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Data Abstraction: How to Create a Highly Efficient & Accurate Process



Sometimes, too much information can be just as problematic as insufficient data. Few professionals have the time –– or energy –– to wade through big data sets to find information relevant to a problem they’re facing. And considering how important time management is to healthcare facilities, quality data abstraction is crucial to overall productivity and patient outcomes. Thankfully, it is possible to streamline the data abstraction process to ensure that healthcare professionals can always access the key info they need when they need it. To that end, here are a few ways to create a better and more efficient data abstraction process at your organization.

Data Abstraction 101

Data abstraction is the process of withdrawing select pieces of information from larger data sets for particular applications. Medical record abstractors spend hours upon hours selecting and abstracting data from patient records to assist healthcare professionals. However, healthcare organizations generate and use a lot of data over time, which makes the task of sorting data difficult. 

Data Abstraction Solutions 

The following are methods healthcare organizations can use to improve their data abstraction process: 

Step #1 Focus on Data Hygiene

In order for a healthcare organization to effectively use data, it has to be accurate and up-to-date. Managing data involves promoting good data hygiene practices –– otherwise known as regular “data cleansing.” Data cleansing involves eliminating duplicate data, updating old data sets, removing irrelevant data, and correcting inaccurate data. Poor data hygiene won’t just affect the data abstraction process (though it will make it more difficult), but can also have a major negative impact on patient outcomes and organizational performance. 

Step #2 Get Specific

A single data set can tell a number of different “stories” –– depending on what you’re looking for. That’s why it’s key for healthcare organizations to have clear objectives in place for their team members. Everyone in the organization –– from administrators to doctors, to data abstractors –– should understand both overarching business goals and specific priorities in the here and now. The more specific your organization’s goals and needs are, the easier it will be for abstractors, administrators, and healthcare staff to work together to address them.   

Step #3 Implement Automation

Data abstraction –– like data analysis –– is a process that cannot entirely be automated. Abstraction requires critical thinking and subjective judgment that only human workers can provide. However, automation can help professionals abstract quality data more efficiently. 

Programs like the Flint™ Patient Quality Abstractor Assistant (FlintPQ) help abstractors by identifying data elements that may be most useful for reporting and patient care. FlintPQ acts to guide data abstractors toward solutions it deems likely within data sets. It also accounts for abstractor responses and uses AI technology to “learn” abstractor preferences over time. Plus, it can also extract and transfer data to other systems and generate alerts for professionals based on myriad factors. 

Investing in automation can help your team abstract data more quickly and effectively and lead to improved clinical results. 

Contact Us

Amitech has years of experience providing transformative automated solutions to companies specifically operating in the healthcare sector. We understand the challenges and opportunities data abstraction presents, and we can help your team use automation to its full potential. Contact us here to learn more or to get started with us today. 

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