How to implement Big Data Testing in Healthcare?

One of the most potential benefits of Big Data Analytics in Healthcare is it helps insurers and healthcare professionals provide the best treatment and life-saving care. When it comes to assessing the validity and accuracy of claims, Healthcare insurers walk a fine line - risking disgruntled fair health plan members in the event of an underappreciated evaluation, or suffers much due to overpayment or payments for fraudulent claims.  

The healthcare industry has produced substantial volumes of data, driven by compliance & regulatory requirements, record keeping, and patient care. Though, the majority of data is stored in hard copies. However, the current trend allows us to rapidly digitize these enormous amounts of data, powered by mandatory criteria and the potential to enhance the quality of healthcare delivery while lowering costs. These gigantic volumes of data are defined as ‘Big Data’ and hold a promise of supporting a wide array of healthcare and medical functions, including disease monitoring, clinical decision support, and population health management.   

No doubt, digital data is transforming our understanding of patients and population health. It can solve many challenges that the industry is currently facing. Nonetheless, this industry needs to ensure data privacy while developing new business models that share data.   

Data-Driven Healthcare   

The healthcare industry has broad issues to resolve at a time when its resources are limited—Digital data is perhaps the key to finding many solutions. For example, it may assist physicians or doctors find better treatments for chronic ailments, which account for roughly 75% of total U.S health care spending. Data may also help provide more efficient care in the face of reducing numbers of health providers. The American Medical Association predicts a shortfall of between 46,900 and 121,900 doctors by 2032 in the U.S.  

There are numerous kinds of healthcare data generated inside and outside of the healthcare industry. More healthcare data comes with more data analysis opportunities to solve the major industry’s problems. Growing evidence stipulates that, when utilized logically, data analysis can help improve disease prevention, health care prediction, and personalization of health care. It is also beneficial for operational design and expense management and promises to make service provision more efficient and targeted. The industry is already having lots of high-quality data about service provision and patients. Meanwhile, patients have the ease to create and maintain their health data through easily accessible wearable consumer technology.   

At the small level, data is also collected by community organizations and governments to acquire a detailed understanding of how population demographic and environmental factors affect health. Moreover, major pharmaceutical companies, such as Rocho and Novo Nordisk, Eli Lilly have great business relationships with digital companies for diabetes management solutions. Connected glucose monitors assist diabetic patients in tracking their blood sugar levels over time. Based on tracking, they obtain the necessary medication and experience positive outcomes.   

Data can benefit the healthcare sector in a variety of ways. Patients’ health outcomes will directly benefit from data. According to the recent Ernst & Young report pegged the value of data in the UK’s National Health Service at a mind-boggling 9.6 billion pounds annually. Personalized medicine, population health, and disease management are key areas. Research exhibits that treatment results become positive when patients are fully engaged in their disease treatment journey. The advanced sensor technologies and the ubiquitous use of sensors support healthcare companies for drug augmentation with a portfolio of wraparound services and digital therapeutic solutions.   

Barriers to Sharing Data   

• Lack of understanding/knowledge about how to access data.   
• Lack of knowledge where data exist.   
• Data is available in incompatible formats.   
• Data is not in electronic form.   
• Only aggregated data is available.   
• Approval process required for accessing data or sharing data.   
• Fear of misuse or misinterpretation of data.   
• Frequency of data release.   
• Privacy and confidentiality concerns.   
• Policies, including state and federal laws that limit access.   
• Limited staff or resources.   
• Technical Incapability.   
• Inaccurate or incomplete dataset.   
• Poor meta data.   
• Poor understanding of different health data types (School, Cancer, Hospital Discharge, Developmental Disability/Autism, Medicaid/Medicare/Other Insurance, Labs, Industrial/Occupational, etc.).   

The most notable barrier to the holistic analysis of health care data has to do with guaranteeing trust, security, and privacy. Patients will be happy if they receive improved health results. However, the main concern is that data can be misused to bias against people with health problems. The fear of cybersecurity breaches makes it difficult for patients to put trust in healthcare industries. For example, in May 2017, the NHS was attacked by WannaCry ransomware. During that time, many companies had to shut down their non-emergency operations, and they were not able to access critical medical records. Cyber-attacks can be expensive. Health-care is a highly popular and regulated company, and data breaches can cost many more millions of dollars or pounds to this sector. The ransomware cyber-attack cost the NHS about 92 million pounds.   

