Healthcare is a very complex system. All around the world, healthcare systems had been facing an astonishing amount of challenges due to the increase in the average life span of the world population and the related health problems and disabilities associated with it. This has led the system to shift from its current traditional approach to a more modern and technology-driven approach. The biggest challenge now is improving the quality of healthcare while keeping the related costs under control. This is where Big Data comes into use. Big Data can help healthcare providers achieve these targets in unparalleled ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of otherwise meaningless data into actionable knowledge for more efficient operations in the healthcare industry.
This brings us to the question, What is Big Data in healthcare?
In a more general context, Big data is an immense amount of information on any given topic. Big data includes any kind of information that is generated, stored, and analyzed on a large scale that is generally too vast and complex to be managed with traditional information storage systems.
In the context of healthcare, Big Data represents large volumes of data and information created through digital technologies that collect patients’ data and generate records, which are otherwise too large and complex for traditional technologies to process. These kinds of digitized records are extremely helpful in managing the performance of dental practices, hospitals, etc. It is generally characterized by the following 4 V’s:
- Volume — which refers to the huge sizes of data
- Velocity — which means that data generation is a continuous process happening in real time.
- Variety — which points out to the broad range of sources and formats that data comes in.
- Veracity — which emphasizes the quality of data and the value that it could potentially bring.
Sources of Big Data in healthcare
- Government Agencies:
Governments and their agencies both generate and collect vast quantities of data through their everyday activities, such as pension management and allowance payments, collection of tax, national health systems, records of traffic data, and records of issuing official documents. - Patient Portals:
Patient portals allow for a more structured approach to collecting, storing and managing patient information as well as addressing and resolving patient concerns. The valuable patient information that they store is accessible to both the patients as well as their healthcare providers. - Research Studies:
Researchers across various disciplines have contributed significantly to big data with their ability to link and cross-reference data from varying sources improving the accuracy of scientific findings that serve as platforms for the development of various disciplines.They ultimately help provide an appropriate starting point for further investigation. - Generic Databases:
Today, an amalgamation of traditional and modern databases is used to acquire relevant big data. These databases are deployed for various purposes and can provide for the extraction of insights that are used to drive business profits. Some popular databases include MS Access, DB2, Oracle, SQL, and Amazon Simple, among others. - Electronic Health Records (EHR):
EHRs track and record patients’ health data eventually reducing the need for unnecessary tests and checkups and the associated costs. This data also helps healthcare providers treat patients more efficiently by reducing the time and effort invested in duplicate tests and ultimately improving patient care. - Wearable Devices:
These include modern-day wearable gadgets like fitness trackers, smart watches, smart clothes, biosensors, blood pressure monitors, glucose trackers, and other electronic devices that can be worn. These devices are designed to collect different kinds of health information. This data is generally transferred to the user’s smartphone and/or a remote/cloud server where it can be retrieved as and when required. - Smart Phones and Apps:
These are also useful for recording and retrieving kinds of health and activity-related data. They also track and keep a record of data that is manually entered by the user or transferred from connected devices. This data is also stored on a server that can be retrieved and shared as and when required. If used on a frequent basis, health and wellness-related apps can be of great help in tracking health conditions and diagnostic procedures. - Payer Records:
This is generally termed transactional data and as the term itself suggests, it refers to the data gathered both online and offline transactions during different points of sale. A unique characteristic of transactional data is that it is time printed, which makes it highly volatile.
Why Collecting Data in Health Care Is Important
Big data collection and analysis enable doctors and health administrators to make more informed decisions in regard to the treatment and services offered to patients. In a simple instance, a doctor with access to big data of his/her patients has data samples to draw from, which is useful in identifying any warning signs of serious ailments well before it arises. Treating a disease at an early stage is simpler and more cost-effective in comparison to when it is treated once it has progressed significantly.
In other prominent areas of the healthcare industry, big data helps administrators make informed decisions on funding and resource allocation using key performance indicators and data analytics. Big data gathered from public health records and Google maps have been of significant use while creating critical health maps. With the help of big data, administrators and providers can make use of relevant information to determine where to deploy their resources.
Hospitals and other large care facilities use big data to capture a comprehensive picture of the patient experience. Big data tools allow healthcare staff to merge useful data that would otherwise only gather dust in the archives of clinics and hospitals. Big data allows for the consolidation of patient data, which in turn facilitates a rapid and accurate mode of communication between patients and providers.
