In the ever-evolving landscape of modern healthcare, the concept of “Big Data” has emerged as a transformative force, promising a revolution in management and analysis, and ultimately resulting in the improved delivery of medical services. Big Data in healthcare is a mosaic of information, including patient data, clinical notes, diagnostic images, treatment histories, and plenty of other healthcare-related data elements.
This vast information base has the potential to streamline operations and drive innovative advances in patient care. In this article, we embark on a journey through health data, delving deeper into data management, data integration, and the analysis of large volumes of data. And, of course, the promising future it brings for the medical industry.
What is Big Data Analytics in Healthcare?
Big Data refers to a challenging process of analyzing vast amounts of data sets to identify hidden patterns, market trends, unknown relationships, and customer preferences, enabling companies to make clinical and business decisions. The term is used to describe the vast amounts of information generated by digital technologies that collect data from a variety of information sources, such as electronic patient records (EHRs), test results, diagnoses, medical images, data from smart wearables, as well as healthcare-related demographic and financial data.
Clinical data management
For many years now, collecting large amounts of data for medical purposes has been expensive and time-consuming. The constantly developing technology makes it easier to gather data, create comprehensive medical reports, and then transform them into valuable conclusions, often saving human lives. This is the purpose of data analytics in healthcare: to use data-driven results to predict and solve problems before it’s too late (predictive analytics), but also to evaluate methods and therapies faster, track changes better, engage patients more in their own healthcare, and equip them with all the tools they need to do so.
How to use Big Data in Healthcare
The number of resources for healthcare professionals to derive knowledge about their patients is constantly growing. This data is usually in different formats and sizes, which is challenging for the user. Nowadays, however, no one focuses on how “big” this data is, but on how to use data wisely. With the right technologies, data can be quickly and cleverly obtained from sources such as:
- Patient portals
- Scientific research
- Payment details
- Databases for general purposes
- Electronic health records (EHR)
- Wearable devices (e.g. smart watches and bands that monitor health parameters, medical equipment such as patient monitors or blood pressure monitors)
Examples of Big Data applications in healthcare
Big Data analytics have numerous capabilities in healthcare, and the list of Big Data uses in healthcare is constantly growing. These solutions can help to improve the quality of care, allow us to optimize processes, and create more effective strategies. The major applications of Big Data in the healthcare industry include:
- Diagnosis and treatment: Big Data enables more accurate diagnosis of diseases and patients’ treatment plans. Analyzing clinical data, such as test results, medical images, and electronic health record (EHR) data, allows doctors to make faster and more precise clinical decisions. It is also possible to monitor the progress of treatment and adapt the therapy to the individual needs of the patient.
- Preventing epidemics: Big Data makes it possible to monitor and track the spread of infectious diseases. Early case detection and contact tracing can help prevent an epidemic and respond quickly in the event of an outbreak.
- Personalized healthcare: With Big Data, you can create personalized healthcare plans which take the individual patients’ needs into account. This also includes tailoring therapies, diets, and other aspects of care to the specific features of the person being treated.
- Optimizing hospital processes: Hospitals and clinics use Big Data to optimize patient management, the availability of places in hospitals, staff planning, and medicine inventory organization. This helps to improve productivity and reduce costs.
- Clinical trials: Big Data makes it possible to conduct more advanced clinical trials, including the analysis of treatment and drug outcomes on a large number of patients. This speeds up the process of developing new drugs and therapies.
- Engaging patients: By monitoring their health through apps and smart wearables, patients can more actively participate in their own healthcare. This data can then be analyzed to provide patients with tailored advice and information.
- Improving health insurance plans: Health insurance companies use Big Data to assess risk and create personalized insurance plans. This allows them to deliver better service and maintain control over costs.
- Better quality: Big Data allows you to monitor the quality of healthcare services and identify areas that need improvement. This helps to safeguard the highest quality of patient care.
