The Big Data Revolution in Healthcare

Simon Waslander, Director of Collaboration, Clinical Research, CureDAO

The healthcare sector has seen a massive explosion in the amount of data, healthcare applications, and algorithms. In this introduction article we will discuss how the Big Data revolution can benefit the healthcare sector.

The global healthcare sector is facing various challenges, fueled by the worldwide demographic trend of aging populations. In 2050, over 2.1 billion people will be over the age of 60, and by 2100, this number will rise to over 3.1 billion.

These trends are putting enormous strain on global healthcare systems, especially in socialized universal coverage systems such as those seen in Europe. OECD data show that healthcare spending has been rising faster than economic growth as measured by GDP for decades.

This trend of ever-rising costs and an aging demographic tsunami can be seen as an existential threat to universal healthcare systems.

The field of "Big Data" is one of the most exciting prospects for global healthcare stakeholders to implement, therefore there is light at the end of the tunnel in the shape of technical advancement.

The Big Data Revolution #1: What is Big Data?

The global healthcare sector produces truly staggering amounts of data. In 2013, the global healthcare sector produced 153 exabytes of data; by 2020 this number would have soared to over 2.314 exabytes.

Where is all the health data coming from?

This data can come from all stakeholders in the healthcare industry and from the patients themselves, including smartphones and wearable sensors all the way to detailed patient records and pharmaceutical company research, among many other sources.

The sheer volume of these data sets is mind-boggling, to say the least. But what is "Big Data"?

Various academic authors (ref 1) have sought to define big data according to their attributes within the industry, better known as the 7 V’s:

• Quantity (which refers to the amount of data)
• Rapidity (the speed with which new data is generated) Variety of (heterogeneity of data; many different types of healthcare data)
• Flexibility (inconsistency of data) Veracity (the reliability and calibre of the data); Visualization (ability to interpret data and resulting insights)
• Value (the goal of big data analytics is to find hidden knowledge in massive amounts of data).

Overall, Big Data cannot be analyzed through standard methods; for the analysis and value extraction of such data sets, technically advanced software applications that can utilize fast and cost-efficient high-end computational power are often needed. Also, tools such as deep learning and artificial intelligence can play a crucial facilitative role in this regard. (Ref 2)

The Potential Benefits of Big Data for Healthcare:

For good reason, there has been an astounding explosion in academic research on big data. (ref 2)

But what are the potential advantages for the entire healthcare ecosystem arising from big data? A scientific review article by Dash et al. 2019 (ref 2) provides a thorough overview, identifying four key areas where big data will add the most value for healthcare sector stakeholders.

1. Improving the quality of healthcare services
2. Supporting the work of medical personnel
3. Supporting scientific and research activity.
4. Business and management

Improving the quality of healthcare services.

o Medical professionals can receive access to clinical decision support tools that use this massive amount of data to help them make better treatment choices.
o Data analysis can elucidate more cost-effective personalized treatment modalities.
o Predicting disease occurrence both individually and within large cohorts

Supporting the work of medical personnel.

o use of the "Internet of Things" (IoT) and other sensor technology for live monitoring of a patient
o Risk stratification involves identifying patients most at risk for complications or disease.
o Health management on a societal level
o Personalized medicine, which also uses more advanced IoT sensors and novel “omics” and aging-based clocks, can usher in a period of extremely personalized medicine.
o Predictive analytics involves being able to forecast a potential health event before it occurs.

Supporting scientific and research activity

o Supporting work and research in the creation of novel pharmaceutical agents.
o Personalized clinical trials
o Being able to predict the severity of a novel agent's side effects before it is administered to a person
o Use Predictive Analytics for better drug design.

Business and management.

- Creating overviews and monitoring dashboards for managers
- Detection of organizational inefficiencies.
- Predictive analytics for an organization such as future workflows, occupancy rates, and use of certain pharmaceuticals

The Role of Incentivizing Data Sharing:

At the moment massive amounts of personal health data are being created and stored. This includes data from classic sources such as medical health records, pharmacy documents and clinical trial data. But a huge amount of data is also being created from novel applications such as trackers of diet, exercise, sleep and many more factors.

Sadly, this massive aggregate amount of data is being stored in individual highly private siloes.

To truly revolutionize the healthcare sector, data from various different sources including:

- Pharmaceutical companies
- Individuals
- Web Development/App companies
- Disease Advocacy Groups
- Digital Health Businesses
- Governments

Should be anonymized and aggregated in a global digital healthcare data marketplace. In this respect blockchain technology and unique NFT creation are key enablers of this possibility to create a global open marketplace for medical data.

The advantages for the healthcare sector in aggregate but especially per user are massive indeed. Here we name a few benefits for key stakeholders:

- Pharmaceutical companies can reduce their development costs and clinical trial expenses. Also novel off-label uses for existing pharmaceuticals can be discovered at little to no extra development cost.
- Individuals will gain royalties for the data they share, creating tradable value for their data.
- Web developers can reduce their development costs for various applications, making contributing development companies more innovative and competitive.
- Governments can see reductions in aggregate societal-level healthcare costs through novel discoveries facilitated by this data sharing marketplace.

Conclusion and Future Directions:

With global healthcare systems under increasing strain, novel, cost-effective solutions are in high demand. The revolution in medical big data and all the innovation surrounding it present large, relatively low-cost solutions that can be implemented by healthcare organizations and professionals.

In the future, for this innovation to become truly radical, data owners such as hospitals, pharmaceutical companies, and individual patients should cooperate to share their data in an open fashion. Combining various siloed datasets holds unimaginable promise to further accelerate the Big Data revolution.


1. Batko, K., Ślęzak, A. The use of Big Data Analytics in healthcare. J Big Data 9, 3 (2022).
2. Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis, and future prospects. J Big Data 6, 54 (2019).

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Author Bio

Simon Waslander

Simon Waslander is currently part of CureDAO as the Director of Collaboration. Simon specializes in creating multiplicative synergies with a network of academic, private, and government contacts.  He has a BSc. in Medicine from the University of Groningen and a MSc. Healthcare Innovation from Maastricht University. He has a deep and keen interest in the biomedical aspects of human longevity. In his professional career, Simon has had tenures at BioViva, Ageless Partner, and AGI Laboratory. He is also an Expert Coach for start-ups in North Netherlands via the VentureLab North start-up community.

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