The Cambridge Dictionary defines big data as “very large sets of data produced by people using the internet and that can only be stored, understood, and used with the help of special tools and methods”. It sounds pretty simple when thinking of the data generated by people and used by marketers to analyze trends and gain insights for future campaigns. However, it becomes more interesting, yet tricky, when dealing with big data in healthcare.
Despite the potential, there are several challenges of big data in healthcare. The value of data privacy in healthcare poses a problem for healthcare data security. For example, the information gathered in Electronic Health Records (EHR) includes lots of sensitive data, which must remain confidential. Recently, we discussed why healthcare data security solutions are important and shared shocking statistics on the healthcare data breach rate. Gathering and storing massive volumes of data from different sources is a challenging task since you need to ensure data interoperability to use it meaningfully.
When processed carefully, big data in healthcare detects trends and patterns. Given the occasional outbreaks of global pandemics, finding cures quicker can help save many lives and keep healthcare costs under control as much as possible. Moreover, the niche is quite promising for developers of data analytics solutions since the global healthcare big data analytics market’s size is forecasted to reach $80.8 billion by 2028, showing a compound annual growth rate (CAGR) of 14.01% during 2022-2028.
Today, we will discuss big data examples in healthcare, use cases, and the best technologies to collect, store, and process big data effectively.
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EXAMPLES OF BIG DATA IN HEALTHCARE
Healthcare stakeholders worldwide have begun to harness big data’s power to decrease healthcare costs and transform workflows. Let’s discuss the massive data sources used to generate meaningful insights in the healthcare industry.
Electronic Health Records: EHRs contain detailed data about patients, medical histories, diagnoses, treatments, procedures, and medications. It is one of the most important big data examples in healthcare. By analyzing EHR data, stakeholders can make more informed treatment decisions (e.g., analyze reactions to particular medication for people with a specific medical condition) and prevent hospital readmission. Integrating world data for health research into EHR analysis enhances the depth of insights, leading to more personalized and effective patient care. In a previous article, we discussed the importance of EHR interoperability.
Medical Imaging: Technologies like MRIs, X-rays, and CT scans generate lots of data. Stakeholders can use AI-based tools like machine learning to identify anomalies and patterns that human observers might have missed.
Genetic Data: The genomics field gathers data that helps identify patients at an increased risk of certain medical conditions and tailor treatment and procedures to their specific needs. Correlating genetic data to regular medical data allows using the genetic information to its full potential, for instance, creating alerts for clinicians dealing with patients with the same baseline data as patients diagnosed with complex and dangerous diseases. Matching genetic data to EHRs can significantly improve the effectiveness of preventive healthcare measures.
Internet of Things: This example of big data in healthcare refers to the data generated by IoT devices, such as smart sensors and wearables. Analyzing the data from wearables in real time can help identify early signs of possible health conditions and allow for early intervention, thus, more effective treatment.
The future of big data in healthcare depends on real-time data analysis. The ability to extract insights from different data sources in real time allows for personalized care, early health risk detection, informed treatment, and better patient outcomes. We can see that frequently from the big data use cases in healthcare.
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BENEFITS OF USING BIG DATA IN HEALTHCARE
Improved cancer care diagnosis
Errors during cancer diagnosis may lead to fatal consequences. One of the vital benefits of real-time big data analysis for cancer diagnosis is the ability to detect sights of cancer at an early stage, which increases the effectiveness of an intervention.
The PALGA database in the Netherlands is one of the best examples of using big data for better healthcare quality. The PALGA foundation has governed all digital histopathological records in The Netherlands since 1971. The base covers all 55 pathology labs in The Netherlands and contains over 72 million records of over 12 million patients.
Whenever a Dutch pathologist authorizes a histopathology report, one copy is stored in the local hospital information system and one in the central PALGA database. Thus, the base contains a real-time pathological follow-up for each patient visible to a pathologist or other PALGA member.
PALGA is a case study of big data in healthcare that illustrates the huge potential in recognizing patterns of data from various healthcare databases, for example, in the case of a suspected tumor, by providing pathologists with previous relevant pathological features.