Resolutions to Barriers  

• Formal Agreements – Define the scope of use through data-sharing agreements, trading partner agreements, confidentiality agreements, IRB, MOUs, permissions.   
• IT – Technical Solutions.   
• Collaboration – Communication between agencies and/or individuals, diplomacy, working with a liaison.   
• Staff Resources –Utilization of time and effort by staff to prepare the data for use.   

 The Time to Take Action   

Big Data Testing in Healthcare Supporting Correct Implementation   

The notion of Big Data revolves around the ‘V’ Method. Let’s understand this before we deep dive into the testing services using big data methodologies.   

Velocity: Every second, minute and hours of test data is being planned, drafted, executed, recorded, and processed. The pace at which data gets produced is unfathomable.   

Volume: About 2 bytes of test data may generate each day, which is equal to 40000000GB of data.  

Variety: Data may produce with the help of different kinds of test data such as security testing, performance testing, functional testing, and so on.   

Veracity: The test data which is assembled from several sources can be in a structured or unstructured format. This data needs to be analyzed, categorized, and visualized to take further advantages in business.   

Value: After making the data in a fully structured and streamlined form, one can derive a tremendous value from this Big Data.   

Major Areas Where Big Data Projects Require Testing  

• Functional Testing  
• Infrastructure Testing  
• Database Testing
• Performance Testing 

Big Data is referred to as a broad set of data (structured or unstructured). Data may exist in numerous forms like images, videos, and flat files. In Big Data characteristics, Volume defines the size of the data obtained from different sources like transactions, sensors, and velocity that is known as the speed. At the same time, variety describes the formats of data.   

 
Must-Know Steps for Big Data Testing   

Execute Live Integration: It is requisite to consider Live Integration as data is obtained from several sources/resources. You should also need to perform end-to-end testing while integrating the live data. 

Data Validation: Data is needed to confirm into Hadoop Distributed File System, which may help you compare sourced data with the added data.   

Process Validation: After comparing the source data with newly added data, the next step is to validate this process, including Business Logic Validation, MapReduce Validation, Data Aggregation and Segregation.   

Output Validation: In this phase, corrupted data needs to be eliminated. Other areas that you should focus on – HDFS data comparison with targeted data, successful data loading, and data integrity maintenance.  

Why is Big Data Testing Necessary for Healthcare?   

 Big Data Testing plays a crucial role in Big Data System. While avoiding testing of Big Data Systems, your business can suffer a lot, and you may find it challenging to understand the roots of failure or errors. It can lead to reputational and financial losses. The solution to most Big Data Problems is Testing because it will help you avoid the unnecessary wastage of resources in the future.   

Benefits   

Easy Health Tracking/Monitoring  

With the effective use of Big Data and Analytics in the Healthcare Sector, professionals can easily track the statistics and vital information of different service users and patients. It allows the monitoring of patients in a more comfortable way. Patients can quickly track their activities – sleep rate, heart rate, glucose level and even get medical assistance if they’ve found something wrong or any silent sign regarding their health may be in trouble. Big Data Testing allows for a more pro-active, forward-thinking, and decision-making healthcare industry.   

Cost Reduction   

The price of healthcare depends upon whether they come under public sectors/governments or they are private. While offering effective medical treatment, many hospitals find themselves in panic while doing hard-work and too much struggle. However, Big Data use predictive analysis to estimate admission fees/rates, and it even helps you know the counts of your staff that you require in your industry. Another benefit of Big Data is you can utilize resources properly, avoid over-booking, and save more if the Big Data System testing is going right. Furthermore, it is possible to reduce serious health problems as health tracking and monitoring devices enable patients to track their symptoms before going to hospitals. In the event of not detecting any disease, customers can prevent themselves from unnecessary hospital visits.   

Quality Assurance   

 Big Data Testing works as per three levels: data integration, data collection and deployment, and scalability. By having an eye on the variety, volume, and velocity of data, Big Data Testing Service Providers can give superior healthcare experiences to customers and ensure the exact market value to businesses.   

 As Big Data Applications are very complex, the testing of such apps requires excellent work experience, a technical mindset, the best data science strategies, and the right Big Data Testing Tools.  

With BugRaptors Big Data Test Offering powered by machine learning-based validation, you can experience up to 25% saving in time-to-market and reduce costs from 25% to 30%.   

Our thorough Big Data and Analytics Testing Services help companies achieve:  

• Analytics Testing with Predictive Models.   
• Data Ingestion Testing for all data sources (structured, unstructured, and semi-structured).   
• Visualization Testing for Data Insights.   
• Data Quality in Big Data.   
• Migration Testing to Big Data Lakes.   
• Data Verification of Database Migration and Data Lake Projects.