Ask a layperson what healthcare data migration is, and they'll likely say something like “moving data from one location [or source] to another.” But exactly how (and where) that data moves is more nuanced. For example, if you don’t need to use the data regularly, you might archive it. Doing so gives you continued access for auditing, legal, and regulatory purposes while reducing costs and mitigating risk.
When you require essential information for clinical care as your facility changes software platforms, however, you must ensure the data from your existing system is usable in the new system through data conversion. In an EHR or EMR conversion, providers typically need immediate access to the problem list, allergies, medications, and immunizations (commonly referred to as PAMI data) as well as other important information that supports patient safety, care coordination, and clinical decision-making.
If the PAMI or other crucial data doesn’t align with the data structure in the new system, or the old system includes unstructured data in healthcare (which makes integration more complex), data conversion transforms it so it’s functional, explains Madelaine Yue, vice president of solutions delivery at Experis Health Solutions, an Olah partner. Madelaine and her colleague Ayesaan Jude Rebello, solutions director of process improvement, spoke with us about some of the common considerations and challenges with converting data and how to ensure success.
Understanding Healthcare Data Conversion Benefits and Challenges
When completed effectively, the conversion of healthcare data contributes to a more seamless patient and provider experience. “It reduces the likelihood of patient safety risks like medication errors,” says Jude, “and boosts provider satisfaction by making it easier to gather information whether it’s in the old system or current system.”
However, you’ll need to anticipate and address potential challenges as well. These may involve problems with your system or internal processes.
One common source of trouble is when the original system’s data structure doesn’t lend itself well to data conversion. For example, EMR conversion may be difficult unless your EMR complies with HL7 and FHIR data standards and includes repeatable, structured data elements.
To address this challenge, create a mapping strategy to translate unstructured data in healthcare into standardized formats that comply with HL7 and FHIR data standards. Then, you should perform data normalization to ensure that all elements are clean, consistent, and fit the standardized schema.
Data conversion can also be difficult when there’s no consistent data capture method to document key elements (e.g., users document data that combines unstructured data in healthcare and discrete fields). To overcome this issue, you may create a standardized schema to map the unstructured and semi-structured data into discrete, structured fields. You can leverage natural language processing (NLP) tools to process unstructured text and extract data elements.
Keeping Things Moving: What to Know About Conversion Timelines
Your data migration plan should address the time you anticipate it will take to convert your data. While it’s common for conversion to take approximately six months, this timeline depends on numerous factors, some of which may or may not be under your control. It’s therefore helpful to account for additional time depending on your specific situation. Consider the following:
- The volume of data you convert. “Organizations participating in value-based care arrangements may want to convert and migrate more than a couple years’ worth of data because they may need access to historical information to support specific quality measures,” says Madelaine. However, it’s a balancing act. “There’s a cost associated with converting all that data,” she adds. “You also don’t want bad data triggering quality measures. Organizations must be tactical in how they approach a data conversion.”
- Your relationship with the legacy system provider. As you retire legacy systems and migrate your data, your legacy system provider must be on board with releasing data. “Sometimes organizations have to bring legal counsel into the conversation because they have significant difficulties pulling their data,” says Madelaine “If your legacy system vendor puts up walls—even unintentionally—it can throw off your entire timeline.”
- Resource constraints of a hosted environment. If you use another healthcare organization’s EMR, it’s different from working directly with a software vendor. You’re at the mercy of that organization and its resources (or lack thereof) for ingesting the data during an EMR conversion.
- Source system delays or the inability to pull data. If you can’t pull the data out of a source system efficiently, your entire data conversion project may take longer. For example, you may face an extended timeline if your source system doesn’t comply with information-blocking regulations.
- Internal resource constraints for evaluating and validating the data. You must have enough internal resources to assess and validate the data to move into the final stages of the process.
- Maintaining a single source of truth. Identifying and correcting duplication errors to create an enterprise master patient index (EMPI) is crucial, otherwise the conversion process may become burdensome and time-consuming.
- Patient matching. If you're unable to match patients easily across systems, the conversion process may also be delayed.
- Scope creep. Failing to monitor the scope of a project can be costly, says Jude. “Sometimes additional asks can make things less straightforward and increase the price” in addition to the time it takes to deliver, he notes.
Safeguarding Your Data and Organization During Conversion
Alongside its people, data is one of the most important assets that a healthcare system or hospital holds. Because it’s also highly sensitive and any corruption could be catastrophic, it must be protected during data conversion.
Ensuring Data Quality and Integrity
Any degradation of data quality and integrity during data conversion can endanger patient care and your operations and organization’s reputation. That’s why you should dedicate resources (e.g., informaticists or medical records specialists) to the important task of comparing a statistically significant sample of patient records in the source system to the transformed data categories in the new system – with the goal of identifying whether any errors (e.g., specification errors, mismatches, or data gaps) occurred during the data conversion process. Once the destination system ingests the data, it’s important to perform additional spot checks for accuracy.
“We’re currently looking at ways to create a low-cost, automated approach to do a full assessment of the files and not just spot checks,” says Madelaine. “We want to streamline the process and hopefully reduce some of the FTE lift because we know healthcare is really tight on resources.”
Following Healthcare Data Protection Best Practices
Data security and privacy are also critical during data conversion. One best practice for protecting healthcare data is to set up Secure File Transfer Protocols (SFTP) properly, so everyone (including offshore vendors) has proper access to perform file transfers. Another is to ensure your vendor meets security protocols. Ask your vendor to articulate how it ensures HIPAA compliance and healthcare data protection.
Heeding Expert Advice: Guidance from a Pro
As you plan for data conversion and migration, Madelaine recommends following these best practices:
- Have a plan and a strong partner. Converting data is complex, but the right partner(s) can help you simplify the process. Ask lots of questions to understand each one and what it brings to the table.
- Keep end-users informed. Let users know what data is being converted versus archived and when so that they know how to access the information they need.
- Keep going, even if you hit a wall. Engage your vendor partners to brainstorm new approaches for data conversion.
- Reiterate the importance of validation. “The reason you’re converting your data is that you need it to be usable,” says Madelaine. “You do this through data validation.”
Support Effective Data Conversion With a Robust Archiving Solution
Data conversion is a critical step in the data migration process, but it’s just the first of many. During the process, you’ll identify information that should be archived to optimize storage costs, improve system performance, facilitate historical data access, and ensure compliance with data security and privacy regulations.
Olah streamlines healthcare data migration with a simple, fast, and complete data archiving solution. Request a demo to see how our stress-free solution can help you modernize your data strategy.
