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Big Data in Healthcare

Benefits of using Big Data in healthcare industry

by Svitla Team

September 13, 2018
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Everyday numerous new records of data are being generated in every aspect of our lives, and it is becoming increasingly challenging to collect and analyze all pieces of data effectively. 

The healthcare industry, which received over 4 trillion USD from the US alone in 2017, is finally leveraging technology to effectively manage and analyze that data.

Electronic health records and every bit of digital data relevant to the healthcare field is amassed in huge quantities, making it necessary to utilize technology and its tools take advantage of the available data.  That’s where Big Data comes into play.

Big Data in Healthcare

While several industries made immediate use of Big Data to further their goals and objectives, the healthcare industry is just beginning to access the many features and benefits that Big Data can bring into the healthcare field. 

Bringing healthcare up to speed with technological advances made via Big Data or Artificial Intelligence was virtually unheard of a decade or two ago. The groundbreaking advances being made in other industries made it impossible for the healthcare industry to look away.

While the vast potential of Big Data in healthcare is still untapped, the remarkable advancements being made are sufficient to prove just how interesting and powerful it can be. 

What is Big Data in Healthcare?

You may be wondering, what is Big Data in Healthcare? To put it simply, Big Data in healthcare is the act of analyzing large quantities of healthcare data collected from four data sources, as detailed in Wikipedia: “claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs), and patient behavior and sentiment data {patient behaviors and preference (retail purchases e.g. data captured in running stores)}.”

The essence of what Big Data can achieve in the healthcare industry is to uncover profound insights and improve operational efficiency.

The Big Data healthcare transformation is just beginning, and we are lucky to be alive at a time when we can fully experience just how much it can achieve and how far it can still go. Thus, it is increasingly important to understand how Big Data can further help obtain desirable results that improve the health industry. 

Despite the challenges of processing enormous quantities of data, new technological improvements are being made constantly with the help of Big Data, proving how it can be transformed into rich, useful, and actionable information.

The power of healthcare Big Data, and all types of Big Data, to be honest, lies in weeding out the noteworthy, important information from what can be discarded. As eloquently stated by Dr. Anil Jain for Fortune’s Brainstorm Health conference, “It’s not about the data, it’s about what you do with the data in terms of making sense of it.”

The sheer scale of healthcare Big Data that is generated on a daily basis is daunting so it is important to connect data with opportunities to best serve consumer needs.

Big Data is messy. It is comprised of structured and unstructured data. For instance, in healthcare, it encompasses information ranging from electronic medical records, imaging data, patient data, sensor data, to internet-connected devices; it is, therefore, necessary to break down healthcare Big Data into smaller pieces of information that can result in actionable insights.

Healthcare mobile apps

Nowadays, smartphones and other mobile devices act and feel like a personal assistant. With this in mind, it’s no wonder that all the important mobile device companies have embedded tools and features into devices to help users track their daily physical activities such as the number of steps walked throughout the day, the rate of heartbeats and other vital signs, the number of calories burned for running, walking, or using an elliptical trainer, and more.
It’s not entirely alien to think of a near future where your physician looks at your smartphone’s physical metrics to better understand your routines and your physical state.

Healthcare mobile apps

Mobile devices and applications are crucial to the Big Data healthcare revolution. Big Data applications in healthcare send all health-related digital information recorded by mobile devices to cloud servers, feeding into the Big Data databases to create reports and support data analysis, including recognizing trends. 

A person’s digital information can serve a greater purpose when it is compared and analyzed with thousands of other users, thus identifying threats, trends, and issues through patterns. This can result in a sophisticated predictive model backed by data from numerous patients with similar conditions, genetic factors, and lifestyle choices.

Healthcare mobile apps are intrinsic to devices designed to access Big Data healthcare information. Next, we will briefly illustrate some of the healthcare mobile apps that are already making waves in the mobile device industry:

The Triage is a patient-facing mobile app advises users on their medical condition and recommend medical care, all based on aggregated data and input made to the app.

CareAware Connect is a mobile app that manages clinical communications on a single device for teams that collaborate to improve care coordination. Users are able to view patient data such as vitals, measurements, allergies, and more.