How big data can change the healthcare industry:
- Reducing Cost
Big data can serve as an incredible means to cut expenses for hospitals that are generally running on overhead costs or are understaffed. Appropriate research with the help of big data can help resolve this problem by foreseeing the confirmation rates and helping with appropriate staff allotment.
- Advancement in the Healthcare Sector
Big data is developing day by day as the amount of data being created is increasing exponentially. This will be a tremendous advantage for advancements in the field of science and technology. Thus big data, in a way, is also supporting the healthcare industry in improving.
- Health Tracking
Big data is assisting a lot in tracking the health record of the user. It helps in tracking many aspects of a patient’s health like sleep, exercise, and distance walked, alongside more important aspects like heart rate, blood pressure, blood oxygen levels, glucose monitors, and many more.
- Assisting High-Risk Patients
The biggest plus point of having a digitized form of data is having the assurance that all important files are stored in a safe and accessible manner and that they will never get misplaced. Similarly, in the case of all hospitals, digitization helps store records of data from numerous patients in a secure and accessible manner, which makes it the ideal data that can be gotten to comprehend the pattern of numerous patients at any given time. This method is particularly helpful to keep a check on high-risk patients and care for them in a more efficient manner.
- Preventing Human Errors
Human errors in a clinic or hospital can end up turning fatal for the patient. Hence this is one of those instances, where the scope for human errors cannot be accommodated at any cost. Big Data can greatly help prevent human errors in hospitals by helping with ready-to-access patient information like medical history and prescriptions at the snap of a finger. This tool is especially beneficial for doctors who meet with multiple patients in a day.
How Big Data Improves Patient Outcomes
Earlier amassing large volumes of data for clinical use was an expensive and time-consuming process. But now, innovative technologies have not only helped in the collection of large amounts of data electronically but have also helped in the seamless conversion of the same into easily readable forms. Health professionals can now come up healthcare solutions backed with solid data to improve patient outcomes in many ways:
- Predictive Analytics: Big data analytics can identify patterns and trends in patient data, enabling healthcare providers to predict disease progression, identify high-risk patients, and intervene earlier. Predictive models can help in identifying patients who are more likely to develop specific conditions, allowing for early interventions and personalized treatment plans.
- Personalized Medicine: Big data enables healthcare providers to analyze vast amounts of patient data to tailor treatments and interventions to individual patients. By considering factors such as genetic information, medical history, lifestyle, and environmental data, doctors can make more precise diagnoses and develop personalized treatment plans that are more effective and have fewer side effects.
- Clinical Decision Support: Big data can provide healthcare professionals with decision support tools that help them make informed decisions at the point of care. By integrating patient data, medical guidelines, and research findings, these tools can suggest treatment options, alert healthcare providers to potential drug interactions or allergies, and provide real-time clinical guidance, leading to better and safer patient care.
- Population Health Management: Big data can be used to identify trends and patterns in larger populations, allowing healthcare providers to implement preventive measures and interventions to improve public health outcomes. By analyzing data on disease prevalence, risk factors, and treatment outcomes, healthcare organizations can target resources to high-risk populations and develop strategies to reduce the overall burden of disease.
- Research and Development: Big data can accelerate medical research by providing researchers with access to large datasets for analysis. By analyzing diverse patient populations and combining different data sources, researchers can discover new insights, develop new treatment modalities, and improve patient outcomes.
- Real-time monitoring and early detection: Big data analytics can enable real-time monitoring of patients, especially those with chronic conditions or those in critical care settings. By collecting and analyzing data from wearable devices, sensors, and EHRs, healthcare providers can detect early warning signs of deterioration, intervene promptly, and prevent adverse events.
- Quality improvement: Big data analytics can help healthcare organizations identify areas for quality improvement. By analyzing patient data, outcomes, and performance metrics, organizations can identify inefficiencies, reduce medical errors, and implement evidence-based practices, ultimately leading to improved patient outcomes and safety.
In the end, it is important to note that while big data holds great potential for improving patient outcomes, challenges related to data privacy, data quality, interoperability, and ethical considerations need to be addressed for its effective implementation in healthcare.
