Big Data in Healthcare: All You Need To Know

Health systems swim in data yet struggle to turn it into action. When teams unify records across tools and formats, they make faster decisions and reduce risk. This enables more innovative staffing and patient-first workflows where teams deliver the fast, personalized care people expect. Big data in healthcare turns this challenge into an advantage. You can identify patterns, predict outcomes, and allocate resources effectively by unifying clinical, operational, and financial signals. In plain terms, it helps teams serve people better, cut waste, and make more intelligent daily choices. 

Let’s delve into what big data truly means, its evolution, and how it drives measurable impact across care delivery and finance. We’ll cover real use cases and practical steps to get started. You are in the right place if you want to modernize decisions and outcomes without adding chaos. Let’s move from theory to progress.

What is Big Data?

Teams analyze large and complex datasets, both structured and unstructured, to uncover deep insights and solve business problems that conventional analytics or software could not previously tackle. Managing data is a critical part of any organization; just ask our analytics agency.

Data scientists leverage artificial intelligence-powered analytics to evaluate these comprehensive datasets constructively, uncovering patterns and trends that can provide meaningful business insights.

Big data in healthcare refers to using prescriptive, predictive, and descriptive analytics services to gain deep insights from healthcare data. The endgame of big data in healthcare is threefold:

• Use patient data to improve clinical outcomes.
• Leverage operational data to boost workforce productivity.
• Utilize healthcare financial data to enhance the revenue stream for a practice, hospital, or healthcare organization.

Analysts expect big data to penetrate healthcare faster and more deeply than media, financial services, or manufacturing, which should come as no surprise, considering that healthcare is the largest private employer in the United States.

Analysts project rapid expansion in healthcare data and analytics: Reuters reports the AI-in-healthcare market rising from $14.92 billion in 2024 to $110 billion by 2030, while ONC shows 70 percent of U.S. hospitals engaged in all four interoperability exchanges in 2023, underscoring sustained demand for big data solutions.

A Brief History Of Big Data

Big data and data analytics have progressed significantly over the last few decades, thanks to the proliferation of the Internet and cloud computing capabilities. Our ability to store and make sense of information has gradually evolved, with many scholars dating it back to around 1800 BCE.

The Babylonians used a handy device called an abacus to perform simple to complex calculations as early as 2400 BCE, coincidentally, when the first libraries emerged. This marked humans’ first attempt at storing information on a large scale.

Fast-forward to 1663: scholars and mathematicians like John Graunt embrace statistics. Experts credit him as the pioneer of statistical data analysis and, perhaps, the father of modern big data. London officials used reports from statisticians like Graunt, who analyzed the Bills of Mortality, to monitor health trends and issue early warnings of bubonic plague outbreaks during the pandemics that ravaged Europe.

However, it was not until 1865 that Richard Millar Devens coined the term “business intelligence.” He entered the term into his Cyclopædia of Commercial and Business Anecdotes while trying to describe how banker Sir Henry Furnese gathered and analyzed relevant business information to gain an edge over his rivals.

In the early 1880s, a young scientist named Herman Hollerith at the U.S. Census Bureau invented the Tabulating Machine. It was a groundbreaking device that employed punch cards to process a large amount of census data, essentially reducing a decade’s work to a mere three months. This data analytics machine would form the foundation of what is now IBM.

Business analytics didn’t go mainstream until the heyday of the 1950s. Still, it took another decade before the US government erected the first data center, storing 175 million sets of fingerprints and 742 million tax returns on magnetic storage tape. Today, big data is no longer a buzzword; it’s a reality that healthcare CIOs need to adapt quickly. Otherwise, their organizations will be edged into oblivion.

“The Vs” of Big Data

Volume, velocity, and variety – aptly called the “Three Vs”, are the cornerstones of big data. In healthcare, these three are effective big data analytics’ defining dimensions or properties.

• Volume entails the remarkable amount of data healthcare generates through its apps, portals, websites, and EHRs.
• Velocity refers to the speed at which datasets are being generated and processed.
• Variety encompasses the different number of types of data we can now generate, gather and analyze.

 There are two additional Vs of big data:

• Value is the attribute that refers to the tangible worth of the data being generated, collected or analyzed.
• Veracity refers to the trustworthiness, integrity or quality of data generated, collected and analyzed by healthcare institutions.

How is Big Data Used?

