Healthcare generates large amounts of data every day. From patient records and medical scans to treatment plans and clinical trials. This information, known as big data, has the potential to improve patient care, improve efficiency, and drive innovation. But many organisations are still figuring out how to use it effectively.

With AI-driven analytics, wearable technology, and real-time monitoring, healthcare providers, insurers, and pharmaceutical companies are using data to make better decisions for patients, personalise treatments, and predict health trends. So how can you do the same?

Let’s explore the fundamentals of big data in healthcare, its real-world impact and what steps leaders can take to maximise its growing impact.

What is Big Data?

Big data refers to the vast amounts of structured and unstructured information from patient records, medical imaging, wearables, and clinical research. Proper analysis can improve patient care, support better decision-making, and reduce costs.

This data comes from a wide range of sources, including electronic health records (EHRs), test results, diagnoses, medical images, and real-time data from smart wearables. It also includes healthcare-related financial and demographic information. When properly analysed, it helps identify patterns, predict health trends, and support evidence-based decision-making.

The global healthcare market is expanding quickly and is expected to be worth USD 145.42 billion by 2033. As more organisations adopt AI-driven analytics and machine learning, data is becoming a key driver of innovation, helping healthcare professionals deliver more personalised and effective care.

The Three V’s of Big Data

To better understand big data, we can break it down into three key characteristics: volume, velocity, and variety.

1. Volume

The industry produces massive amounts of data, from electronic health records (EHRs) and medical imaging to clinical research and wearable devices. The total volume of healthcare data doubles every 73 days. Managing this requires advanced storage solutions, such as cloud computing and NoSQL databases, to handle both structured and unstructured data effectively.

2. Velocity

Healthcare data is constantly being created. Real-time data streams from patient monitoring systems, wearable technology, and AI-powered diagnostics provide continuous updates. To be useful, this data must be processed instantly, allowing professionals to make fast, informed decisions that support better patient care.

3. Variety

Healthcare data comes in many formats, from structured databases to unstructured text, images, videos, and biometric data. Around 80% of healthcare data is unstructured, meaning it doesn’t fit neatly into traditional databases. A patient’s medical history might include lab results, prescriptions, clinician notes, and radiology reports, all in different formats. Integrating and analysing this diverse information is essential for identifying trends and improving treatments.

Mastering these three V’s helps healthcare organisations make better use of data, leading to more accurate diagnoses, personalised treatments, and improved patient outcomes.

Key Sources of Healthcare Data

Now that we’ve discussed the Three V’s, it’s important to explore where this data originates. The primary sources of healthcare data contribute to the massive volumes of information, real-time updates, and diverse formats that we’ve just covered.

Here are some of the key sources:

• Electronic Health Records (EHRs) & Medical Records (EMRs) – Digital records containing patient histories, test results, and prescriptions.
• Wearable Devices & Health Apps – Smartwatches, fitness trackers, and remote monitoring tools that gather real-time health metrics.
• Medical Imaging & Genomic Data – X-rays, MRIs, and DNA sequencing that assist in diagnostics, research, and precision medicine.
• Clinical Trials & Research Databases – Data from large-scale studies that drive medical advancements and evidence-based medicine.
• Public Health & Epidemiological Data – Population health data that track disease trends and guide public health strategies.
• Hospital Information Systems (HIS) & Administrative Data – Operational data that help manage resources and patient flow within healthcare facilities.

These sources contribute to the expanding pool of healthcare data, helping organisations make smarter decisions and deliver better care for patients.

Benefits of Big Data in Healthcare

As healthcare organisations continue to collect more data, big data is proving to be a game-changer in improving patient care, driving clinical outcomes, and making healthcare more efficient. By analysing vast amounts of information, providers can identify trends and patterns that may have otherwise gone unnoticed. Below are some of the key benefits that big data brings to healthcare, from better patient care to more effective operations.

Data Privacy and Security in Healthcare

While big data enhances patient care and efficiency, it also brings critical data security challenges. IBM’s 2024 Cost of a Data Breach report highlights the average healthcare breach costs $9.77 million. Protecting patient data is crucial for maintaining trust and avoiding risks.

Source: Cost of Data Breach Report, IBM

Key Challenges in Healthcare Data Security

Several issues make healthcare data security more difficult:

With so much data being collected and shared, these challenges are becoming more complex, making it crucial to stay on top of security measures.

Regulatory Framework: HIPAA and Beyond

In the U.S., the Health Insurance Portability and Accountability Act (HIPAA) sets the rules for protecting patient data. While HIPAA covers the basics, healthcare organisations need to stay on top of evolving security threats and regulations as technology changes.

Besides HIPAA, other important regulations include the HITECH Act, which supports the use of electronic health records (EHRs) and strengthens privacy protections, and the General Data Protection Regulation (GDPR) in the European Union, which controls how personal data is used and gives patients more control over their information.

In our previous blog, The Golden Age of Data in Healthcare, we touched on the challenges that come with using new technologies like blockchain. While blockchain offers secure data storage, it also raises concerns around data ownership and staying compliant with rules like HIPAA and GDPR.

Solutions to Enhance Healthcare Data Security

To better protect patient data, healthcare organisations should implement:

• Data Encryption: Keeps data secure even if intercepted.
• Multi-Factor Authentication (MFA): Adds an extra layer of security by requiring more than just a password.
• System Monitoring and Threat Detection: Monitoring systems for unusual activity helps quickly spot potential breaches.
• Employee Training: Teaching staff about security best practices and how to spot phishing attempts helps reduce risks.

By following clear security measures and meeting regulatory requirements, organisations can prevent breaches and keep patient trust intact.

Enhancing Healthcare Security with Erlang, Elixir, and SAFE

As we’ve seen, healthcare faces ongoing security challenges such as outdated systems, weak passwords, internal threats, and securing mobile and cloud data. Erlang and Elixir, by their very nature, offer solutions to these problems.

• Outdated systems: Erlang and Elixir are built for high availability and fault tolerance, ensuring critical healthcare systems remain operational without the risk of system failures, even when legacy infrastructure is involved.
• Weak passwords & internal threats: Both technologies provide process isolation and robust concurrency, limiting the impact of internal threats and reducing the risk of unauthorised access.
• Mobile and cloud security: With Erlang and Elixir’s scalability and resilience, securing data across mobile platforms and cloud environments becomes easier, supporting secure, seamless data exchanges.

To further bolster security, SAFE (Security Audit for Erlang/Elixir) helps healthcare providers identify vulnerabilities in their systems. This service:

• Identifies vulnerabilities in code that could expose systems to attacks.
• Assesses risk levels to prioritise fixes.
• Provides detailed reports that outline specific issues and solutions.

By combining the inherent security benefits of Erlang and Elixir with the proactive audit capabilities of SAFE, healthcare organisations can safeguard patient data, reduce risk, and stay compliant with regulations like HIPAA.

Conclusion

Big data is transforming healthcare by improving patient care and outcomes. However, with this growth comes the need to secure sensitive data and ensure compliance with regulations like HIPAA and GDPR.

Erlang and Elixir naturally address key security challenges, helping organisations protect patient information. Tools like SAFE identify vulnerabilities, reduce risks, and ensure compliance.

Ultimately, securing patient data is critical for maintaining trust and delivering quality care. If you would like to talk more about securing your systems or staying compliant, contact our team.