Big Data in Healthcare: Everything You Need to Know

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BigDataScalability.com – At glance, the term “big data” might sound like a buzzword that is often thrown around. The truth is, that isn’t true today. If anything, big data is very likely to be a part of our future. We can see the trend as more and more organizations across various sectors use big data, including the healthcare sector.

Big data has a long history. Back then, the term refers to a big amount of data. Today, it is much more than that. In this post, we explain what big data is, big data in healthcare, its characteristics, why it is important, why use it, its applications in the healthcare sector, and its benefits.

What Is Big Data?

What-Is-Big-Data

Before we delve further, let’s get to know what big data is. In general, big data is defined as a large set of complex data, whether structured, semi-structured, or unstructured, that can be used to gain deep insights as well as solve problems that couldn’t be solved using traditional software or analytics.

Usually, data scientists utilize artificial intelligence (AI) -powered analytics to evaluate big data to discover trends and patterns that can provide meaningful insights.

Big data is projected to penetrate deeper and faster into the healthcare sector than in financial services, manufacturing, and media. This is hardly surprising. The healthcare sector is the top private employer in the US. Moreover, the sector’s spending accounts for a fifth of the nation’s GDP.

Globally, the market of healthcare big data is expected to experience an impressive compound annual growth rate of 36% through 2025.

Moreover, due to the rise of investments in electronic health record systems, practice management solutions, and workforce management tools, the global healthcare big data analytics sector is expected to be worth no less than $68 billion by 2024.

What Is Big Data in Healthcare?

What-Is-Big-Data-in-Healthcare

The term big data in healthcare can be defined as the use of analytics services, whether prescriptive, predictive, or descriptive, to gain deep insights from healthcare data.

Some others define big data as the vast amount of data that is too large and/or complex for conventional technology to make sense of. The vast amount of data is created by the widespread adoption of the internet and digitization of information, which includes health records.

Previously, the problem has been figuring out how to collect all that data and analyze it to gain actionable insights.

As big data technologies emerge, healthcare organizations are now able to not just consolidate but also analyze such a vast amount of data in order to find trends, better treat patients, as well as make more accurate predictions.

There are three main goals of big data in healthcare:

  • To improve clinical outcomes
  • To boost workforce productivity
  • To improve the healthcare organization’s revenue stream

These goals are achieved by using patient data, leveraging operational data, and using healthcare financial data, respectively.

The “V”s of Big Data in Healthcare Sector

The-Vs-of-Big-Data-in-Healthcare-Sector

Initially, big data is associated with three key qualities. These key qualities are volume, variety, and velocity. However, it continues to grow and change. As a result, industry experts add another two “V”s, which are value and veracity, to describe its characteristics.

Below we explain each “V” of big data in healthcare.

1. Volume

Volume is the quantity or amount of data. The advance in technology has made it possible for such a remarkable volume of data to flow to and from websites, applications, portals, as well as EHRs.

2. Variety

There is also variety, which encompasses different forms or types of data that we can generate, gather, and analyze.

Structured, semi-structured, or unstructured data can include everything from facts, numbers, and statistics to text, photos, as well as videos.

3. Velocity

The next “V” is velocity, which is the measure of how fast information or data is flowing. It refers to how fast datasets are being generated and processed. The velocity of data affects healthcare organizations and their ability to make accurate and timely decisions.

Thanks to the Internet of Things, cloud computing, and machine learning, data flows in real-time. As a result, data can be available in an instant.

4. Value

Value is the latest “V”s, along with veracity. This term refers to the usefulness of the data and what’s done with said data to make it worth something. It demonstrates the return on investment of the data being generated, collected, or analyzed.

5. Veracity

Lastly, veracity. Veracity is the quality, integrity, or trustworthiness of data that is generated, collected, and analyzed by healthcare organizations.

Note that data is coming in different types from different sources. If we want to draw reliable conclusions from it, we need to make sure of its quality, integrity, and trustworthiness.

Why Is It Important?

Why-Is-It-Important

There are many reasons why big data is important in healthcare. For example,

Patient care is becoming increasingly complex. Without proper analytics, providing safe and quality patient care is difficult.

Operational inefficiencies, such as records duplication, financial benchmarking difficulties, and missing entitled reimbursements, are caused by inadequate data governance. This is a problem that big data can fix.

Discrepancies between clinical and accounting departments. Unfortunately, this is a common problem for many healthcare organizations. The root causes of these problems are data mismatches and inaccuracies. Again, big data can fix this.

Why Use Big Data?

Why-Use-Big-Data

1. Tracks and prevents care

In the US, the annual cost of delivering healthcare has reached over $3 trillion. This is where big data in healthcare can help. Combined with other health technologies, big data can help healthcare providers track and identify diseases long before they happen. Thus, boosting preventive care.