MyChart is an app that allows patients to view health data from previous doctor visits. Users can see test results, medication, and health conditions as input by the care provider.

Healthtap is a website and mobile app that serves as an interactive health platform that originally began as a question-and-answer service to allow patients to send health questions to verified doctors. Now, the company offers services to include health tips, telemedicine, and physician reviews of specific medicines.

Big Data Databases

As previously stated, Big Data is so massive, including structured and unstructured data, that it is virtually impossible to process all information using a traditional or simple database. In most cases, the volume of data is so large or it moves so fast that it exceeds an enterprise processing capacity.

It’s safe to say that traditional relational database management systems are not up to the task of analyzing Big Data. In the face of this fact, Big Data Databases must handle the three V’s: Volume, Variety, and Velocity.

Volume: Big Data is measured in petabytes, exabytes, and even zettabytes. These colossal amounts of data require software techniques that distribute data appropriately.

Variety: Structured and unstructured data require a flexible data storage model to ensure all types can be stored and queried.

Velocity: Real-time information is expected to be consumed, stored and processed in near-real time. Big Data Databases are designed to capture all the accumulation of data without losing performance or speed.

databases

Over the years, two more V’s have emerged: Veracity and Value. Value refers to the intrinsic worth of data once it is unearthed from piles of available information. Veracity refers to the truthfulness and accuracy of data: its reliability.

Big Data Databases are frequently referred to as NoSQL databases since they don’t rely on the SQL query language. NoSQL databases and its management systems are critical players in Big Data analytics. The key categories of these types of databases include document, key/value, navigational, graph, event, content, big table, and time series, to name a few.

Another concept to consider is Big Data warehouses. These data warehouses are designed as data collection tools that amass data from different sources into a centralized repository, making it easier to categorize and analyze data. 

You may be wondering why you would use a data warehouse for healthcare analytics if you already have a robust database in place. To put it simply, data warehouses exist on top of other databases and extract information from them to create a repository solely destined to optimize and commit to analytics.

For healthcare analytics and with data collections continuing to expand, health data should be stored and analyzed in a database or databases. These databases should be collectively used to amass data from different sources, making it available in the centralized hub that is the data warehouse.

Healthcare Analytics

What is healthcare analytics? As detailed in Forbes, healthcare analytics help “enable the measurement and tracking of population health.”

Next, we are going to take a look at how Big Data analytics in healthcare offers insights on both a macro and micro level.

Big Data Analytics in Healthcare

At the forefront of everything, Big Data analytics in healthcare exist to keep patients healthy. 

This clear purpose helps shape and transform the meaning of Big Data analytics in healthcare. In essence, healthcare analytics focuses on offering insights into hospital management, patient records, costs, diagnoses, and more. 

Healthcare analytics power data-driven transformations that seamlessly combine financial and administrative data to aid patient care efforts, provide better services, and improve existing procedures.

Healthcare Predictive Analytics

Healthcare Predictive Analytics encompasses the use of all types of data, statistical processes and machine learning techniques to identify and provide an assessment of the probability of future outcomes based on data.

Healthcare Predictive Analytics plays a massive role as an agent to save lives and to help medical facilities reduce costs. Predictive analytics enable healthcare organizations to build and assess models to support preventive care.

Healthcare predictive analytics can help medical organizations identify and classify patients based on major health conditions. For example, patients who are at risk of a heart attack can be promptly notified of the threat, making it possible to treat at-risk patients in a timely manner and to provide customized care that addresses the specific illnesses.  

Another significant use of healthcare predictive analytics is in creating demand forecasts for medical supplies. If hospitals have reliable and accurate data, it is easier to estimate and predict demand and supply as well as shortages ahead of time. This saves time and money.

Healthcare Data Analytics

Healthcare Data Analytics leverages data to get ahead of chronic diseases, costly events, and uncertain outcomes for all types of patients. This impacts profoundly the overall public health.

Healthcare data analytics integrates real-time and historical data to power personalized and anticipatory experiences.

healthcare analyticsBenefits of Big Data in Healthcare

Using Big Data in healthcare has helped transform the industry towards a value-based model that delivers superior patient experiences, treatments, and outcomes.