Businesses across all industries utilize big data to deliver significant benefits. Healthcare businesses are leveraging big data and associated analytics in myriad ways. These applications that are driving change and transformation in healthcare and business environments include:

1. Product Development

Developing new drugs and other health products is a costly and time-consuming process. Big data has been gaining significant attention in healthcare and business product development, and for good reason:

• Product R&D teams typically struggle to make sense of the large amounts of data they have available. This is an area where big data can come to the rescue, zeroing in on the right data and thereby reducing the time involved in product development.
• Developing new products involves a lot of trial and error. Big data removes the hassle and guesswork from the equation, helping R&D deliver better and more precise products.
• Real-time data analytics help healthcare organizations refine their products based on large data sets.

2. Preventive Maintenance

Healthcare teams can use big data to predict failures and schedule maintenance for medical equipment and connected health devices. They also harden websites and apps as healthcare breaches surge. In essence, big data-informed preventive maintenance helps healthcare organizations reduce overall equipment maintenance costs.

3. Improve Patient Outcomes

Big data and analytic services make it easy for clinical practitioners and researchers to diagnose and treat diseases better.

By analyzing a vast amount of patient health data, doctors and clinicians can zero in on otherwise hard-to-diagnose and rare diseases like Parkinson’s disease. The advantage of using big data in healthcare is that it’ll significantly improve patient outcomes.

4. Operational Efficiency

Gathering and analyzing workforce data helps hospitals, pharmaceutical companies, and other healthcare organizations boost the productivity of their employees. It will help health organizations redesign their workflows, direct more resources where they are most needed, and enhance the overall operational efficiency.

5. Driving Innovation

Innovation is key in healthcare; it drives patient outcomes and drug discovery, and improves the quality of care. And there are many instances where big data has set the pace for innovation in healthcare:

• Pairing predictive data analytics with patient care.
• Diagnosing and preventing cardiovascular diseases like heart attacks.
• Creating tailored drugs and therapies for complex and rare diseases.

When all’s said and done, one of the most important benefits of using big data is reducing healthcare costs. For starters, big data can help healthcare organizations prevent fraud, data breaches, and other security problems.

Of more importance is that electronic health record systems, when coupled with big data in areas like cardiovascular health, can lead to cost savings of billions of dollars from reduced lab tests and doctors’ office visits. Interested in emerging technologies in the cardiovascular space? Check out our article on 9 Cardiovascular Health Technologies Doctors Should Know About.

The goal of efficiently using Big Data in healthcare is to understand current data sets, the problems a health organization is trying to fix, and to find innovative solutions that will help reduce operational costs. This mindset and approach will benefit healthcare players such as providers, manufacturers, insurers, and most importantly, recipients/patients.

Why is Big Data So Important in Healthcare?

There’s increasing excitement about the prospects of big data in healthcare, and investment in analytics is on the upward trend. Inadequate data governance leads to duplication of records, missing entitled reimbursements, difficulties in financial benchmarking, and other operational inefficiencies. Big data can fix that! Patient care is also more complex these days, and without proper analytics, it becomes increasingly difficult to provide quality and safe patient care that has much better outcomes.

Many healthcare organizations have seen discrepancies between clinical and accounting departments due to data mismatches and inaccuracies.

Why Use Big Data in Healthcare?

Big data in healthcare turns scattered signals into practical insight across the care journey. With modern interoperability and predictive analytics, we can flag high-risk patients earlier, prevent disease, lower operating costs, reduce human error, and accelerate innovation in treatments and workflows. 

Federal initiatives, from the CDC’s Public Health Data Strategy to AHRQ’s readmission toolkits, demonstrate how connected data reduces the time to action and improves outcomes nationwide. Below, we break down five proven ways to apply analytics where it matters most. Each delivers measurable value for patients, clinicians, and payers.

1. Provide High-Risk Patient Care

Healthcare extensively utilizes big data to identify and manage both high-risk and high-cost patients. Payers are leveraging the power of predictive big data analytics to identify high-cost patients. More specifically, they are looking at the patient’s gender, age, prescription drug usage, and spending history as predictors of whether an individual should be considered a high-cost or not.

Big data is also used to identify high-risk areas where patients can be provided with more efficient healthcare to reduce spending and increase patient satisfaction.

By helping payers and healthcare providers identify high-risk and expensive patients, big data and analytic tools can provide these individuals with adequate intervention and reduce expenses, such as preventive care, well ahead of time.

