The Global Big Data in Healthcare Market size is projected to be worth around 145.8B by 2033. This is exciting news for the industry, but what does this mean for healthcare providers?
Besides its unprecedented worldwide value, big data in healthcare is transforming patient outcomes, practice management, and how providers and organizations make informed decisions.
Join us as we break down the role of big data in healthcare and reveal:
- What big data in healthcare is
- How different healthcare sectors harness this information
- The future of big data
- How to leverage big data in your practice
What is Big Data in Healthcare?
Stripped down, big data in healthcare refers to the large amounts of structured and unstructured data generated by agents like electronic health records (EHRs), wearables, genomics, medical imaging, and patient feedback.
When analyzed carefully, this massive data set helps identify trends, uncover patterns, and make informed decisions that can transform the healthcare industry.
So, what are the tangible benefits of big data? Below, we list eight impactful ways organizations use this information and the individual roles big data plays in healthcare today.
How Big Data is Harnessed in Modern Healthcare
1. Population Health Management
Population health management relies heavily on big data to stratify patient groups based on risk, demographics, and health outcomes. By leveraging this information in healthcare, providers can anticipate the needs of specific populations, allowing for targeted interventions.
Big Data in Action
For example, one article reveals that Jefferson Hospital studied social determinants of health regarding the COVID-19 vaccine. The hospital used metrics called the “social vulnerability index” and the “community need index” to assess and target where the vaccines should go.
2. Predictive Analytics
Predictive analytics leverages advanced algorithms and machine learning to foresee potential health issues before they occur. For providers, this often means avoiding preventable hospitalizations and reducing the chances of complications.
Big Data in Action
Wearable devices and IoT sensors can collect continuous data on everything from blood pressure to sleep patterns, and predictive models can alert healthcare professionals about irregularities that may signal impending issues like heart attacks.
3. Personalized Medicine and Minimizing Errors
“One-size-fits-all” treatment approaches are dwindling. Big data for personalized medicine enables clinicians to create tailored treatment plans based on individual health profiles, including genetic data, medical history, and even lifestyle choices.
Applying big data analytics in healthcare can also help lower medical error rates. By analyzing data such as EHRs for anomalies, data analytics solutions can flag suspicious entries for manual inspection.
Big Data in Action
Researchers at Johns Hopkins developed a technique to harness big data for correcting diagnostic errors, comparing patient symptoms with diseases frequently misdiagnosed.
The metric This approach could potentially transform diagnostic quality and safety across a wide range of clinical problems and settings.
4. Reducing Healthcare Costs with Big Data
Value-based healthcare heavily relies on big data to measure patient outcomes and cost drivers, according to the World Economic Forum. By analyzing treatment patterns, hospital stays, and patient behaviors, providers can identify inefficiencies and implement cost-cutting measures without sacrificing care quality.
Big Data in Action
One paper finds that predictive analytics helped healthcare organizations reduce emergency room admissions and unnecessary testing, leading to significant savings and revenue generation.
5. Driving Medical Research
Big data is driving scientific discoveries. Integrating big data and deep learning transforms clinical trials, genomic research, and drug development.
Researchers use this information in clinical trials to rev timelines, improve study accuracy, and uncover connections that would otherwise remain hidden.
Big Data in Action
He et al. developed a deep learning algorithm using neural networks to detect carotid plaques and assess their resilience. Now, clinicians have a consistent and objective diagnostic method for carotid artery screening, which is crucial for stroke prevention.
6. Enhanced EHR Data Utilization
Electronic Health Records are a treasure trove of patient data. Unfortunately, providers struggle to make full use of this resource. Big data analytics transforms EHR data utilization by converting raw information into actionable insights.
Big Data in Action
The Journal of Big Data cites that healthcare providers and organizations use these insights to:
- Track disease progression
- Predict patient outcomes
- Provide real-time clinical or medical care
- Personalize treatment protocols
7. Artificial Intelligence in Healthcare
Artificial intelligence (AI) and big data analytics are helping physicians realize the promise of precision medicine and have the potential to drive innovations in disease management.
