Big data is bringing major changes to healthcare and other industries. Described as a healthcare “game-changer” in a Forbes article from earlier this year, big data involves gathering, organizing, managing, and analyzing huge data sets with the hope of gleaning important insights and predicting various outcomes.
For the healthcare industry, these insights can potentially save lives. Big data may help us predict responses to medications and likelihood of adherence to a course of treatment, helping healthcare professionals intervene more effectively. We may improve our ability to come up with effective individualized treatments for cancer and other serious illnesses. Big data can also play a significant role in preventative medicine and delivering more accurate diagnoses.
One of the significant trends in healthcare IT involves anticipating big data needs and developing the means to handle big data projects. What are some of the main challenges?
Determining the ‘why’
It’s tempting for healthcare companies, providers, and research groups to turn to big data merely because it’s touted as the ‘next big thing.’ However, before embarking on any kind of big data project, it’s important to step back and question the purpose of such an undertaking.
There are issues that don’t necessarily require a big data approach. They can be analyzed and addressed in other ways. Organizations need to ask why they’re turning to big data and consider how they’ll use it. What are their goals? As a project unfolds, and new data and insights emerge, the goals may very well change, but there needs to be an understanding of the purpose from the start.
Strengthening data security
Organizations working with patient data need to comply with HIPAA standards at minimum. The technology that’s used to undertake big data projects may have critical vulnerabilities that expose patient data to theft and tampering. It’s imperative to choose reputable vendors with a strong track record for data integrity, safety, and security compliance. Working with experienced IT professionals will help healthcare organizations with these choices.
Data will also be flowing in from multiple sources, through networks of Internet-enabled devices. Protecting these devices and complex networks is crucial for safeguarding patient data.
Considering the data quality and type of data
Working successfully with data requires confronting problems with its quality and standardization. A recent article from Health IT Analytics discusses some of the current problems with patient records and clinical documentation. These include mismatches in records and poorly merged records, unnecessary duplication, incomplete information, lack of standardization in reports and notes, and blatant errors.
How can an organization address inaccuracies and deal with poor standardization? What are the best IT tools to rely on? These questions are important to ask not simply in relation to projects involving large volumes of data, but for a variety of reasons. Improved data integrity is critical for healthcare organizations and can reduce the chances of costly errors that may compromise people’s well-being.
Another point to consider is the nature of the data processed and analyzed in a big data project. Traditionally, what organizations work with are relational databases involving structured data, like Microsoft SQL Server. However, big data projects drawing on unstructured data that’s quickly changing and growing will use non-relational databases.
What role will big data play in your organization?
Big data is transforming the healthcare industry, and your organization may have plans to use it in various ways to improve healthcare outcomes and quality of life for your patients and to boost your own growth. To further discuss big data projects and possibilities, don’t hesitate to contact us. Even if you aren’t about to embark on any big data initiatives, there are still ways to improve your IT set-up to allow for the possibility in the future and to strengthen current performance of your networks, devices, and databases.