Genomic big data for personalized health care
Genetic data is a foundation for personalized healthcare. Personalized medicine refers to using diagnostic tests to determine the most effective treatments for individual patients. The rapid development of innovative technologies has helped the niche reach several milestones. For example, the computational approach is projected to significantly reshape the therapeutic field in the coming decades.
These approaches use genomic data for a more precise classification of patients to provide more personalized healthcare recommendations, improve disease prediction, and cancer diagnosis, analyze the efficiency and safety of drugs, conduct biomarker research, etc.
Machine learning approaches also allow for working with various types of data, such as generic, genomic, medical images, EHRs, metabolic data, etc., for combining all of this information to design optimum classifiers. Classification of patients is a crucial step towards achieving personalized healthcare since it groups patients based on specific characteristics and tailors treatments and interventions that suit them best.
BIG DATA TRENDS IN HEALTHCARE
The vice president of analytics for the Healthcare Information and Management Systems Society (HIMSS) refers to real-time analytics as the process of rethinking the traditional approach to data due to its complexity and exponential growth. HIMSS is an American not-for-profit organization that works towards reducing costs and improving healthcare quality through the best use of IT and management systems in healthcare.
Big data analysis opens up opportunities for implementing modern solutions and personalized approaches in healthcare. However, the vast volumes of data generated by the healthcare industry make stakeholders seek creative ways to analyze and process all this data. As a result, some approaches become more helpful, gain general recognition, and become trendy. HIMSS defines the following top trends for big data in healthcare:
Expanding Categories: Big data was formerly categorized by volume, velocity, and variety. However, the massive switch to remote treatment and innovation driven by the COVID-19 outbreak resulted in huge healthcare data volumes. For that reason, value and veracity were added to complement existing big data categories.
Data Lakes: In our article about data mining in healthcare, we discussed the advantages of transmitting healthcare data in a unified data format for further analysis. Unlike warehouses, data lakes store data in raw format, which also can be beneficial for data analysis since it preserves the context in which the data was collected.
Predictive Analytics: The numerous benefits of using big data in healthcare include advanced possibilities for predictive analysis. Healthcare stakeholders (such as clinicians, finance departments, researchers, etc.) use insights from big data to ensure the early diagnosis of complex diseases.
Diverse Data: There are lots of data sources that significantly impact the inclusiveness of healthcare solutions. Apart from data about medical conditions, big data should comprise socioeconomic information. Big data in healthcare may include information from social media to analyze society’s satisfaction with the services they receive.
Artificial Intelligence (AI) for Big Data: AI has been actively conquering various markets, and healthcare is no exception. Now, it is more common for the niche to use AI in healthcare as a data analysis tool (to analyze medical imaging, quickly identify patterns in large volumes of medical data, build predictive models for various health issues prevention, etc.).
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Big data plays a significant role in global healthcare since it builds an informed roadmap for improving patient outcomes and developing new healthcare models. Indeed, it is an intricate task that requires advanced tools with sophisticated architecture to process big data effectively.
BIG DATA TECHNOLOGIES IN HEALTHCARE
Big data has enormous potential to revolutionize healthcare and provide a base for new, more effective healthcare models. However, the huge volumes of healthcare data and its diverse sources complicate big data processing. This is where big data technologies come in, such as the Kodjin FHIR Server, which is made for vast amounts of data and high speed. One example is the improvement of claims management.
The Kodjin FHIR Server is an enterprise-level solution for healthcare data management. This server significantly enhances the healthcare IT infrastructure by streamlining data management and analysis. One of the main advantages of the Kodjin server in the context of big data is the functionality of the Kubernetes Cluster software: Kubernetes Cluster containerization, synchronous operation of different applications, better performance, and comfortable microservices management.
The technological stack includes Apache Kafka, which processes real-time data streams. Big data brings significant benefits when processed in real time, such as quick and informed healthcare decisions, reduced likelihood of medical errors, quality analysis of patients’ vitals, preventive alerts for potential healthcare issues, etc.
Another paramount component of the Kodjin FHIR Server is MongoDB. It is a popular NoSQL database for handling large volumes of data.
Our team of healthcare domain experts will help you leverage innovative big data technologies. Contact us and discover how to use the power of big data to the fullest.