Take Dayton Children’s Hospital in Ohio, for instance. It’s using big data to comb through and analyze data from Google products to target potential patients. This data-driven approach helps the hospital identify potential patients at risk of lifestyle conditions like diabetes, depression, high blood pressure, and cardiovascular disease.

With the proliferation of EHR systems, telemedicine, and other healthcare technologies, initiatives like Dayton Children’s Hospital will continue to take center stage. Of course, some work on big data analytics has already begun, but much more needs to be done to gain efficiency and reduce costs.

2. Tracks and Prevents Care

The cost of healthcare delivery in the US is more than $4.9 trillion annually. This is where big data, combined with other health technologies, can help track and identify diseases long before they happen – therefore boosting preventive care.

Every executive should be aware that the use of big data in healthcare begins even before a patient visits a doctor’s office.

This is a crucial area where health-tech companies like Fitbit come into play. Through data gathered from wearables, such as activity, sleep, blood pressure, and more, healthcare providers can get a more complete picture of patients’ health and devise preventive care plans that result in much better patient outcomes.

Payers can start offering discounts, reduced rates, and even other enticements to members who are at risk of a heart condition. Fitbit tracks over 173 billion Active Zone Minutes of exercise activities, 5.4 billion nights of sleep, and 85 trillion steps clocked. Providers can use this large amount of health data collected by wearables and other devices to provide better patient insights and guidance.

3. Reduces Costs for Healthcare Providers

Predictive data analytics can greatly reduce healthcare expenses and minimize financial waste.

More than 57 percent of healthcare executives believe that predictive data analytics will save healthcare organizations a quarter or more in costs annually over the next half-decade or so. 

With the vast information and insights that healthcare data analytics offers, healthcare executives and providers are in a position to make better financial and operational decisions while providing an enriched quality of patient care. 

There are several different ways healthcare data analytics can help cut costs for providers and practices. One great example is optimizing staff allocation by predicting patient bookings and minimizing financial waste. This will help providers avoid underbooking or overbooking staff at times of greater or lesser demand, translating to more cost savings.

Another example where big data in healthcare can help large health organizations includes the overall cost reduction for patient care.

The Mayo Clinic is currently using predictive data analytics to zero in on patients with two or more chronic conditions. These patients are highly likely to benefit from preventive and early intervention care right at home. In this way, big data analytics saves both the Mayo Clinic and the patients by helping them avoid visits to the emergency department. It’s a win-win situation.

Less clinical guesswork = more healthcare savings.

Thanks to deep clinical insights derived from data and predictive analytics, providers can make more accurate clinical decisions and prescribe treatments with greater precision.

When big data is used correctly, there’s no room for guesswork when it comes to diagnosis and treatment, an excellent combination for enhancing the quality of patient care and lowering costs.

Big data also has the potential to reduce costs for payers.

By taking advantage of predictive analysis based on data from wearables, insurers can help get better, faster and, consequently, leave their hospital beds faster. Moreover, big data insights can help reduce bed shortages and staffing needs.

4. Prevents Human Errors in Healthcare Services

A study found that medical fraud and abuse account for three to 15 percent of healthcare expenditures worldwide from 2000 to 2024, affecting care delivery and cost-efficiency. As if that isn’t bad enough, there is also the fact that errors in prescription dosage can result in overdosing, risking a patient’s health and overall well-being.

Healthcare accounting errors put an additional financial burden on the healthcare institution, as reconciliation with insurers, payments, etc., needs to be redone, which becomes time-consuming and expensive.

When companies leverage big data and predictive analytics in the healthcare industry, fraud and errors can easily be detected and prevented, saving healthcare organizations tremendous money. Already, several big data and analytics solutions help providers prevent such frauds and human errors, especially when it comes to dosage.

One great example is MedAware, an Israeli medtech company co-founded in 2012 by Dr. Gidi Stein, a professor of medicine and molecular imaging at Tel Aviv University. The big data-powered software solution integrates seamlessly with EHR systems operated by most hospitals, detecting prescription errors before they occur. The platform draws prescription patterns in hundreds of thousands, if not millions, of EHR records to alert to medication-order outliers.

Phoenix Children’s Hospital has also implemented a dosage range checking (DRC) platform that analyzes huge patient data sets to prevent overdosing or underdosing. PCH’s DRC system is designed to generate soft/hard stop alert warnings for prescribers on dosage issues before they write the orders.

