Data extraction is an important first step to any data management project. It involves locating, identifying relevant files, and preparing them to be moved (aka: migrated) to a new system or other location. Sound simple? In theory, yes. However, there are many things that must go right for successful data extraction. What are the main challenges in healthcare data extraction? There are several challenges that can impact the data extraction process. Three of the main hurdles include: Data lineage. It is vital to understand the data source/type within an EHR. There can be numerous data formats (images, videos, text, numeric, etc.) that are stored as structured (quantitative/easily formatted) or unstructured (narratives and summaries, images, pathology reports, etc.). While EHRs try to standardize data capture, there is a lot of inconsistency to date and 80% of health data is unstructured. Further, exploration needs to determine if the data is encrypted or compressed in a format that is not standard. Data location. Healthcare data can be spread across multiple source systems. This can create logistical challenges. Some larger health systems manage more than 10 EHRs and work with 18 disparate vendors. This can create complexity and challenging workflows. System experience. The talent pool of people with experience managing legacy systems is drying up. In some cases, custom built legacy systems or specialty systems with unique architecture can become hurdles for organizations as they attempt to increase interoperability and streamline their overall stable of applications that are in production. All that said, there are four systematic recommendations that health care providers can follow to help kick off a data move with a smooth extraction. Four steps to ensure a successful data extraction project: Identify clear project goals. As part of a legacy data management strategy, it is important to think through the project goals. This includes reviewing your strategy around application rationalization and aligning the extraction with both upstream and downstream expectations. Deep dive into everything about the data. This means documenting and understanding the data lineage, location, and access to inside knowledge prior to extraction and build begins. This involves really getting into the nitty gritty of understanding all the details about the data. Obtain data access from vendor. Work with vendors to procure data access when needed. This may take some time and you may choose to involve a third-party partner to alleviate the burden on your internal team. Work with an experienced data management partner. Make sure you compare apples to apples when evaluating partners. Look at ratings, ask for referrals, review the scope of your project against the partner’s experience. Ask questions about your unique data and make sure there is alignment for the project goals, timeline, and expectations as well as a good fit for a positive working relationship. This RFP Template and HR and Payroll System Archive Template can help. Learn more about data procurement & extraction from Darryl Mais, Solutions Engineering Director at Harmony Healthcare IT in this HealthData Talks Podcast Episode. At Harmony Healthcare IT, we have extracted some of the most complicated and involved healthcare data. Our solutions engineers have a blend of architectural engineering, data engineering, and product knowledge to leverage the right solution for your organization. At the end of the day – you are trying to preserve patient data and have it accessible. Extracting the data should not be a roadblock. We can help.