2. Reduces costs for healthcare providers

The US healthcare costs account for approx. 16.7% of the nation’s GDP. Although the number is hardly surprising, the costs for healthcare providers can be reduced by utilizing big data.

The vast amount of data puts healthcare providers and executives in a better position to make better operational and financial decisions and, at the same time, provide quality and enriched patient care.

For example, using big data, healthcare providers can predict patient booking and minimize financial waste, thereby optimizing staff allocation and thus, reducing costs.

3. Prevents human errors

Human errors are common in healthcare services. It is estimated that human error accounts for approx. 6% of a healthcare provider’s expenses. There are also human errors in prescription dosage, which put a patient’s health and overall well-being at risk. Using big data, these human errors can be prevented.

Big Data Applications in Healthcare

Big-Data-Applications-in-Healthcare

Big data has various applications in any business environment. In healthcare, the applications of big data include

1. Mitigate hospitalization risks

Healthcare organizations can use big data in healthcare to mitigate hospitalization risks. Using big data and healthcare analytics, healthcare organizations are better equipped to track hospitalization risks for patients with chronic diseases.

By analyzing criteria such as the frequency of doctor visits, medication intake, and symptom security, clinics can provide patients with targeted preventive care to reduce hospital admissions.

Using big data, hospitals can predict who might be admitted. This allows hospitals to be prepared well in advance as they can allocate space and necessary resources to prospective patients. 

For example, during the COVID-19 pandemic, doctors are utilizing big data in healthcare to collect data from every part of the nation to pinpoint vulnerable spots that are at the greatest risk.

2. Improve patient outcomes

Another application of big data in healthcare is to improve patient outcomes. Combined with analytic services, big data makes it easy for researchers and clinical practitioners alike to better diagnose and treat diseases.

By analyzing a vast amount of health data, clinicians and doctors can focus on otherwise rare and difficult-to-diagnose diseases, thereby improving patient outcomes.

3. Product development

Developing and designing new drugs and other health products is not only incredibly time-consuming but also expensive. The use of big data in healthcare can help to reduce the time required for product development.

R&Ds deal with a huge amount of data. Using big data, R&Ds can focus on the right data. This reduces product development time.

In addition, the product development process involves lots of trials and errors. Big data in healthcare helps R&Ds to remove hassles and most importantly, guesswork from the process. As a result, R&Ds can develop better, more precise health products.

There is also real-time data analytics. These help healthcare organizations to refine their products based on the datasets.

4. Preventive maintenance

Healthcare organizations can utilize big data in preventive maintenance of their equipment, which include medical and health tech equipment, as well as digital assets such as websites and apps. The latter is particularly important in the digital age where data cybersecurity breaches are common.

With big data, healthcare organizations can become well-informed regarding preventive maintenance needed to keep their pieces of equipment operating as they should.

5. Operational efficiency

Collecting and analyzing workforce data can help healthcare organizations like hospitals and pharmaceutical companies improve productivity.

Big data in healthcare will help healthcare organizations redirect resources efficiently, redesign workflows, and improve the organization’s operational efficiency as a whole.

6. Drive innovation

Last but not least, big data in healthcare drives innovation. Innovation is undoubtedly important in the healthcare sector. It is what improves patient outcomes and care, drives drug discovery, and so on. Big data can be used in several ways to help drive innovation.

For example, patient care can be paired with predictive healthcare data analytics, preventing and diagnosing cardiovascular diseases, as well as creating tailored therapies and drugs for rare and complex diseases.

The Benefits of Big Data in Healthcare

The-Benefits-of-Big-Data-in-Healthcare

Now let’s talk about the benefits. Who benefits from big data in healthcare? Virtually all aspects of healthcare are benefitted from big data. The biggest winners are

1. Patients

Patients are the main winners in a data-driven healthcare environment. In such an environment, patients can get predictive care, superior health management, healthier lives, as well as savings in insurance.

2. Providers

Health providers such as clinics and hospitals are also benefitted from big data in healthcare. Insights gained from big data will help healthcare providers deliver better patient outcomes, apply efficient workflow, and reduce wastage.

3. Payers

Payers, like insurance companies, can benefit from using big data in several ways. Using big data can reduce improper and false claims, fraud elimination, better service, and faster reconciliation.

4. Pharmaceutical companies

Using big data, pharmaceutical companies can have better R&D, savings on developing drugs, and more effective and innovative drugs.

5. Device manufacturers

Data analytics help device manufacturers build devices that are relevant to patients’ needs and create better, more innovative products that solve health issues.

Big data plays a crucial role in healthcare. The use of big data in healthcare brings a myriad of benefits for everyone involved in the industry. Including healthcare providers, payers or insurance companies, pharmaceutical companies, device manufacturers, and most importantly, patients.

It has the potential to transform the sector from top to bottom. For healthcare organizations, it is a good idea to bet on big data to improve patient outcomes, build efficiency across departments, as well as save costs.

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