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.
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 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.)
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:
Additional Read: Why SaaS for Cybersecurity Data Analysis?
The entire healthcare industry has been undergoing a digital transformation, but that doesn't mean that systems magically work together. According to the Ponemon Research Report: The Economic Impact of Third-Party Risk Management in Healthcare, the average hospital has relationships with 1,300 vendors and the healthcare industry averages $23B annually alone in risk management.
On top of that expense, there is an entire industry of consultants and middleware that exist solely to help health systems achieve harmony across systems. Any Data Engineer knows, that data is only useful if it is organized in a way to deliver insights. Data silos exist across systems and must be combined in a useful manner, while still respecting patient privacy. A powerful, but simple data exploration tool such as Gigasheet can help engineers work with business users to write requirements for data marts and reporting cubes.
Gigasheet is a no-code platform designed to handle big datasets, built with security in mind.
Gigasheet offers virtually unlimited cloud data storage, so healthcare data analysts can wrestle with huge datasets without stressing about loading time, errors in opening the file, data not rendering, and file format compatibility.
Start analyzing big data without spending big bucks!