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

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Public healthcare spending is a major issue that can be encountered all over the world and is often caused by demographic and social changes (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 aforementioned technologies enable streamlining physicians’ work, optimizing systems, improving patients outcomes, reducing human error, and lowering costs in general (Hermes et al., 2020). To give a basic overview, the technologies of digital transformation in healthcare are divided into the Internet of Things (IoT), big data, and artificial intelligence (AI).

The internet of things is defined as “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). This technology is strongly supported by the trend of wearable medical devices. 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 better track the well-being of individuals (Haghi et al., 2017). Additionally, patients are enabled 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 extremely 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 been growing exponentially within the last years and the growth is not expected 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 in itself has a wide scope of application. Contrary to common belief, AI will not replace the work of healthcare staff, but rather facilitate and enhance their 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, which open up many new opportunities to find genetic based treatments.

The digital transformation opens up many new possibilities which range from technological advancements such as wearables to collect data, to big data analysis which structures the data for further applications, and finally to AI which creates actual value for patients and public healthcare. These digital technologies are just a few of many more, which can fundamentally change and improve healthcare from an individual perspective as well as from a public healthcare perspective. Nevertheless, there exist several barriers to digital technology adoption in healthcare, which range from regulatory to individual adoption barriers. In order to overcome these barriers, the right incentives have to be in place to enable fast and easy digital adoption, which in turn will help solve several problems the healthcare industry and public healthcare are currently facing.


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.

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).

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.

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

World Health Organization (WHO), Global Health Expenditure Database (2019).

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Bianca Barreto
9 dias 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
9 dias 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?

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