By Fiona Villamor on April, 22 2020
Advanced analytics has shaped the healthcare industry in significant ways. It has shown to impact the way healthcare is delivered and received by streamlining workflows, improving the patient experience, and lowering overall costs. Now, not only is the use of advanced analytics widespread—47% of providers are currently using the tool—but 93% of healthcare executives also consider it important to the future of their business.
Related: The road to Advanced Analytics in Healthcare: Overcoming budget constraints
Despite the benefits that come with the technology, there are a number of challenges that have staggered the process to adoption. Healthcare executives reported that having incomplete data (12%) and having too much data (9%) has been delaying the process. Fortunately, these roadblocks can be effectively handled with proper data quality management.
The healthcare industry awash in data
The two challenges mentioned above can be boiled down to one thing: unusable data. In its raw, unfiltered form, data can do more harm than good. Poor data quality can cost companies as much as $14.2 million annually, according to Gartner research. What’s more, data that doesn’t go through proper preparation and cleaning can result in false facts, inconsistencies, and erroneous decisions—things that are unacceptable in a healthcare setting.
According to the North Carolina Medical Journal, health care data in the United States is forecasted to grow at 80 megabytes per patient per year—exceeding a trillion megabytes overall by 2020. This equates to about 665 terabytes for the average hospital. Doctors and nurses are collecting data on a regular basis (patient information, diagnosis data, electronic health records, etc.) and even work with monitoring equipment that can generate 1000 readings per second.
So while there is a wealth of data in hospitals and clinics, there is still the question of converting this pool into a useable format. Healthcare analytics systems will simply not deliver the desired results with unstructured or polluted data. This is why it’s critical to be diligent with data preparation and cleaning procedures to ensure the data is accurate, unified, consistent, in a proper timeline, reliable, and ready for analysis.
Must-read: Data Collection, Preparation, and Cleaning: A guide
Data management is essential, but it can be a long and comprehensive process. In fact, it often takes up 80% of data scientists’ valuable time. Fortunately, enterprise data science platforms like Analance has made the task more streamlined and efficient. With built-in data transformational tools to merge and cleanse data, even citizen data scientists can easily prepare data for advanced analytics.
With the right data preparation techniques and the right data science platform, the healthcare industry can effectively extract value from data and use the insights gained to provide better care plans, educate patients, and improve patient care and outcomes while decreasing healthcare costs.
We are happy to demonstrate how Analance can help you overcome hurdles to healthcare analytics adoption. Request a demo to get more information.
ABOUT THE AUTHOR
Fiona Villamor is the lead writer for Ducen IT, a trusted technology solutions provider. In the past 8 years, she has written about big data, advanced analytics, and other transformative technologies and is constantly on the lookout for great stories to tell about the space.