Big Data Challenges and Solutions in Healthcare – 2022

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  • Feb 23, 2022
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Big Data Challenges and Solutions in Healthcare 2022

Big data has become one of the keys to improving the operational processes of modern healthcare organizations. Healthcare institutions have started using the power of data to derive actionable insights from patient records.

Along with implementing data-driven strategies, healthcare firms face challenges like capturing data, consolidating it, and ensuring data privacy. But using solutions like predictive analysis, cloud data lakes, and hiring skilled professionals, they can tackle these problems.

This article will talk about significant big data challenges in healthcare and their solutions to enable medical facilities to enhance patient care.

Collecting Authentic DataCollecting Authentic Data


Medical data is usually collected from multiple sources such as electronic health records, patient documents, scanned images, and medical databases. But this collection is one of the primary big data challenges in healthcare.

Most of this information is available in various formats and isn’t necessarily structured. Sometimes, data is fetched from multiple departments within a medical facility. All this makes capturing and consolidating authentic information difficult.

As a result, doctors can’t improve their treatment plans or understand more about their patients.

Poor data capturing also leads to problems in diagnosis and can affect a patient’s well-being. Although the data is collected from various sources, the efforts for maintaining accuracy are not strong enough.


Professionals collecting the data can define data types and formats for specific medical projects. For instance, basic patient information can be stored using an excel file.

They can implement predictive analysis with the available data to create dashboards representing a patient’s journey. It can map all information from the patient’s admission to their discharge from the hospital.

Cleansing Data

Cleansing DataSource

Proper analysis and data-driven treatment can only be possible with clean data. So, along with maintaining hygiene in hospital premises, medical data cleanliness also requires attention.

As uncleansed data can hamper a medical data analysis attempt or the development of a treatment procedure, data cleansing is an absolute must.

Redundant medical data can negatively impact diagnosis and patient care. Moreover, doctors might not track a patient’s progress effectively, as they won’t have the appropriate data to refer to.

The need for sophisticated tools has included data cleaning amongst the big data challenges in healthcare.


Cleansing will remove redundant data, multiple data formats, or incorrect data. Medical institutes can contact IT organizations that offer scrubbing tools. These high-end cleansing tools can fix inaccurate, corrupt, and duplicate data from various Healthcare databases.

Artificial Intelligence and machine learning techniques will become more popular in cleaning hospital data effectively while maintaining high accuracy levels.

Storing Healthcare Data Storing Healthcare Data


When talking about big data challenges in healthcare, you can ignore data storage.

As more patients get admitted, the associated patient data volume increases. Usually, hospitals store most of this data on on premise storage systems, which are convenient to handle.

But, as the number of cases, patient admissions, and medical procedures increase, this data spikes up exponentially. Then, it gets extremely difficult and expensive to store the information properly on these storage systems.

On-premise systems are also trickier to scale and maintain, which eventually increases the data storage expenses for the facility.


Opting for cloud storage systems is a perfect solution to handle this situation. Many healthcare firms have realized this, so almost 83% of the industry uses cloud storage nowadays. These systems provide professionals more control over the medical data along with the following advantages –

  • Better scalability of the infrastructure according to patient inflow
  • Remote access to critical patient information via mobile devices
  • Decreased expenses for maintenance and storage

Another excellent option is a hybrid cloud approach, where both on-premise and cloud storage systems will accumulate data. However, data sharing needs to be convenient between all systems.

Ensuring Data Privacy

Ensuring Data PrivacySource

You will be surprised to know that the average number of data hacks were very approximately 1.76 per day in 2020. And it cost almost $6 trillion for healthcare institutions to compensate for these breaches.

That’s why data security is one of the top big data challenges in healthcare to date.

It happens because there’s such a huge amount of sensitive information but no full-proof data privacy system. As data breaches have become more advanced, antivirus software, multi-factor authentication, and secret encryption might not be enough.


The first step in securing sensitive patient information is to follow the HIPAA security measures and communicate the same to all employees. Using advanced antivirus software and firewalls will provide basic support.

Cloud data lakes can provide excellent data security and convenient access to data from various sources. But, the primary strategy must be educating all the staff on data privacy techniques. It might include the following –

  • Password proofing laptops and desktops
  • Not leaving unprotected laptops having sensitive information anywhere
  • Avoiding unreliable emails or text messages that have a link to click on
  • Not sharing passwords with other employees

Lack of Appropriate Staff

To handle the big data challenges in healthcare, any medical institution will require the appropriate workforce having a background in IT or Computer Science. But at present, most medical facilities lack skilled tech professionals and cannot tackle big data issues.


The straightforward solution is to hire more technical professionals for healthcare organizations. They might include data analysts, cyber security professionals, and big data experts. It will be a great move to create a team of these professionals and handle the entire IT infrastructure.

Along with this step, hospitals can also start basic technical training programs for their employees. These can include data security fundamentals and data analysis basics.

Parting Thoughts

If the big data challenges in healthcare are appropriately taken care of, data can empower the future of medical science. As a professional, you can utilize the power of big data to determine a patient’s complications enhance the existing treatment plans for their benefit.

As the great Pat Gelsinger said, “Data is the new science. Big Data holds the answers.” It’s time more healthcare firms focused on leveraging it.

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Sam Wilson, Consultant