For companies shifting towards digital operations, data extraction is just the starting point on the journey from data to useful insights. In the healthcare field, acquiring data is simple–hundreds of patient files, test results, and more flood your digital systems daily. When you’re inundated with data and information, it’s difficult to sort and analyze that data when you need it.

Data extraction identifies relevant data sources and consolidates or refines said data so it can be stored in a central location, following a specific format. The primary goal of data extraction is to obtain high-quality, usable data and analyze, process, and use it for a range of purposes. This data can then be used for reporting, machine learning, visualization, and more.

In this article, we’ll define data extraction, the stages in the data collection process, and the uses and benefits of data extraction.

Data Extraction: Explained

Data extraction is the process of collecting or retrieving several types of data from a range of sources. Once collected or retrieved, this information and data may be scattered or difficult to consolidate in its raw form. However, with data extraction, you can consolidate, process, and even refine data to be stored in a centralized location.

These centralized locations can be on-site at your facility, cloud-based, or a hybrid of both. Data extraction is the first step in two different processes:

• ETL: Extract, transform, load
• ELT: Extract, load, transform

Both of these processes are necessary to complete a data integration strategy. Once data has been processed through either ETL or ELT, it can be analyzed for insights. 

The ETL and ELT Processes

Both ELT and ETL follow the same process and allow for organizations to consolidate data collected from a wide range of sources into a centralized location, and assimilate this data into a common format. These processes are used by companies across industries for a multitude of reasons.

The three steps in the ETL Process are:

• Extraction: This involves retrieving data from one or more systems and preparing it for processing or transformation.
• Transformation: Data is sorted, organized, and cleansed to refine it. One example is deleting duplicate entries or performing audits.
• Loading: This involves delivering the transformed, high-quality data to a single, unified target for storage and analysis.

While these same processes can be leveraged in a multitude of industries, healthcare industries can use these processes to pull many types of data from a range of local and cloud-native sources to streamline processes. 

Why Data Extraction Is Necessary in Healthcare

In the healthcare and medical care industries, data extraction can make it possible to consolidate and integrate data related to patient charts and care, healthcare providers, and insurance claims. For example, clinical coding officers often use hierarchical condition category (HCC) coding to estimate future healthcare costs for patients. This coding category relies on data extraction in order to: 

• Predict healthcare resource utilization
• Create a complete, comprehensive profile of patients
• Analyze structured and unstructured data in a patient’s chart
• Organize clinical research

When factoring in HCC with your data extraction or ETL/ELT practices, finding the best tool for HCC risk adjustment coding is key. Inferscience offers multiple tools and resources to streamline your data extraction and HCC-related coding.

Next Steps

Patients and providers alike need answers, and a streamlined, centralized database is the first step to getting prompt answers or solutions. Inferscience can take your ETL/ELT and HCC coding processes to the next level with our HCC Assistant and HCC Validator.

For more information on these tools and resources, contact us today to request a demo at info@inferscience.com and see how we can improve your HCC coding and documentation!

References:

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419774/