Big Data in healthcare refers to the vast amount of data that is continuously expanding and cannot be efficiently stored or processed using traditional tools. Not only in healthcare, but various industries have also adopted big data to enhance their capabilities, improve customer experiences, and strengthen their brand reputation, thereby contributing to overall growth of the company.
The concept of big data is often described using three key characteristics, known as the 3 V’s, as coined by Doug Laney in the early 2000s:
Volume: Big data is collected from a wide range of sources, like transactions, Internet of Medical Things (IoMT), industrial equipment, videos, images, audio, and social media.
Velocity: Big Data needs to be handled promptly as businesses generate data at an unprecedented speed, driven by the growing adoption of Internet of Things (IoT), and technologies, such as RFID tags, sensors, and smart meters are used to deal with the high velocity of data in real-time.
Variety: Big data comes in various forms, ranging from structured data (such as, names, dates, addresses, credit cards, and stock information) to unstructured data (such as, text, medical records, video files, audio files, and financial transactions).
Types of Big Data in Healthcare
Structured Data: Structured data is quantitative data in the form of numbers and values that can be processed, stored, and retrieved in a fixed format. Within the healthcare sector, structured data includes demographic details, vital indicators (like, height, weight, blood pressure, blood glucose). In addition, various data components like billing codes, prescriptions, and laboratory test results are also considered as structured data. Notably, such form of data is highly organized, allowing for efficient management and easy storage and retrieval from a database.
Unstructured Data: Unstructured data is qualitative data, comprised of information having unexplained conceptual definitions which cannot be analyzed using standard techniques. It accounts for the majority of big data in healthcare and comprises information, such as medical images, surveys, chats, and written narratives. Notably, unstructured data cannot be easily interpreted or analyzed by standard databases or data models.
The figure presents an insightful overview of the expanding unstructured healthcare data
Semi-Structured Data: Semi-structured data is a hybrid of both structured and unstructured data, and it can be easily analyzed. It is loosely arranged into categories using meta tags. For instance, healthcare data stored in JSON and XML formats as well as tweets organized by hashtags.
Management and Storage of Big Data
Big data management is the process of organizing and handling large volumes of structured, semi-structured as well as unstructured data. Across various industries, stakeholders are adopting big data management strategies to effectively manage the exponentially growing data streams. Notably, this digital data can eventually reach several terabytes or even petabytes. Therefore, the primary goal of big data management is to ensure the high quality and accessibility of enormous volumes of information generated from multiple sources for business intelligence and other applications. To facilitate the storage and management of big data, service providers offer solutions, such as data lakes and data warehouses. Both data lakes and data warehouses play crucial role in big data management. They offer businesses the ability to store, manage, and analyze vast amounts of data, enabling them to derive valuable insights and make data-driven decisions. These insights can be used to make improvements in various areas, including customer experience, operational efficiency, and strategic planning. The following table highlights the difference between data lake and data warehouse.
Big Data Analytics in Healthcare
Big data analytics plays a crucial role in the healthcare sector, helping organizations analyze and uncover trends and patterns in large amounts of data. This analysis can be instrumental in improving patient outcomes and optimizing resource allocation. There are four types of big data analytics in healthcare industry, which have been briefly described below:
Descriptive Analytics: Descriptive analytics analyzes data and past events to generate insights . Big data technologies and tools allow users to mine and recover data that helps dissect an issue and prevent it from happening in the future.
Diagnostic Analytics: Diagnostic analytics takes the descriptive analytics a step further by using data to identify correlations between variables and determine the reasons behind observed patterns and trends, providing valuable insights for healthcare organizations.
Predictive Analytics: Diagnostic analytics takes the analysis a step further by using data to uncover the underlying causes of trends and correlations between variables. It builds upon descriptive analytics to identify the reasons behind observed patterns and trends, providing valuable insights for healthcare organizations.
Prescriptive Analytics: Prescriptive analytics is an advanced data analytics technique that involves creating intricate models by integrating multiple data sources and utilizing machine learning to make optimal data-driven decisions.
Application of Big Data in Healthcare
Over the past few years, due to the large volumes of data generated in the healthcare domain, the popularity of big data / big data analytics tools and technologies has increased exponentially in healthcare. Big data in healthcare turns these challenges into opportunities to provide personalized care to the patients by using huge amounts of existing data. Big data can be used across different verticals of healthcare industry.
Population Health Management: Population health management is a crucial aspect of improving community health, and data analytics can play a vital role in this process. By leveraging data analytics, healthcare providers can gather demographic and clinical data from various sources to identify populations in need of care, measure the care provided, and deliver care to the right people.
Electronic Health Record (EHR) Management: One of the key applications of big data analytics in EHR management is predictive analytics. By leveraging this technology, healthcare providers can identify patterns and trends within patient data, enabling them to predict the likelihood of specific diseases or conditions. This early detection and intervention can lead to improved health outcomes for patients.
Hospital Management: Hospitals can leverage big data analytics to drive better patient outcomes, reduce costs, and enhance the overall quality of care. It is worth mentioning that hospitals generate vast amounts of data from various sources, including patient medical records, hospital records, and medical examination results. By analyzing this data, healthcare organizations can identify patterns and trends, predict patient outcomes, and develop personalized treatment plans.
Pharmaceutical Research: Big data analytics can be applied in various areas of pharmaceutical research, including drug discovery and development, precision medicine, clinical trial optimization, pharmacovigilance and drug safety, supply chain optimization, and real-time monitoring and surveillance. By analyzing large datasets from various sources, such as clinical trials, genetic data, and EHRs, big data analytics can help identify new potential drug candidates, accelerate the drug discovery process, and lead to the development of more effective and targeted medicines.
Telemedicine and Telehealth: Through remote patient monitoring, healthcare providers can access real-time information about patients’ health conditions, enabling early detection of potential issues and timely intervention. The analysis of large volumes of patient data, including EHRs and medical imaging, empowers healthcare professionals to identify patterns, trends, and correlations, resulting in more accurate diagnoses, personalized treatment plans, and improved patient outcomes.
Supply Chain Management: The integration of big data analytics in healthcare supply chain management can significantly benefit healthcare organizations by enabling them to gather, store, analyze, and process vast amounts of data, leading to more informed decision-making processes. Big data analytics can be leveraged to optimize the supply chain by providing valuable insights into staffing schedules, inventory management, and demand forecasting.
Owing to the increasing popularity of big data in healthcare domain, there is a huge impact of big data in healthcare market size. Big data analytics is used not only in healthcare market but also being used in different sectors for the growth of the organization and to forecast future trends using machine learning and artificial intelligence. Moreover, big data has also had a considerable impact in the financial sector. Big data in healthcare domain has several advantages and the integration of predictive analytics and machine learning algorithms with big data can enable early detection of diseases, personalized treatment plans, and precision medicine.
Jayita is a Senior Business Analyst who has been a valuable member of the Roots Analysis team since 2021. She holds a bachelor’s degree in pharmaceutical sciences from a prestigious institute, which has provided her with a strong academic background. With her commitment to staying at the forefront of the healthcare and life sciences domain, she is able to effectively analyze complex datasets and provide actionable insights. Throughout her career, Jayita has contributed to over six syndicate market research reports, covering a wide range of trending domains. Some of the areas she has worked on include protein design and engineering, metabolomics services, bioavailability enhancement technologies and services, plasmid DNA manufacturing, NAMPT inhibitors, and big data analytics in healthcare domain. In addition to her expertise in market research, Jayita has also demonstrated her financial acumen through her involvement in an investor series project focused on the intriguing field of RNA therapeutics. This project highlights her ability to navigate the complex intersection of science and finance.