Why is Big Data Testing Important for the Healthcare Industry?

Big data analytics is enabling healthcare providers and insurers to deliver greater patient satisfaction through life-saving care. The healthcare industry produces massive amounts of data for patient care, record keeping and compliance & regulatory requirements. Thus, big data can support multiple medical and healthcare functions including health management, disease monitoring and clinical decision support, among others.?

However, with such massive amounts of data to process, big data systems may be prone to defects that can be identified only through advanced big data testing. Here's why big data service providers in the healthcare industries, need to employ quality engineering experts to ensure their systems are flawless as lives depend on the same.

Data-driven healthcare?

Healthcare data analysis opportunities are increasing with growing healthcare data. It can help solve several raging healthcare issues including healthcare prediction, disease prevention and enable personalized healthcare support.?

It can also help healthcare organizations become operationally efficient, manage expenses better and deliver services in a targeted manner. It can help patients maintain their health data through easily accessible applications and wearables. For instance, connected glucose monitors track blood sugar levels and suggest required medication based on the collected data.?

Patients get better treatment results when they are more engaged in the treatment. Smart use of sensors also support healthcare organizations deliver more efficient digital therapeutic solutions.?

Challenges for healthcare data sharing?

Data related to a person’s physical or mental health is protected under the General Data Protection Regulation (GDPR). To avoid any data breach and illegal data use, healthcare organizations need to comply strictly with data sharing rules. Sometimes, it affects the data flow within the organization.?

End-of-life (EOL) software and infrastructure provides a healthcare data security risk as vendors discontinue support for your IT systems, including vital security patches. While facing the situation and purchasing a brand-new server can be tough on the budget, it’s easier than dealing with the fallout of a data breach.?

?Across the nation, healthcare providers are grappling with how to incorporate state-of-the-art technologies into their practices without violating HIPAA or putting patients at risk. One solution against the growing threat is data encryption.?

Big data testing in healthcare?

Avoiding big data testing can become counterproductive for the healthcare business. Robust big data testing can ensure legitimate data usage under HIPAA compliance and keep healthcare data accessible and secure from hackers.?

Big data testing of healthcare applications is complex. It is executed at three levels including data collection, integration and deployment. Test engineers need to keep a keen eye on the variety, volume, and velocity of big data to ensure flawless and secure user experience.?

Big data scalability?

The amount of healthcare data stored in databases is increasing at a rapid pace. Quality assurance (QA) teams need to validate the data on a regular basis to ensure it is accurate and relevant to the business objective of the healthcare organization.?

Test engineers can run automated scripts to identify and fix any flaws in big applications.?

Abrupt increase in workload can also hamper accessibility, processing and networking capability. By leveraging Cluster and data partitioning techniques, performance bottlenecks of big data can be identified and fixed. It will help you achieve real-time scalability without any major performance bottlenecks. ?

Efficient health monitoring?

Big data testing can enable healthcare professionals to easily track vital information of multiple patients and users. It also helps patients to easily track common stats including glucose level, heart rate and sleep rate. It also alerts the patient if any medical attention is required immediately. ?

Optimized operational costs?

Big data can leverage predictive analysis to help you forecast future medical fees, operational resources’ requirements and expected number of patients. Healthcare organizations can leverage this information to become future-ready by planning the resources cost-efficiently and managing the surge in patients in a better manner.?

Telehealth?

As an aftermath of COVID, Telehealth is more personalized and convenient for many patients as it is almost synonymous with doctor’s home visits. Big data can make the interaction between patients and telehealth providers more effective.?

Big data can enable healthcare providers to link up healthcare apps to monitor and track patient health data. Data points can be transmitted directly through wearable apps to track multiple vitals including breath rate, heart rate, diet and more.?

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Types of big data testing?

Every healthcare software enabled must undergo proper big data testing. Big data systems are also vulnerable to defects and flaws, which need to be monitored and inspected to rule out chances of errors. Here are the major big data testing types:?

Architecture testing: This type of testing ensures that the processing of data is proper and meets the business requirements. And, if the architecture is improper then it might result in performance degradation due to which the processing of data may be interrupted, and loss of data may occur. Hence, architectural testing is vital to ensure the success of your big data project.?

Database testing: As the name suggests, this testing process typically involves the validation of data gathered from various databases. It verifies the data extracted from cloud sources or local databases that are correct and proper.?

Performance testing: It is for checking the load time and processing speed to ensure stable performance of big data applications. This testing type helps check the velocity of the data coming from various databases and data warehouses as an output known as IOPS (Input Output Per Second). Further, it validates the core big data application functionality under heavy load by running different test scenarios.?

Functional testing: Big data applications encompassing operational and analytical parts involve thorough functional testing at the API level. It includes tests for all the sub-components, scripts, programs, & tools used for storing or loading and processing applications.?

Wrapping Up?

As healthcare organizations expedite the adoption of digital transformation across the world, the need to become data compliant and data-driven is becoming business-critical. Big data testing can help healthcare organizations make sense of heaps of data generated on a regular basis efficiently. It can also help healthcare providers become more customer-centric and future-ready through predictive analytics and forecasting. Finally, big data testing can keep healthcare organizations compliant and immune from expensive lawsuits.?

Talk to experts at Qualitest to make the first move towards robust big data testing.?