The healthcare industry is abuzz with the Big Data revolution. What is Big Data in healthcare? In simple terms, Big Data is the analysis of large quantities of healthcare data collected from multiple sources.
The healthcare industry generates new data records every day in huge quantities. This overflow of information requires the industry to use technology to take advantage of the available data. That’s where Big Data comes into play.
As the healthcare industry begins to tap into the many features and benefits rendered with Big Data, it’s clear that there’s still a long road ahead. There is a notable expansion in technological advances that prove just how interesting and powerful the partnering of healthcare with Big Data can be.
Big Data is messy. It’s comprised of structured and unstructured data, and in healthcare, this encompasses information that spans electronic medical records, imaging data, patient data, sensor data, to internet-connected devices. Thus, it is necessary to break down healthcare Big Data into smaller pieces of information that can then become actionable insights.
Mobile apps for healthcare
At a time where mobile devices and smartphones act and feel like personal assistants, companies embed tools and features that help users track daily activities such as the number of steps walked during a day, vital signs, number of calories burned, and more.
It’s not crazy to think of a near future where your physician looks at your smartphone’s physical metrics to understand your physical state and give a diagnosis.
A person’s digital information has the power to serve a greater purpose by comparing and analyzing it against thousands of other users to identify threats, trends and issues. This can lead to a predictive model from numerous patients with similar conditions, genetic factors, and lifestyle choices.
Some of the most popular mobile apps, such as Triage, CareAware Connect, or Healthtap, rely on Big Data.
Databases for Big Data
Big Data’s massive size is nearly impossible to process with a traditional or simple database. A capable Big Data database must cover the three V’s: Volume, Variety, and Velocity.
- Volume: Database software techniques to handle large quantities of data and distribute it appropriately.
- Variety: Flexible data storage models to ensure all types of data can be stored and queried.
- Velocity: Real-time consumption, storage and processing of data without losing performance or speed.
In recent years, two more V’s have emerged: Value and Veracity. Value refers to the data’s worth as it is unearthed from piles of available information. Veracity refers to the accuracy of data and its reliability.
Big Data databases are frequently referred to as NoSQL databases since they don’t rely on SQL query language. These databases are key players in Big Data analytics and include categories such as document, key/value, graph, event, content, and more.
Big Data warehouses are data collection tools that amass data from different sources into a centralized repository, making it easier to categorize and analyze data. Data warehouses exist on top of other databases and extract information from them to optimize analytics.
Healthcare analytics
Healthcare data analytics measure and track the health of the population and its main purpose is to keep patients healthy as it focuses on offering insights about hospital management, patient records, costs, diagnoses, and more.
Healthcare predictive analytics uses all types of data, statistical processes, and machine learning techniques to identify and provide an assessment about future outcomes based on data. Predictive models support preventive care by identifying and classifying patients based on major health conditions.
Advantages of Big Data in healthcare
These are some of the most prominent benefits of Big Data in healthcare:
- Healthy patients: Applications that monitor a person’s vital signs to take a proactive approach for a healthy state.
- Cost reduction: Information management to drive cost improvements by identifying areas where costs can be reduced.
- Error minimization and precise treatments: Ability to deliver more accurate and personalized care treatment with the use of Big Data.
- Prevention services: Preventive care to provide efficient services, optimized operations, and improved prevention of medical risks.
- Streamlined hospital operations: Ability to track and manage operational aspects of a hospital’s activities.
Challenges of Big Data in healthcare
Now, let’s take a look at some challenges present in Big Data healthcare:
- Size: Companies struggle to keep up with the volume of data in terms of analysis and storage.
- Value: Data must be curated, cleaned and organized to ensure it is valuable for analysis.
- Security: Data is exposed to leaks and breaches, which is why it must be protected and guarded to avoid risks of data exposure.
- Aggregation: Data must be periodically aggregated and updated to ensure the most recent and accurate information is available for analysis.
To wrap things up
Discovering the meaning behind data is the most important aspect of Big Data analytics in healthcare. The value of data gives birth to groundbreaking contributions for the future of the healthcare industry.
From predicting health outcomes, curing chronic or terminal diseases, to making patient care more effective, Big Data has become a familiar companion to the healthcare industry.
