologies. Sociology compass, 8(12), 1344-1359.

Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103-110.

Marr, B. (2018). How is AI used in healthcare-5 powerful real-world examples that show the latest advances. Forbes, July, 27.

Mazanderani, F., O’Neill, B., & Powell, J. (2013). “People power” or “pester power”? YouTube as a forum for the generation of evidence and patient advocacy. Patient Education and Counseling, 93(3), 420-425.

Raghupathi, W., Raghupathi, V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2, 3 (2014).

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Bianca Barreto
4 meses atrás

Excellent text, Patrick!

During the Covid-19 pandemic, the use of telemedicine was existential, as many people didn’t feel safe going to hospitals for routine consultations anymore. Even though the doctor-patient contact is essential, it is obvious that this technology has helped us get through this turbulent period.

Furthermore, the article written by Professor-Master Marcelo Dias Carvalho and Professor Rodrigo Aquino deals very clearly with how AI resources have been used in addition to telemedicine. One example is Remote Monitoring Assistance. To help reduce overcrowding and hospitalization costs. The Baptist Health association – a non-profit organization for healthcare in the US – has developed an online remote monitoring system for its patients, which monitors the evolution of the health condition of each person.

The system uses Machine Learning techniques to send alerts to the medical team if it identifies anomalies. In addition, it allows the patient to be better assisted at home or in hospitals at the same time. In this case, AI uses sensors attached to the human body and newly developed algorithms that recognize abnormalities within the body, which helps the medical team in better monitoring of patients. Further mechanisms are already being developed and deployed to monitor changes, for example, with diabetes and automatic drug injection, which will improve people’s quality of life.

We must use technology and innovation in healthcare to our advantage and and fully enable all benefits it brings along.

Deisy Ventura
4 meses atrás

Obrigada pelo post, Patrick. Ele trata de um tema muito importante. Creio, porém, que faltam dois elementos importantes para que tenhamos um post mais informativo sobre o assunto. O primeiro é uma visão crítica sobre o uso das tecnologias digitais na saúde. Quando escrevemos sobre algo, é recomendável citar ao menos uma boa referência de opinião contrária, obviamente se esta não for baseada em fake news nem preconizar a violação de direitos humanos e do Estado Democrático de Direito. Veja, por exemplo, a revisão de literatura crítica feita por Debora Lupton, Critical Perspectives on Digital Health Technologies, segundo elemento é o enorme impacto da pandemia de Covid-19 sobre o avanço deste tema, que não é referido pelo post. Neste ponto, sugiro Celuppi et al. An analysis of the development of digital health technologies to fight COVID-19 in Brazil and the world
Com estes elementos, poderemos ter um segundo post mais completo, certo?

Patrick Eugster
3 meses atrás
Reply to  Deisy Ventura

Obrigado pelo comentário. É claro que as opiniões críticas sobre o tema da digitalização são muito importantes. Portanto, eu completei o texto com fontes críticas e tentei dar uma imagem mais completa da digitalização no setor da saúde. Infelizmente, muitas tecnologias inovadoras não são disponibilizadas para toda a população, embora muitas vezes as camadas mais pobres da população possam se beneficiar mais com elas.

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