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  • Apoorva Pachori

3 Big Challenges with Big Data in Healthcare

In 2012, data miner Atul Butte hinted at big data’s potential in healthcare by saying,

Hiding within those mounds of data is the knowledge that could change the life of a patient or change the world.

10 years later, this quote has aged like fine wine. Big data has truly transformed the healthcare industry. We believe it will continue to do so.

3 Big Challenges with Big Data in Healthcare

How Big Data is Making Big Waves in Healthcare

If you are like most millennials, you’re probably relieved to know that your next doctor’s appointment is just a few clicks away. A nifty mobile app does the job, and you don’t need to gear up for a phone call. (Phew!)

But digital healthcare is more than just a modern-day privilege.

The rapid digitization of healthcare has provided sophisticated ways of collecting colossal amounts of data. And the COVID-19 pandemic further accelerated e-healthcare. American citizens have rapidly adopted telehealth services like online consultations, virtual doctor visits, and telemedicine.

The exponential rise of e-healthcare has prompted businesses to look into consumer, patient, doctor, and clinical data.

With this wealth of information, they can create comprehensive profiles of consumers, patients, and physicians. As a result, they can offer personalized healthcare plans and predict patterns in patient health outcomes. Businesses can also leverage insights to refine their marketing campaigns, reduce costs, and make strategic business decisions.

While this sounds like a big win for all parties involved, some challenges remain.

The 3 Big Challenges to Big Data in Healthcare

#1 Mo’ data, mo’ problems

The first challenge to using big data is that, well, it is notoriously B.I.G.

Digital health systems, data analysts, and business owners are always trying to improve ways to analyze large data files and unlock their full potential.

And the amount of healthcare data in the world won’t stop growing anytime soon.

Big data is high-variety and high-velocity, which complicates things. To appreciate this, have a look at the definition proposed by the Health Directorate of the Directorate-General for Research and Innovation of the European Commission:

Big Data in health encompasses high volume, high diversity of biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.

This means data analysts have to adapt to rapidly changing datasets. New types of data are getting appended to existing databases every single day, making the big data puzzle even more perplexing. (The missing piece? Tools like Gigasheet that help you analyze endless rows of data in a jiffy.)

#2 Data privacy and security are a BIG deal

Over the years, we witnessed countless incidents of data breaches, hacker attacks, cybercrime, and malware infiltration. Citizens are wary about sharing their personal data, and companies are striving to protect their customer data at all costs.

The healthcare industry, in particular, has been a frequent target of cybercriminals. As reported to the Department of Health and Human Services Office for Civil Rights:

  • A whopping 4,419 healthcare data breaches occurred between 2009 and 2021. These incidents have resulted in the theft, impermissible disclosure, and exposure of 314,063,186 healthcare records. This means that more than 94.63% of the 2021 population of the United States has been affected by data breaches.

  • In 2018, healthcare data breaches of 500 or more records occurred at a rate of 1 per day. Four years later, this rate almost doubled to 1.95 a day.

Additional Read: Why SaaS for Cybersecurity Data Analysis?

#3 Interoperability Remains a Challenge

The entire healthcare industry has been undergoing a digital transformatio