Two challenges associated with using Big Data within healthcare.

Two challenges associated with using Big Data within healthcare.

Data Privacy and Security: It is increasingly important to ensure the privacy and security of medical data when using Big Data. Private information such as financial information, health records, and other sensitive information is commonly stored in healthcare data. It is challenging to secure and protect Big Data from unauthorized access, breaches, and misuse due to the sheer volume and variety of data. Health insurance companies are required to comply with HIPAA regulations to safeguard patient data. Data privacy and security management in a Big Data environment, however, can be challenging, requiring robust security measures such as encryption, access controls, and other measures to maintain patient privacy.

 

Data Quality and Reliability: In addition to ensuring data quality and reliability, using Big Data in healthcare is another challenge. Often, big data comes from a variety of sources, including electronic health records (EHRs), social media, wearable devices, and other connected devices. Data quality, accuracy, and completeness will vary based on the source. It is possible to make incorrect clinical decisions and have inaccurate insights when data quality is poor, which can have serious consequences for patients. Furthermore, new data streams and sources are frequently emerging in healthcare, making Big Data an ever-evolving field. In healthcare organizations, managing Big Data velocity, veracity, and volume while ensuring data quality and reliability can be challenging. To guarantee the accuracy, relevance, and reliability of the data used for analysis and decision-making, robust data validation, data cleaning, and data integration processes are necessary.

 

Big Data challenges in healthcare include ensuring data quality and reliability, as well as privacy and security concerns. For healthcare organizations to mitigate these challenges and maximize the benefits of Big Data, they must implement robust data governance, security measures, and validation processes.