Some of the benefits of Big Data healthcare that the industry has experienced are translated into terms of improved patient experience, prediction of epidemics, avoidance of preventable deaths, effective surveillance of public health, educated decision-making of policies, improvement of the quality of life, and more.

Next, we will describe some of the most prominent benefits of Big Data in healthcare to illustrate the achievements being made.

  • Healthy patients: Monitoring applications of vital signs to ensure a proactive approach to a person’s healthy state is monumental. For example, diabetes patients can track their insulin dosages, next medical appointments, and more.
  • Cost reduction: Big Data offers the ability to manage information and use it to drive cost improvements. With insights from Big Data analysis, healthcare organizations can identify areas where cost reductions can be made, whether related to admission rates, diagnostic tests, or operational procedures.
  • Error minimization and precise treatments: Big Data in healthcare enables providers to deliver more accurate and personalized care treatment. By having a detailed picture of patients, it is easier to predict the response to a specific treatment.
  • Prevention services: Preventive care to provide services more efficiently, optimize operations, and improve the prevention of medical risks. For example, the Apple Watch is being refined to determine if the embedded sensor of the watch can help detect atrial fibrillation. If found true, this could be groundbreaking in helping users seek timely medical attention.
  • Streamline hospital operations: with data being generated at breakneck speed, hospitals have the demanding role of managing the operational aspects of the facility. For example, Big Data analytics helps track staffing metrics while predictive analytics is able to enhance billing efforts.

Big Data Modeling

The straightforward analogy for Big Data Modeling is a library. In a library, all books must be arranged and categorized to make every book easily accessible. Similarly, immense quantities of data must be sorted and stored appropriately, which is what is referred to as Big Data Modeling. As depicted by DZone, data models offer performance, cost reduction, efficiency, and quality.

The Big Data Modeling frameworks provide definition and format to data structures. Techniques and methodologies should be used to manage data in a standardized, consistent, and predictable manner to achieve the following:

  • Assist clients and organizations understand and use the model.
  • Manage data as a resource.
  • Integrate information systems.
  • Design databases/data warehouses.

Big Data Modeling is vital for Data Scientists who use scientific methods, processes, and systems to extract knowledge from data. These analytical data experts are technically skilled in collecting large amounts of data and transforming it into usable insights that solve a problem. 

Data science in healthcare makes it easier to achieve the goal of maintaining high-quality patient service standards by pairing highly capable data scientists who understand medical data and variables, with efficient Big Data services to the greater purpose of improved health outcomes.

Big Data Challenges in Healthcare

These are some examples of the present Big Data challenges in healthcare:

Size. One of the biggest challenges with Big Data is related to its extraordinary size. As we’ve explained before, Big Data is big. Really big. Big Data healthcare companies struggle to keep up with data and find effective ways to analyze e it and store it.

Valuable data. With these large quantities of data, quality must be ensured. Data is curated or cleaned to make it relevant and organized in a way that fosters analysis. Data must be captured in a clean, complete, and accurate way to facilitate analysis of indispensable information about specific health-related matters such as disease progression, therapeutic effectiveness, and patient outcomes.

Data security. Data security is the top priority for healthcare organizations. HIPPA enforces security rules that must be covered to protect data and safeguard electronic health information. These safeguard measures and rules include transmission security, authentication protocols, and control over access.

Aggregating and updating data. Data is not a static entity, but rather a volatile being. With many elements in place, healthcare data must be periodically updated so the most recent and accurate information is always at hand. Additionally, data comes from many sources, which is another challenge for organizations. Aggregating data that is spread across multiple sources helps healthcare organizations pull and arrange all data into a more collaborative space.

big data

Conclusion

Data is more available than ever before. With mobile devices, smartphones, wearables, and the thundering rise of the internet, the quantity/quality of data offers a fountain of potential for the healthcare industry.

Discovering the meaning behind data is the most important aspect of Big Data analytics in healthcare. Overall, Big Data makes it possible to gain knowledge and confidence in the data.

As you read in this article, Big Data in healthcare is on the brink of invaluable contributions to the future of the healthcare industry. From predicting health outcomes, curing chronic or terminal diseases, to making patient care more effective, Big Data is now a familiar companion to the healthcare industry.

by Svitla Team
September 13, 2018

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