The healthcare industry generates an overwhelming amount of data every single day-roughly 137 terabytes!
This massive influx comes from a variety of sources, including medical devices, genetic studies, medical imaging, patient-generated health data, and historical EHR records. But the challenge is that this data isn’t neat or uniform. It’s a chaotic mix of structured EHR entries, unstructured physician notes, and complex imaging files, making it tough to store, process, and analyze. And clearly, traditional data handling and processing won’t be enough for this.
That’s where big data implementation comes in. This approach relies on AI-powered, large-scale systems that orchestrate several tools, techniques, and platforms to analyze complex healthcare datasets to uncover patterns, predict outcomes, and make smarter, faster decisions. Curious about how big data implementation is helping medical research and patient care? Dive in.
The State of Big Data in Healthcare
The big data in the healthcare market is a fairly new, sizeable, and growing segment in the broader big data market. It accounted for almost 10% of the entire market, crossing US$ 25 billion1 in 2023 (when the big data market stood at US$220 million2). It is anticipated to grow at a CAGR of 18.48% during the forecast period of 2024-2032. (Source: 1, 2)
Key Technologies Driving Big Data in Healthcare
“Big Data,” the name almost gives it away. It refers to large and complex datasets that cannot be processed efficiently using traditional methods. In healthcare, big data implementation refers to the collection and analysis of such datasets, which have patient, clinical, and surgical data to improve diagnostics, personalize treatments, and enhance operational efficiency. But intuitively, data alone cannot make it happen. It takes a suite of multiple technologies, including:
1. AI and ML
Like in many industries, AI and machine learning in healthcare serve as a powerful tool to process and analyze large volumes of big data. Combined with human expertise, it becomes even more impactful in offering actionable insights like accurate diagnoses and tailored treatment plans.
Example: IBM’s Watson Health was a prime example of AI and big data utilization in healthcare. It was programmed to analyze millions of medical journals and patient records to recommend evidence-based treatment options. However, due to several technical hurdles and market deployment issues, Watson’s capabilities and healthcare requirements were mismatched. You can read more about this in the IBM Watson Summary Paper. Despite its downfall, Watson’s challenges paved the way for better AI systems in the healthcare sector.
Discover the Power of AI/ML + Human Intelligence for Better Data Management
“Incorporating AI in data management alone isn’t enough. An efficient way of tackling big data management challenges is combining AI with human expertise, creating a balanced approach that ensures data is used and managed responsibly…”
2. Data Engineering
As the healthcare industry relies on various kinds of data (patient records, medical journals, disease spread data, etc.) from a range of sources, there has to be a system to unify the findings from each. Data engineering is the technology behind this unification. It integrates, prepares, and processes large volumes of healthcare data, enabling the utilization of robust data pipelines that handle diverse formats, such as EHRs, imaging files, and IoT streams.
Example: Mass General Brigham, a reputed Boston-based healthcare center, pioneered using a centralized data engineering system to analyze EHR data for medical histories. This system allowed nurses, doctors, and even patients to access their medical information through a single access point.
3. IoT
IoT devices generate real-time streams of health-related data, contributing to the volume, velocity, and variety of big data in healthcare. A single hospital can generate gigabytes of data from these wearables, connected medical devices, and environmental sensors. This data, when collected and processed using AI/ML algorithms, can give many action points.
Example: Nowadays, “mood-aware IoT devices” are becoming very popular. These devices (a watch, smart LED, lighting system, etc) collect and analyze data such as heart rate and blood pressure. Using these data points, they can determine a person’s mental state and overall mood.
IoT medical devices are so effective that their corresponding market is expected to exceed $500 billion by 2025!
4. Edge Computing
Building on IoT’s utility in healthcare systems, edge computing allows you to process the accumulated healthcare data closer to its source, such as medical equipment, to reduce latency. This is particularly helpful for unconscious patients in ICUs/CCUs where doctors or other caretakers need real-time updates.
5. Robotic Process Automation (RPA)
Today, robotic surgeries have been normalized in the healthcare industry. Due to rising demands for minimally invasive procedures, the growing elderly population, and the lack of consistent medical expertise throughout the globe, RPA is more prevalent than ever.
Example: In fact, in the USA alone, more than 65,000 general surgeries were performed by robotic arms and systems this year, as per PubMed.
The Role of Big Data in Transforming Healthcare
Implementing big data solutions in the healthcare segment can help in a range of tasks, including:
1. Precision Analytics Diagnostics
Big data in healthcare aids precision diagnostics by enabling quick analysis of vast datasets, including imaging scans, genetic data, and patient histories, to detect disease patterns. For instance, healthcare data analytics platforms can identify subtle anomalies in imaging results, improving early disease detection rates and reducing diagnostic errors. The oncology segment has been the largest benefactor (in terms of revenue) of this big data application, accounting for 24% of the total revenue generated by this market.
