Applications and Examples of Big Data in Healthcare

How Big Data Improves Efficiency, Costs, and Patient Outcomes

March 11, 2021
Two scientists examine models of DNA on computer screens.
One of the most current and relevant big data examples in healthcare is how big data analytics supported the rapid development of COVID-19 vaccines.

The amount of information — or data — healthcare organizations collect, manage and analyze has increased rapidly with advancements and integrations in technology. Technology, in turn, is changing the way the healthcare sector uses data. Advanced tools and software have been essential to the unprecedented growth of big data, making healthcare information easier and cheaper to store, access and use.

What is big data?

At one time, big data referred simply to large amounts of data. Since then, big data has evolved to become more broadly defined as clusters of information — data sets — too diverse, complex or massive to be handled efficiently by traditional data-processing application software. What is designated as big data can vary based on the tools and capabilities of people and organizations using it.

As a field of study, big data explores how large data sets can be systematically managed and analyzed to extract or infer useful insights from them. Originally associated with three key qualities, big data continues to change and grow. Industry experts have widely adopted the 5 Vs to describe its characteristics:

  • Volume is the amount or quantity of data. Technology has made it possible for unprecedented volumes of data to flow to and from devices, applications and networks.
  • Variety is the different forms or types of data and their sources. Unstructured, semi-structured or structured data can include everything from numbers, facts and statistics to text, photos and videos.
  • Velocity is the measure of how fast data is flowing — in other words, its speed. The velocity of data directly impacts organizations and their ability to make timely and accurate business decisions. With the Internet of Things (IoT) and other connected devices, machine learning and cloud computing, data flows in real time, so information can be available in an instant.
  • Veracity is the inconsistencies and uncertainty of data. With data coming in different forms from different sources, the quality and accuracy of it has to be controlled to draw reliable conclusions from it.
  • Value is how useful the data is and what’s done with data to make it worth something. Value demonstrates data’s return on investment.

Today, big data encompasses mathematical and statistical methods in data analytics. These include fields of predictive analytics, user behavior analytics or other advanced data analytics that uncover relationships and predict outcomes in large sets of data. Big data sets come from a variety of fields: banking and finance, business, media and communications, sports and entertainment and healthcare, to name just a few.

Healthcare analytics

Big data analytics for healthcare uses health-related information of an individual or community to understand a patient, organization or community. In the past, managing and analyzing healthcare data was tedious and expensive. More recently, technology has helped the healthcare sector make leaps and bounds to keep up with the flow of big data in healthcare.

Diagnostic devices, medical machinery, instrumentation, online services — sources such as these are transferring data throughout a healthcare network. This is done with the help of big data tools such as Hadoop and Spark.

Big data examples in healthcare

With a variety of data analytics tools and methods, healthcare analysts use big data to inform health prevention, intervention and management. Efforts such as these can help enhance the patient experience, improve efficiency and quality of care and lower healthcare costs. Big data analytics for healthcare makes it possible to get a more complete picture of something to make smarter decisions.

One of the most current and relevant big data examples in healthcare is how it has impacted the global coronavirus crisis. Big data analytics for healthcare supported the rapid development of COVID-19 vaccines. Researchers can share data with each other to develop advanced medications very quickly. Big data in healthcare also predicted the spread of disease by allowing healthcare information to be processed much more rapidly than in the past during other pandemics.

Big data in healthcare can benefit patients and providers alike in many different ways. Here are just a few other big data examples in healthcare:

  • Patient outcomes. Big data can be used in healthcare to identify individual and community trends and develop better treatment plans or predict at-risk patients.
  • Staffing and operations. Healthcare analytics can use big data to forecast patient admissions trends at specific times of the day and schedule the right number of staff during peak or slow periods.
  • Product development. Big data in healthcare can help drive innovation and reduce the time it takes to bring a new product, such as prescription meds, to market.
  • Strategic planning. Healthcare analytics can help compare chronic disease and population growth in neighborhoods to identify problem areas and plan additional services.
  • Crime prevention. Healthcare analytics has helped streamline insurance claims processes, so providers can detect fraud more easily and patients can receive payments faster.

Challenges of big data in healthcare

As a relatively new field, big data in healthcare is still evolving to keep up with the fast pace and changing nature of technology. With such vast amounts of data available to work with, organizations and leaders can struggle with knowing where and how to start with data analytics in healthcare to find the information that is meaningful.

Making use of all of this data raises concerns of healthcare cybersecurity and information privacy. The issue of governance — who owns and is responsible for overseeing the planning, implementation and management of big data — is also a common concern among healthcare organizations.

Many healthcare organizations lack adequate systems and databases — and the skilled professionals to handle them. As such, the demand for healthcare analysts with advanced education and training is very high in the United States.

Build your career working with big data in healthcare

Among big data degrees, programs and schools, Touro stands out with its online master’s degree in healthcare data analytics. The program curriculum leverages Touro’s strength and reputation in technology and healthcare education while developing your technical skills and providing hands-on experience.

For example, the Intro to Data Analytics course uses real-world big data examples in healthcare and case studies to provide context of how big data fits into the healthcare structure. You’ll learn a variety of data analytics methods used to manage healthcare data such as clustering, deep learning, linear regression, logistic regression and optimization. This foundational course will also introduce you to industry best practices and tools, including the R programming language and Sisense. Additional courses include:

  • Enterprise Electronic Health Records
  • Healthcare Governance
  • Health Data Visualization
  • Compliance and Ethics

Our healthcare analytics degree offers both a graduate degree and advanced training to develop the skills and knowledge you need for healthcare analytics jobs. This 10-course, 30-credit master’s degree program can be completed in as little as one year and prepares you for professional certification in data analytics.

Explore Touro’s online master’s program in healthcare data analytics and find out how you can kickstart, change or advance your tech career.