Top Five Challenges Of Big Data Analytics In The Healthcare Industry

Big data for healthcare has the potential to revamp the healthcare delivery process and inform providers to create the most efficient and effective treatment pathways. Value-based buying programs encourage both healthcare providers and insurers to explore new ways to leverage healthcare data to measure care quality and effectiveness. The use of analytics in information on healthcare poses a variety of challenging obstacles, but also ample opportunities.

Here is a list of challenges that are expected to affect the growth of big data healthcare analytics.

1) DATA CAPTURING

Various medical organizations collect data, but not every company has accurate data collection. Getting specific, clear, and consistently organized information that can easily be used in digital systems is the problem, or I'd say the real battle that most companies are going through. Sadly, most of them cannot cope with this challenge.

Weak functionality of electronic health record, complex workflows, and poor awareness of why big data healthcare analytics are necessary to capture data will bother data during its complete life cycle. Nonetheless, to address this, providers need to fine-tune their data capture procedures, prioritize valuable data, hire experts in data governance, and train clinicians on how to use the data for analytics.

2) DATA CLEANING

Data consistency is the bottleneck for any project to moving to big data healthcare analytics. It's enough to ruin your project with irrelevant data. Irrelevant data means data that is incorrect or in different units that can produce tragic results when combined together. That's why there is a need for data cleaning. In cleaning, it is ensured that all the data collected from different sources are reliable, consistent and relevant.

Cleanliness is of utmost importance in big data analytics. Ultimately, if it's irrelevant, the data may derail the project. And so, it is necessary to perform manual cleaning by IT vendors who can compare, contrast and correct big data sets. This is the only way to achieve reliability with dignity.

3) DATA STORAGE

Small clinicians have very little idea of where and how to store their data and what it takes to safely store them without affecting performance. In fact, this is a challenge for the IT department. Managing the exponentially increasing healthcare data in small data centers with less than good infrastructure is becoming difficult.

At the moment, the way to go seems to be a hybrid approach to data storage. It is very flexible, functional and provides varying storage and access to data. However, the main problem is to ensure that the systems communicate well and share data whenever necessary.

4) DATA SECURITY

Given the widespread cases of high-profile data breaches, hacking, and ransomware, the processing is not sufficient. The HIPAA Security Rule contains a long list of ways to protect Protected Health Information (PHI), including security during transmission, authentication, and access, integrity, and audit regulation. Unfortunately, using upgraded antivirus software, providing a firewall, encrypting data and using multi-factor authentication does little to dissuade vulnerabilities faced by data centers. And that's why staff members of a health organization need to be continuously informed of the need for data protection to ensure continued security.

5) DATA STEWARDSHIP

Data is often stored for a more extended period in the healthcare industry, so that it is available for research purposes when necessary to trace back to patient-related concerns. Stored data can also be used for quality analysis, which explains the importance of continuous curing and stewardship of stored data.

Not only to develop complete and accurate metadata but also to keep it up-to-date is an essential component of a good data management plan. A steward will be responsible for assigning, updating and ensuring that they are evergreen, generic definitions and formats.