The DRC system has delivered considerable benefits because there has been no reported case of overdosing since it was implemented back in 2011. Even more interesting is that the DRC system allowed the review of over 1 million patient records, helping delist a popular prescription analgesic from the market.

5. Innovates Healthcare Solutions

Big data, predictive analytics, and a host of other technologies, such as AI, machine learning, and telemedicine, are the new frontiers in medicine. Big data analytics, in particular, helps researchers and clinicians discover innovative healthcare solutions to boost the quality of treatment and patient care.

Here are a few areas where groundbreaking healthcare solutions are turning heads:

• Finding solutions to streamline operations across departments and locations;
• Managing a large volume of patient data to identify trends that will influence positive patient outcomes;
• Refining drugs and therapies for patients suffering from chronic illnesses.

One innovative solution driven by big data is a wearable sensor tech device that Philips created in collaboration with Radboud University Nijmegen Medical Center in the Netherlands and SalesForce.

The innovative device is designed to help patients with chronic obstructive pulmonary disease improve their lifestyles and boost their treatments.

Big data innovation in cancer treatment: The National Center for Tumor Diseases (NCT) in Heidelberg, Germany, has leveraged big data to identify tumor markers from doctors’ notes, creating a unique tumor registry. 

CancerLinQ, a nifty tool developed by the American Society for Clinical Oncology, brings together cancer data from over 1 million patients across 100 clinics. By using big data analytics, oncologists can develop high-accuracy treatments.

Another classic use of big data to innovate is at Mercy, a US healthcare provider with over 40,000 employees, including 700 physicians. Mercy’s big data analytics platform allows the organization to boost operational efficiency and achieve breakthrough patient outcomes.

Seoul National University Bundang Hospital is also trailblazing the way for the rest when it embraces big data. Thanks to its big data platform and paperless approach, quarterly analysis, which usually takes around two months, is now a two-second affair.

How Can Health Organizations Deploy Big Data?

Here are three crucial ways big data can be properly implemented in healthcare sector:

1. Data driven mindset – Training all institution staff and patient care personnel on how to accurately record data, store and share it.
2. Proper collection and storage mechanism – Using proven processes and mechanisms to collect, store and access data.
3. Smart algorithms – Building smart algorithms that will consume the large volume of data, properly analyze it and produce relevant results, which will be used in predicting the right outcomes for patient care.

Who Benefits From the Use of Big Data in Healthcare?

Big data and predictive analytics stand to benefit nearly all aspects of healthcare. Here are the biggest winners:

• Providers (Clinics, Hospitals): The insights generated by big data analytics will help healthcare providers deliver better patient outcomes, reduce wastage, and enjoy efficient workflows and processes.
• Payers (Insurance) – Executing data analytics at a large scale can benefit payers in a number of ways, including the elimination of fraud, reduction of false and improper claims, faster reconciliation, better service.
• Patients – Patients are the ultimate winners in a data-driven healthcare environment. They’ll reap countless benefits such as superior health management, predictive care, healthier lives, savings in insurance and overall healthcare.
• Device Manufacturers – Data analytics helps manufacturers create better, more innovative products to solve health issues and build devices relevant to patients’ needs.
• Pharma – Better R&D, more effective drugs, savings on manufacturing, and innovative drugs. Interested in learning more about Big Pharma and predictive analytics?

Check out our Artificial Intelligence & Pharma Industry article: What’s Next.

Leverage Big Data in Healthcare With Digital Authority Partners

Big data has the potential to revolutionize healthcare from the top down. Healthcare organizations should bet big on big data to improve patient outcomes, save costs, and build efficiency across all departments.

Big data will help clinicians and hospitals provide more targeted healthcare and see better results. For pharmaceutical companies, big data is a driving force that will help them design and build more innovative drugs and products.

Healthcare stakeholders can rely on big data and predictive analytics to tackle major issues such as readmission rates, high-risk patient care, staffing issues, dosage errors, and more.

Prove the value of big data with ZERO upfront costs. For a limited time, Digital Authority Partners is offering healthcare organizations with 500+ employees a FREE big data assessment and proof of concept.

Do you need guidance with your digital transformation initiatives? Digital Authority Partners has worked with companies like Athenahealth, Omron Healthcare and Blue Cross Blue Shield on cutting-edge digital initiatives that improve patient outcomes and quality of care. Contact Digital Authority Partners at [email protected] or 312-820-9893.