- Predictive analysis and prevention: Big Data makes it possible to identify patients who may be at risk of serious diseases in the future. This, in turn, allows you to intervene faster and undertake preventive actions.
- Scientific research: Scientific research and analysis of health trends at the population level, which can lead to new discoveries in medicine, is also possible thanks to Big Data.
Big Data in biomedical research
Big Data in biomedical research is a key factor expediting the discovery and development of new therapies, diagnostics, and a better understanding of biological processes. Genome sequencing, gene expression research, and protein analysis are just some examples of the key role Big Data plays. This data allows researchers to identify the genetic root of diseases, detect diagnostic biomarkers, and develop tailored therapies tailored to individual needs. The combination of Big Data and advanced technologies opens the door to a revolution in healthcare and leads us toward a medicine that is more personalized and effective.
Internet of Things (IoT)
The Internet of Things (IoT) is another significant contributor to the transformation of healthcare through Big Data. In the medical environment, IoT means a combination of a variety of devices and sensors that collect medical data and transmit it to central systems. There, it can be analyzed and used to improve medical care.
With ongoing access to data on glucose levels, blood pressure, physical activity, and other health metrics, carers can respond rapidly to changes and adjust therapies. This not only improves patients’ quality of life but also reduces healthcare costs by preventing complications and visits to the hospital. Combined with Big Data, IoT is a powerful tool for personalizing healthcare and making key clinical decisions.
Data Management: Big Data Applications in Healthcare
Data management plays a key role in harnessing the potential of Big Data in healthcare. In a medical environment with a massive amount of data generated, the effective collection, storage, processing, and analysis of information poses a major challenge. It also improves administrative processes, such as bill settlements and management, or arranging the schedules of medical staff. As a result, data management enables doctors, researchers, and medical institutions to take more informed clinical and strategic measures.
Fortunately, there are a number of tools and systems which can help with collecting, processing, analyzing, and managing huge amounts of medical information. Below you will find some of the most popular tools for this industry:
- Patient Data Management Systems (EHR/EMR): Examples include Epic, Cerner, or Allscripts. These systems facilitate the collection and sharing of patient information (including medical history, test results, and prescriptions).
- Databases: Tools for Database management such as Oracle, Microsoft SQL Server, and MySQL are used to store and organize medical data.
- Hadoop: This is a Big Data framework that can be used for Big Data analytics in healthcare.
- Spark: A tool for processing and analyzing Big Data that is especially useful for analyzing medical data.
- Tableau: A system for creating interactive data visualizations. It makes the analysis and presentation of results easier.
- SAS: Data analysis software that is used in clinical trials and results analysis.
- Apache Cassandra: A solution for real-time storing and managing medical data.
- RedCap: A system for creating databases and managing data in clinical trials.
- QlikView: A data visualization Business Intelligence system that can also help you analyze medical data.
- MongoDB: A NoSQL database that can be employed to store and analyze medical data in an unstructured format.
The above systems are only a small number of the available solutions. The decision to choose a particular tool depends on the needs of the medical organization and the type of data that is to be collected and analyzed.
Challenges of Big Data in Healthcare
Big Data analysis in healthcare brings both many benefits and challenges, often related to the implementation of Big Data healthcare systems for data collection, data storage, and data sharing.
The first such challenge is privacy and data security. Medical data is extremely sensitive and is subject to strict privacy laws. This data must be kept safe, as any breaches can lead to serious legal consequences and loss of patient trust. With this in mind, we move to another challenge: compliance. Healthcare is one of the sectors that are subject to many legal and ethical regulations (such as HIPAA in the United States or the GDPR in the European Union). Complying with these regulations is mandatory and may impose restrictions on data analysis and processing.
