![\"Transforming](\"https:\/\/www.technoscore.com\/blog\/wp-content\/uploads\/2024\/12\/Transforming-Patient-Care-and-Medical-Research.jpg\")
<\/p>\n<\/p>\n
The healthcare industry generates an overwhelming amount of data every single day-roughly <\/i><\/b>137 terabytes<\/i><\/b><\/a>!<\/i><\/b><\/p>\n 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. 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<\/sup><\/a> in 2023 (when the big data market stood at US$220 million2<\/sup><\/a>). It is anticipated to grow at a CAGR of 18.48% during the forecast period of 2024-2032. (Source: 1<\/a>, 2<\/a>)<\/p>\n “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<\/i><\/b>. It takes a suite of multiple technologies, including:<\/p>\n 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.<\/p>\n Example: <\/b>IBM’s Watson Health was<\/i> 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.<\/b><\/a> Despite its downfall, Watson’s challenges paved the way for better AI systems in the healthcare sector.<\/p>\n “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\u2026”<\/i><\/p>\n Read the Full Blog<\/b><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n 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<\/a> 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.<\/p>\n Example: <\/b>Mass General Brigham, a reputed Boston-based healthcare center, pioneered<\/b><\/a> 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.<\/p>\n 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.<\/p>\n Example:<\/b> 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.<\/p>\n IoT medical devices are so effective that their corresponding market is expected to exceed $500 billion by 2025<\/b><\/a>!<\/p>\n 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.<\/p>\n 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<\/a> is more prevalent than ever.<\/p>\n Example: <\/b>In fact, in the USA alone, more than 65,000 general surgeries<\/b><\/a> were performed by robotic arms and systems this year, as per PubMed.<\/p>\n Implementing big data solutions in the healthcare segment can help in a range of tasks, including:<\/p>\n 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%<\/b><\/a> of the total revenue generated by this market.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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<\/b><\/a>.<\/b> 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.<\/p>\n 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.<\/p>\n The Client:<\/b> 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.<\/p>\n The Challenge:<\/b> 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%<\/p>\n We took a data-focused approach to tackle the client’s claim denial challenges.<\/p>\n This holistic process reduced errors, improved claim approvals, and helped the client recover lost revenue.<\/p>\n Read More<\/b><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n 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:<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n With the exponential growth of healthcare data (463 exabytes<\/b><\/a> 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.<\/p>\n Implementing real-time big data solutions requires significant investment (can even go beyond US$300,000<\/b>) in infrastructure, tools, and skilled personnel. For many healthcare providers, especially smaller ones, these costs are prohibitive.<\/p>\n 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.<\/p>\n 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:<\/p>\n Data engineering allows you to integrate various datasets by designing custom ETL pipelines and APIs<\/i><\/b>. 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.<\/p>\n Many companies use data engineering techniques focused on cleansing, <\/i><\/b>standardization<\/i><\/b><\/a>, and indexing, with ML and NLP to transform unstructured data into an agreed-upon format<\/i><\/b> that can be used for real-time analysis. This simplifies clinical decision-making, saving both time and resources.<\/p>\n The Client:<\/b> A leading U.S. medical consultant who helps healthcare providers streamline medical documentation<\/p>\n The Challenge:<\/b> They needed a big data handling solution to manage and organize extensive medical records (EHR, logs, insurance claims, etc) from multiple facilities.<\/p>\n
\n
\nThat’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.<\/p>\n\nTable of Content<\/b><\/div>\n\n
\n
\n
\n
\n
<\/span>The State of Big Data in Healthcare<\/h2>\n
![\"State](\"https:\/\/www.technoscore.com\/blog\/wp-content\/uploads\/2024\/12\/State-of-Big-Data-in-Healthcare-Infographic.jpg\")
<\/a><\/p>\n<\/span>Key Technologies Driving Big Data in Healthcare<\/h2>\n
<\/span>1. AI and ML<\/h3>\n
\n\n\nDiscover the Power of AI\/ML + Human Intelligence for Better Data Management<\/h2>\n
<\/span>2. Data Engineering<\/h3>\n
<\/span>3. IoT<\/h3>\n
<\/span>4. Edge Computing<\/h3>\n
<\/span>5. Robotic Process Automation (RPA)<\/h3>\n
<\/span>The Role of Big Data in Transforming Healthcare<\/h2>\n
<\/span>1. Precision Analytics Diagnostics<\/h3>\n
<\/span>2. Predicting Disease Outbreaks and Onset<\/h3>\n
<\/span>3. Patient Stratification<\/h3>\n
<\/span>4. Virtual Clinical Trials<\/h3>\n
<\/span>5. Genome-Side Association Studies<\/h3>\n
<\/span>6. Reducing Hospital Readmissions<\/h3>\n
<\/span>7. Healthcare Claim Denial Management<\/h3>\n
\n\n\nCase in Point: How Expert Claim Data Handling Helped our Client Recover $240K+<\/h2>\n
How did Claim Data Management Help them?<\/h3>\n
\n
<\/span>Why does Big Data Implementation Still Feel Out of Reach for Many Healthcare Providers?<\/h2>\n
<\/span>1. Fragmented Healthcare Data<\/h3>\n
<\/span>2. Managing Unstructured and Semi-Structured Data<\/h3>\n
<\/span>3. Data Privacy, Security, and Compliance<\/h3>\n
<\/span>4. Scalability and Real-Time Processing<\/h3>\n
<\/span>5. High Costs of Infrastructure<\/h3>\n
<\/span>6. Difficulty in Identifying Actionable Insights<\/h3>\n
<\/span>Making Big Data Work: The Role of Data Engineering<\/h2>\n
<\/span>1. For Fragmented Data<\/h3>\n
<\/span>2. For Managing Unstructured and Semi-Structured Data<\/h3>\n
\n\n\nCase in Point: Simplifying Healthcare Data with Cleansing and Indexing<\/h2>\n
How Data Engineering (with a focus on cleansing and indexing) Made it Happen<\/b><\/h3>\n
\n
\n
![\"data](\"https:\/\/www.technoscore.com\/blog\/wp-content\/uploads\/2024\/12\/data-engineering-experts.jpg\")
<\/a><\/p>\n