2. Predicting Disease Outbreaks and Onset
AI and big data in healthcare work together to spot patterns in health records, environmental data, and even social media trends, helping predict disease outbreaks. During the COVID-19 pandemic, these tools were instrumental in forecasting infection spikes and guiding resource allocation. Such predictive insights are invaluable for public health preparedness and reducing the impact of epidemics.
3. Patient Stratification
Big data enables healthcare providers to group patients based on factors like genetics, lifestyle, and medical history, identifying those at higher risk for specific conditions. Advanced healthcare data management ensures these stratified insights are accessible across departments, improving interoperability in healthcare systems. This seamless data sharing helps prioritize high-risk individuals and coordinate personalized treatment plans effectively.
4. Virtual Clinical Trials
Virtual clinical trials are decentralized studies where participants interact with researchers remotely, often using digital tools like wearables and mobile apps to collect data and healthcare data analytics platforms to analyze it. This enables faster participant recruitment, continuous monitoring, and personalized insights, cutting actual trial costs a considerable amount.
5. Genome-Side Association Studies
GWAS are large-scale studies that examine genetic variants across genomes to identify their associations with specific diseases. These studies rely heavily on AI and big data in healthcare, often analyzing terabytes of genomic data from hundreds of thousands of participants. The findings of this genetic data are combined with other phenotypic and environmental datasets to identify markers for widespread diseases like Alzheimer’s, Diabetes, and even certain cancers.
6. Reducing Hospital Readmissions
As per the NIH (National Institutes of Health), the average cost of 30-day all-cause adult hospital readmissions stands at US$ 16,037.08. Imagine the total cost for millions of patients who are readmitted each year?! With proper big data implementation, hospitals can significantly reduce the number of readmissions. By analyzing patient discharge patterns, follow-up care, and social determinants of health, they can try identifying certain characteristics/factors that lead to readmissions.
7. Healthcare Claim Denial Management
Big data simplifies healthcare claim denial management by uncovering patterns and root causes behind rejections. It analyzes claim histories, payer rules, and patient information to detect issues like incomplete documentation or coding errors. With these insights, healthcare analytics platforms proactively flag potential problems before submission, reducing denials and speeding up reimbursements.
Case in Point: How Expert Claim Data Handling Helped our Client Recover $240K+
The Client: A California-based psychiatry and neurology practice specializing in mental and behavioral health faced high claim denial rates and revenue losses while managing submissions for 20 insurance providers, including Medi-Cal and commercial insurers.
The Challenge: Delayed and unpaid claims were impacting their cash flow and the overall financial stability of the practice, requiring efficient claims processing and follow-up. They had over $300,000 in denied claims across 2,000+ cases, with a denial rate exceeding 35% and an appeal success rate below 38%
How did Claim Data Management Help them?
We took a data-focused approach to tackle the client’s claim denial challenges.
- Our team reviewed and categorized denied claims to identify key issues like missing information or coding errors.
- By acquiring real-time eligibility data from insurance portals using custom ETL pipelines, we validated pre-authorizations and verifications.
- Given the unique complexities of psychiatry and neurology billing, we aligned specialized medical data engineers to oversee the documentation and appeals for denied claims.
This holistic process reduced errors, improved claim approvals, and helped the client recover lost revenue.
Why does Big Data Implementation Still Feel Out of Reach for Many Healthcare Providers?
Despite growing significantly, successful big data implementation is still a far-reaching reality for many healthcare providers, particularly smaller ones. They face several barriers, including:
1. Fragmented Healthcare Data
Healthcare data often resides in silos across hospitals, clinics, labs, and insurance providers. These systems often lack interoperability, making it difficult to unify and analyze data comprehensively.
2. Managing Unstructured and Semi-Structured Data
A large portion of healthcare data, such as physician notes, medical imaging, and patient feedback, is unstructured and often riddled with inconsistencies. Cleaning this data to remove errors, redundancies, and inconsistencies is a critical challenge that requires advanced data-cleansing processes. Additionally, indexing this diverse data to make it searchable and usable for analytics is resource-intensive, requiring robust data engineering solutions to ensure accuracy and accessibility.
3. Data Privacy, Security, and Compliance
Healthcare organizations must adhere to strict regulations such as HIPAA and GDPR while handling sensitive patient data. Breaches or non-compliance can lead to heavy penalties and eroded trust.