But that is not all. The interoperability of systems and data quality are also big challenges. Many healthcare providers use various EHR systems and other data management tools. Ensuring compatibility and interoperability between these systems is often hard and requires standardization. Multiple data sources can be subject to errors, inaccuracies, and gaps. Incorrect data can lead to wrong conclusions and clinical decisions. So now you can see that in data-driven healthcare management, precision and confidence in data quality is a must. Errors in the analysis or the incorrect interpretation of data can lead to serious consequences for patients.
Scalability, data management, and analysis are some of the key challenges here. As the amount of medical data increases, it is necessary to provide adequate infrastructure and resources to store, process, and analyze medical data – not to mention expert knowledge and skills. In this context, a lack of competences can be an additional challenge.
Costs and return on investment can also be a big challenge. Implementing Big Data analysis in healthcare can be expensive, and organizations need to carefully assess the potential return on investment. Such transformations affect business processes, the organizational culture, and the way of thinking so that medical institutions can fully utilize the potential of the solutions implemented.
Despite these difficulties, the potential for Big Data to transform healthcare is huge. The benefits are recognized by many medical organizations striving to address these issues to deliver better healthcare and medical innovation.
Benefits of Big Data in the Healthcare Industry
The implementation and use of Big Data brings undeniable benefits, including:
- Cost reduction: According to Datavant CEO Pete McCabe, smarter use of data could eliminate up to 75% of unnecessary healthcare costs. By identifying patterns and trends, Big Data analytics can help healthcare organizations discover areas for improvement. Big Data helps optimize the management of medicine inventories, medical equipment, as well as other resources.
- Improving the quality of care: healthcare personalization, improving chronic disease management, and early disease detection and prevention: By analyzing clinical outcomes and monitoring quality metrics, Big Data helps identify areas for improvement and raise healthcare standards. It can help with patients suffering from chronic diseases, monitor their health, and deliver customized care.
- Optimization of clinical processes: Medical data allows for better management of processes in medical facilities (hospitals, clinics, etc.). This results in shorter waiting times for patients, optimized availability of places in hospitals, and improved resource management.
- Research and development of therapies: Big Data accelerates clinical research and analysis of therapy results. This allows scientists to discover new medications, treatments, and medical novelties.
- Epidemics and public health monitoring: In the event of epidemics and threats to public health, Big Data allows for rapid monitoring and response. This can help control the spread of disease and save lives.
- Development of telemedicine: Big Data supports the development of telemedicine and makes medical consultations available remotely. This is especially important in situations when access to traditional healthcare is limited.
- Prevention of fraud and abuse: With Big Data, you can detect irregularities in billing and payments in health insurance systems. That’s a big help in preventing fraud.
Future Prospects of Big Data in Healthcare
The future of Big Data in health organizations and the impact of Big Data is highly promising. We had a first-hand look at the global growth of Big Data in the healthcare market during the COVID-19 pandemic, when it reached a value of $32.9 billion in 2021. From 2022 to 2032, this market is forecasted to expand at the average annual rate (CAGR) of 19.2%, reaching an impressive value of $94.7 billion by the end of 2032.
Currently, the healthcare data analysis sector accounts for almost 14.2% of the entire healthcare sector, which is testament to the significant role it plays. Such favorable predictions will also affect the market for Big Data technologies and services; hence, as this technology is adopted in the healthcare sector, it will generate new sources of income. It is also worth mentioning that a significant part of the revenue related to data management in healthcare results from monitoring and managing transactional information between payers and healthcare providers.
Big Data management – summary
The future of analyzing large volumes of data in healthcare is promising. Data processing brings a great deal of challenges and benefits for both medical providers and patients. Companies in the sector can count on reducing costs, improving the quality of services, optimizing clinical processes, developing innovative therapies, and monitoring people’s health. However, to be able to fully use the potential of Big Data, it is worth equipping yourself with the knowledge of experts.
As Inetum, we understand the complexity of customer needs in the healthcare industry, which is why we approach each project individually. If you are interested in using the potential of Big Data in your business or need advice in this area, please contact our specialist: