the many ways to use big data in healthcare

Key Takeaways

  • 70% of organizations prioritize automation, increasingly relying on AI and big data in healthcare to assist in diagnosis, resource planning, and fraud detection.
  • The healthcare technology market is projected to grow to $1.25 trillion by 2029, with a CAGR of 20.6%, reflecting strong demand for big data-driven solutions.
  • 73% of organizations feel limited by their current data capabilities, which shows an urgent need for scalable analytics tools.
  • 65% of healthcare teams use three to four instruments for machine learning across data workflows, highlighting the complexity of modern data environments.

Healthcare generates an enormous amount of data every day. From smart surgical tools and connected monitoring devices to EHR systems and telehealth platforms, big data in healthcare constantly feeds clinicians with streams of information that weren’t even accessible a decade ago.

With all this data coming in, analytics plays a key role in helping medical professionals make sense of it. That’s exactly where big data comes in. Whether it’s patient scans, lab results, clinical notes, or insights from recent studies, they need clear, evidence-based input to guide decisions.

With 15+ years of developing highly technological healthcare software, Acropolium is recognized as a top big data analytics software. Today, we want to showcase the most relevant applications of big data analytics for healthcare that reimagine fundamental medical operations.

What Is Big Data in Healthcare?

big data and healthcare technology market report

Big data in healthcare makes it easier to work through large volumes of healthcare information quickly and more accurately. It gives medical teams the tools to improve how they treat patients, spot potential risks earlier, and tailor care to individual needs, especially in managing chronic conditions.

Ultimately, it supports better, more informed decisions that can improve long-term health outcomes. As 73% of organizations still feel limited by their current data capacity, they apply trending healthcare technology to be able to process the growing volumes.

As for tech-savvy medical teams, they use multiple tools across the data workflow. Around 46% use two tools for data management, while 65% rely on three to four for machine learning.

While managing data today is rarely a one-tool job, the impact of big data in healthcare is huge. Thus, the healthcare technology market is expanding fast — it’s projected to grow from $592.49 billion in 2025 to $1.25 trillion by 2029, with a CAGR of 20.6%.

Advantages of Big Data in Healthcare

advantages of big data analytics in healthcare industry

Big data is changing how medical systems function, bringing benefits of big data in healthcare that translate to higher performance of hospital management and accuracy in internal processes.

With the right infrastructure that provides data-driven insights, organizations can analyze large volumes of structured and unstructured data to make decisions faster. Here’s the advantages that you can unleash both as a business and a medical provider.

Improved Patient Outcomes

Big data in healthcare allows clinicians to spot trends, track responses to treatment, and intervene earlier. With a clearer view of each patient’s history and live health data, care becomes more personalized and hence more effective.

Operational Efficiency

Hospitals and clinics can streamline workflows by analyzing patterns across departments. It helps reduce delays, improve staff allocation, and keep systems running more smoothly even during peak demand.

Reduced Operational Costs

Better data leads to smarter resource use. Whether it’s avoiding duplicate tests or predicting supply needs more accurately, analytics helps cut waste and reduce unnecessary expenses.

This is especially beneficial for those running on cloud-based infrastructure, as you’ll get more resources to invest in its maintenance.

Faster Diagnosis and Treatment Cycles

By integrating data from multiple sources like imaging, labs, wearable devices, big data analytics for healthcare can support faster, more accurate diagnoses. Clinicians spend less time searching and more time treating.

Support for Medical Research and Innovation

Large datasets are critical in driving new discoveries. Researchers can analyze information across patient populations to test hypotheses, track disease progression, or accelerate drug development.

Population Health Management

Big data analytics in the healthcare industry gives a much broader view of public health trends. By collecting and processing information based on a region or demographic, SaaS-based health systems can identify at-risk groups, plan preventative strategies, and better allocate resources.

Risk and Fraud Detection

Patterns in data can reveal irregularities — from unusual billing practices to potentially unsafe treatment plans. With automated alerts and smart monitoring, systems can respond more quickly to problems.

Enhanced Clinical Decision-Making

Access to data-based insights at the point of care gives clinicians a stronger foundation for every decision. Big data recommends treatments, adjusts care plans, and helps reduce uncertainty and improve precision. At the same time, the analyzed patterns can soon be used for automating common tasks with AI and ML algorithms, with 70% prioritizing automation.

Use Cases of Big Data in Healthcare

big data applications in healthcare for 2025

About 33% of medical companies are directing their tech investments toward the biggest service pain points identified by both patients and staff. Using feedback to guide where innovation can make the most immediate impact is a focused approach that gives life to a variety of big data use cases in healthcare.

Now, let’s see how businesses use big data to drive medicine forward.

Predictive Healthcare for Specific Conditions

By analyzing medical histories, lab results, and lifestyle data, healthcare providers can identify patients at higher risk for heart disease, diabetes, or cancer. Mount Sinai Health System, for example, uses these capabilities to proactively manage chronic disease and flag potential health issues before they escalate.

Personalized Treatment Plans

Personalized treatment is another major breakthrough. AI-driven tools, trained on genomic, behavioral, and clinical data, are helping clinicians tailor therapies to individual patients.

IBM Watson for Oncology has been used to recommend cancer treatment plans based on each patient’s unique profile, supporting more targeted and effective care.

Hospital Operations Optimization

Speaking of operational examples of big data in healthcare, hospitals are using big data to run more efficiently. Real-time insights help manage staffing, patient flow, and bed occupancy, reducing bottlenecks and easing pressure on emergency departments. Johns Hopkins Hospital, through its data-driven command center, has significantly reduced ER wait times and improved patient throughput.

Precision Medicine & Advanced Research

In advanced research, big data helps uncover how diseases progress on a molecular level, enabling therapies built around patient subgroups rather than generic protocols. Companies like Tempus use clinical and genomic data to guide oncologists toward highly targeted interventions, driving better outcomes in cancer care.

Remote Monitoring & Real-Time Care

Remote monitoring technologies also uncover big data benefits in healthcare. Wearables and connected devices constantly stream health information, allowing clinicians to detect signs of decline early and intervene before conditions worsen.

Kaiser Permanente has successfully integrated remote monitoring into chronic disease care, improving both patient safety and outcomes.

Fraud Detection and Risk Management

Using big data in healthcare also aids in fraud detection and risk management. Patterns in billing records and insurance claims often reveal inconsistencies or suspicious activity that would be hard to spot manually. These insights help organizations flag potentially fraudulent behavior early, cut down on unnecessary procedures, and tighten overall compliance.

UnitedHealth Group has applied these capabilities at scale, using predictive modeling to reduce financial waste and improve the accuracy of claim processing.

Drug Discovery and Development

In research and development, companies like Pfizer leverage big data and machine learning modules to accelerate drug discovery. By mining data from clinical trials and molecular studies, they can identify promising candidates faster and anticipate outcomes more accurately — shortening the path from lab to market.

Population Health Management

At the population level, systems like Northwell Health use big data to monitor public health trends, manage chronic disease across large groups, and design targeted interventions that address specific community needs. It’s one of the big data examples in healthcare, giving a shift toward proactive, data-informed care that scales well across regions.

Challenges and Solutions of Big Data in Healthcare

big data healthcare use cases and challenges

With the great impact of big data analytics on healthcare comes a set of challenges in its implementation. Since medical software are complex systems that operate with advanced modules, they require robust maintenance, high availability, and airtight security protocols:

  • Data privacy and compliance. Regulations like HIPAA and GDPR set strict rules around how patient information is handled, stored, and shared. Healthcare businesses must make sure that their data infrastructure, including analytics platforms and third-party tools, meets these standards.

The solution here lies in a combination of end-to-end encryption, access control, audit trails, and regular cybersecurity audits.

  • Data standardization and interoperability. Medical data often lives in siloed systems, stored in different formats across EHRs, labs, imaging platforms, and insurance databases. Without a shared framework, integrating this data for meaningful analysis becomes slow and error-prone, limiting the value of even the most advanced analytics tools.

To fully unleash the advantages of big data in healthcare, it’s worth adopting common standards like HL7 FHIR, along with integration layers or middleware solutions. This way, you can bridge legacy systems and create a more unified data environment.

  • Storage and real-time demand processing. Managing data from high-resolution imaging and continuous remote monitoring feeds at scale requires robust infrastructure and low-latency systems that can handle heavy loads without slowing down care delivery.

Moving to cloud-native architectures with scalable storage, high-performance computing, and stream processing frameworks (such as Apache Kafka or Spark) helps healthcare organizations handle this influx of data without compromising performance or availability.

Of course, the best way to make your journey toward embracing big data in healthcare smoothly is to address your needs to a verified tech developer.

Why Choose Acropolium?

Recognized as a top healthcare app provider globally, Acropolium brings deep technical expertise and domain insight to help medical providers open up the full potential of big data.

With a strong track record in building secure, compliant, and scalable on-demand systems, we’re equipped to address the core challenges of the industry.

We don’t just build bespoke software and mobile apps for healthcare. We deliver data-driven solutions that make clinical, operational, and financial impact. And here’s what one of our partners has achieved after cooperating with our dedicated team.

AI-based Resource Planning Software

A hospital group in Belgium struggled to keep pace with its own rapid growth. As patient volume surged, the cracks started to show: overwhelmed staff, underused equipment, and growing inefficiencies in day-to-day operations. They turned to Acropolium to implement big data in healthcare operations and make their business more agile.

Solution

We developed AI-powered predictive analytics software that uses historical and live data to forecast patient demand, automate scheduling, and optimize resource allocation.

  • Our team worked hands-on with hospital administrators, clinicians, and IT staff to map bottlenecks and inefficiencies.
  • From there, we designed a system that seamlessly integrated with EHRs, hospital management tools, and IoT-connected devices.
  • The solution is built with AI and machine learning to process real-time and historical data, offering predictive insights for patient flow, automating shift planning, and optimizing resource use, from ICU beds to surgical equipment.
  • It also maintains full HIPAA and GDPR compliance with strong encryption and role-based access.

Results

  • 25% improvement in operational efficiency
  • 30% reduction in patient wait times
  • 15% boost in staff satisfaction

Final Thoughts

The rise of big data in healthcare marks a new chapter of precision, personalization, and proactive care. But unlocking its full potential means addressing the underlying infrastructure, integration, and compliance demands with the same level of precision.

This is where Acropolium steps in, not just as a vendor but as a long-term technology partner. Our subscription-based cooperation model gives healthcare organizations continuous access to expert development and support, ensuring that the systems are aligned with evolving business goals.

Whether you’re looking to build predictive analytics tools or optimize existing data workflows, our dedicated team is here to guide your transformation journey from day one.

Sources of Information