4. Scalability and Real-Time Processing
With the exponential growth of healthcare data (463 exabytes generated each day) from IoT devices, EHRs, and wearables, systems often struggle to scale while maintaining real-time analytics capabilities. This can delay critical clinical decisions and strain infrastructure.
5. High Costs of Infrastructure
Implementing real-time big data solutions requires significant investment (can even go beyond US$300,000) in infrastructure, tools, and skilled personnel. For many healthcare providers, especially smaller ones, these costs are prohibitive.
6. Difficulty in Identifying Actionable Insights
Even when large datasets are integrated, sifting through them to identify actionable insights can be challenging due to the sheer volume and complexity of healthcare data.
Making Big Data Work: The Role of Data Engineering
Data-related challenges remain the biggest roadblocks to successfully implementing big data in healthcare. This is where data engineering steps in to orchestrate multiple systems to collect, process, and manage large-scale data. By tackling issues like integration, scalability, and analytics, it provides the solid foundation that healthcare providers need to turn complex data into actionable insights. Here’s how it can help you address specific challenges:
1. For Fragmented Data
Data engineering allows you to integrate various datasets by designing custom ETL pipelines and APIs. The resulting framework connects multiple healthcare systems to transfer this data into a centralized healthcare analytics platform. With a unified data view, providers can derive actionable insights faster, improving patient care.
2. For Managing Unstructured and Semi-Structured Data
Many companies use data engineering techniques focused on cleansing, standardization, and indexing, with ML and NLP to transform unstructured data into an agreed-upon format that can be used for real-time analysis. This simplifies clinical decision-making, saving both time and resources.
Case in Point: Simplifying Healthcare Data with Cleansing and Indexing
The Client: A leading U.S. medical consultant who helps healthcare providers streamline medical documentation
The Challenge: They needed a big data handling solution to manage and organize extensive medical records (EHR, logs, insurance claims, etc) from multiple facilities.
How Data Engineering (with a focus on cleansing and indexing) Made it Happen
- Medical Data Cleansing: Our team of healthcare data experts employed data engineering techniques to process medical PDFs within the client’s portal. This involved meticulous data cleansing, removing duplicates, blank sheets, and irrelevant documents based on client-provided guidelines to ensure that only accurate and relevant healthcare information was retained.
- Healthcare Record Indexing: After cleansing, our team organized documents chronologically, verified accuracy, and compiled a new PDF with only relevant healthcare data, ready for the coding team to process and submit to insurance claims companies.
- Clinical Data Abstraction & Log Creation: We then created detailed medical logs consolidating key patient information-demographics, diagnostics, treatments, claims, and test results, into a single Word file. Our team extracted and summarized other important details for streamlined medical reimbursements.
- Security Implementation: To safeguard sensitive medical data, we have implemented strict security measures:
- Signed an NDA to ensure confidentiality.
- Follow ISO 27001-certified practices for data and information security.
- Adhere to HIPAA and other relevant regulations as a trusted healthcare BPO provider.
3. For Ensuring Data Security and Compliance
Carefully designed and developed data engineering systems can internalize encryption mechanisms, role-based access control, and audit trails to secure healthcare data management systems. These measures ensure compliance, preventing costly breaches and penalties.
4. For Scalability and Real-Time Processing
Cloud-based data engineering solutions provide the scalability and flexibility necessary to handle the massive influx of healthcare data from IoT devices, EHRs, and wearables. These systems rely on custom data pipelines that efficiently ingest, process, and store high-velocity data streams, providing real-time data to healthcare providers.
5. For High Costs of Infrastructure and Expertise
Cloud-based solutions for medical data management provide cost-effective scalability, allowing you to pay for only the resources you use. Coupled with edge computing for real-time processing, this can reduce upfront infrastructure investments. However, the setup for this arrangement also requires some investment, but the ROI is much better (with reduced overhead and reliance on hardware) if you get professional guidance or partner with a reliable data engineering company.
Ending Note
Big data carries an undeniable potential, be it in assisting medical research or improving patient outcomes. However, implementing successful big data solutions requires more than a technological framework. It depends on the overall state of your healthcare data-quality, format, integrity, etc. Even the most advanced systems can fall short without clean, accurate, and well-structured data.
But that should not stop you from getting started in this direction. With proper planning, data handling, and execution, you can make the most of your healthcare data. And this is where expertly designed data engineering solutions can make a difference. It consolidates fragmented data, transforms it into the required format, and passes it onto healthcare data analytics systems for actionable insights. Orchestrating all these processes empowers healthcare providers to overcome most data-related challenges in big data implementation.
