If the thought of migrating electronic health records (EHRs) stresses you out, you’re not alone. From downtime to lost productivity to diminished data quality, the process is rife with challenges.
One of the most important—and often overlooked—steps is ensuring that data is cleaned and normalized before transferring it to a new system. Sure, this requires some extra work and planning, but it’s a great (and rare) opportunity to do things differently and better. After all, you wouldn’t want to flood your shiny new EHR with dirty data and legacy issues, right?
We explored this topic in a recent insight brief, outlining five essential considerations for a smarter, more streamlined migration. Each section gives you actionable strategies to support long-term data quality in healthcare.
Click here to read the full brief or keep scrolling for the inside scoop on data normalization.
How normalization refines data quality in healthcare and boosts EHR performance
As patient data flows through various EHRS and health information systems, it often develops gaps, making it less credible and usable. While seemingly insignificant, a handful of missing lab results or unspecified diagnostic codes here and there can snowball over time, greatly undermining critical functions like complex analytics, quality reporting, and revenue cycle management.
Furthermore, manual efforts to standardize this data can eat away at vital resources and require high-end technologies to accurately capture nuances in unstructured data, such as abbreviations and negations. By leveraging normalization tools grounded in rich clinical terminology, provider organizations can assess data quality weaknesses pre-EHR migration and automate the mapping process, majorly reducing manual effort and the threat of human error.
“Can we develop our own internal expertise to standardize data?” you might be wondering. Yes, but it’s unlikely to be cost-effective or ideal. Specialized solutions – namely those that leverage domain-specific natural language processing (NLP) to normalize data and add standard codes – can address inconsistencies in data with unparalleled accuracy and efficiency, empowering data scientists and analysts to focus on more meaningful projects. They can also accelerate critical analytics and innovation.
The bottom line? Data standardization is a pain—but with the right tools and technologies, it doesn’t have to be.
Keep scrolling for a sneak preview of the insight brief.
Can we afford to normalize data pre-migration? (Can we afford not to?)
Master files or dictionaries within EHRs contain references for various types of clinical data such as lab tests and surgical procedures. They are also critical to the proper
functioning of EHRs, supporting workflows, clinical documentation, and reporting. But one cannot simply drag and drop this data from one system to another, and in some cases, the legacy EHR vendor may actually own these files which prevents them from being transferred at all. Resolving this issue is a time consuming, labor-intensive endeavor whereby the files are rebuilt from scratch or cleaned and normalized to help elevate the quality of the data prior to import.
And while compiling the terms – the lists of labs, procedures, etc. – is fairly straightforward, the mapping to standardized codes requires meticulous, specialized work that can throw migration timelines way off track.
Understanding this process and the amount of time it may take is essential to develop realistic transition plans. At the same time, tackling this issue before EHR migration begins can prevent the transfer of dirty data into an otherwise pristine new system. As part of migration planning, provider organizations may want to explore data normalization solutions built to address this pervasive problem. Tools grounded in rich terminology can be leveraged to assess data quality weaknesses and automate the mapping process, drastically reducing the time and labor required to do the work while minimizing the potential for human error.
