What Are the Top Challenges Affecting the Implementation of Big Data in Healthcare?
Big data has marked a place for itself in several industries. As far as the healthcare industry is concerned, healthcare is on the brink of a significant transformation through the use of big data technologies and advanced analytics. The use of big data in healthcare can show providers how to increase profitability and improve operational […]
Big data has marked a place for itself in several industries. As far as the healthcare industry is concerned, healthcare is on the brink of a significant transformation through the use of big data technologies and advanced analytics. The use of big data in healthcare can show providers how to increase profitability and improve operational efficiency. Also, big data also has the potential to uncover trends that can directly improve people’s well-being. However, the road to meaningful healthcare analytics could often prove to be a rocky one, considering the vast and complex database that healthcare companies possess. So, what are the top challenges organization
Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is a pre-requisite for implementing big data in healthcare. However, impeccable data governance habits is an aspect that is often lacking among healthcare companies. Several recent research shows that the EHR data and the patient-reported data match only in 23.5% of records. Such unreliable data in healthcare records contribute to quality issues that will plague data throughout its lifecycle, and the use of big data in healthcare to identify meaningful analysis goes in vain.
It is not just essential for companies in the healthcare industry to keep their hospital and clinical premises clean, but it is also crucial to cleanse data too when implementing big data in healthcare. Unstructured and scattered data can quickly derail a big data analytics project, especially when bringing together different data sources that may record clinical or operational elements in various formats. By cleansing the data and structuring them into similar patterns, healthcare providers can ensure that datasets are accurate, correct, consistent, relevant, and not corrupted in any manner.
The healthcare industry has access to vast amount of patient data, and data storage is a major concern while dealing with big data in healthcare. As the volume of healthcare data grows exponentially, several companies are no longer able to manage the storage costs and impacts of on-premise data centers. Cloud storage is increasingly becoming a popular option as costs drop and reliability grows. However, organizations must be extremely careful about choosing partners that are well aware and understand the importance of healthcare-specific compliance and security issues.
Healthcare data is highly sensitive and are subject to a nearly infinite array of vulnerabilities. Hence, while using big data in healthcare, data security is the ‘numero uno’ priority for organizations especially in the wake of high profile breaches, hackings, and ransomware episodes. So, it becomes vital for healthcare organizations to remind their staff members of the critical nature of data security protocols and consistently review who has access to high-value data assets to prevent malicious parties from causing damage.
Analytics and reporting
Generating reports that are clear, concise, and accessible to the target audience is the primary goal of healthcare organizations while devising big data in healthcare. The accuracy and integrity of the data have a critical downstream impact on the authenticity and reliability of the report. Since regulatory and quality assessment programs frequently demand large volumes of data to feed quality measures and reimbursement models, healthcare companies must ensure that they can generate the right data and reports with the help of big data analytics.