Natural language processing (NLP) allows AI to interpret medical records more effectively, while machine learning (ML) algorithms create predictive models for diseases.
Big Data in Action
Diagnostic tools powered by AI are even used to detect early signs of conditions like cancer. For example, a report reveals data from thousands of images boosted by patient medical information taps into a larger knowledge bank that no human could achieve.
When highlighting a lesion, the AI images are precisely marked — using different colors to point out various abnormalities, such as extreme cell density and shape distortions.
8. Improving Patient Outcomes with Big Data
The ultimate goal of any healthcare system is to ensure optimal outcomes for its patients. Big data improves this mission by enabling evidence-based care, reducing health inequities, improving coordination, and decreasing errors.
Big Data in Action
For example, The Digital Twin Neighborhoods project uses EHR data to better understand health disparities by pulling geographic, biological, and social determinants of health (SDOH) information. Tools that accurately predict the outcomes observed within a population’s EHRs can inform health equity interventions.
Challenges of Big Data in Healthcare
Big data in healthcare is highly desirable. Still, according to TechTarget, concerns about data access and collection, bias, and patient security issues have caused providers and organizations to be leery in working with this information.
Here are a few examples:
- Health organizations must follow regulations such as HIPAA but are largely left to their own devices to determine what policies they put in place to protect their patient’s data.
- Utilizing big data requires experienced personnel. And merging data from EHRs with other technology or software requires robust integration tools to ensure accurate analysis.
- Even with the right staff, obtaining quality training data is complex, leaving many organizations needing more resources to build effective models.
Overcoming data quality, bias, and security issues and fostering user trust will be vital to successfully using these models in clinical care.
Tackling Data Security in Healthcare
While big data offers immense promise, it comes with security risks. The Office for Civil Rights (OCR) report found that 387 data breaches of 500 or more records were reported in 2024.
Protecting sensitive patient information is critical for maintaining trust and compliance with HIPAA regulations. Using advanced HIPAA-compliant communication tools and EHR systems that follow security best practices is a powerful way to make sure your patient data remains secure. These systems should offer:
- Advanced encryption protocols
- Regular audits
- AI-driven monitoring
Top Benefits of Big Data for Providers
Utilizing big data helps healthcare providers make smarter decisions about improving patient care. But it goes much deeper than that. Here’s how:
- Better patient experience and care. Utilizing big data and data-driven healthcare solutions improves the overall patient experience. Identifying patient patterns personalizes treatment, helps make more accurate diagnoses, and helps tailor care plans based on individual needs.
- Early disease detection and better clinical decisions. More data means better decision-making. Big data can power clinical decision support systems that provide real-time recommendations to physicians during patient consultations. This helps in early disease detection and directs them toward evidence-based treatment options.
- Big data saves big money. The technology, like EHRs, used in big data reduces practice costs by pinpointing areas for improvement. Analyzing patient flow data allows practices to manage staffing levels better, improve clinical workflows, optimize patient visits, and reduce wait times. It also digitizes records and improves proactive care, lowering overall costs.
- Builds bridges between patients and providers. The connectivity offered by big data leads to better communication between patients and providers. Reporting from interoperable EHR systems also makes it easier to see when something goes wrong through real-time alerts.
The Future of Big Data in Healthcare
Big data is not just a tool to collect information—in this new value-based era, it’s a catalyst for a healthier, more efficient, and more equitable healthcare system.
By enabling more personalized patient care, accelerating medical research, optimizing practice management operations, and enhancing preventive care and data security, big data is positioned to transform the healthcare industry.
Leveraging Big Data in Your Practice
Big data transforms modern healthcare systems and empowers providers and researchers to make better-informed decisions that directly impact lives. However, if you want to leverage big data effectively, having the right tools and scalable solutions is essential.
Contact EverHealth today to learn about our integrated ecosystem of intuitive, easy-to-use products that work with how you run and grow your practice.
Together, we can help you utilize data and innovation to improve patient care and operational efficiency.