EMR Conversion Hurdles: Merging Data from Multiple Sources
Healthcare merger and acquisition activity is prevalent as hospitals and health insurance companies look at growth and alliances to drive success in a post-healthcare-reform world. But with M&A comes real-world data sharing and conversion problems as it pertains to information systems and electronic medical records (EMRs). Successfully merging data from multiple sources is one of the most misunderstood and consistently underestimated problems in health IT today.
Harmonizing Data to be Usable Outside of its Source Systems
If you are combining data from multiple sources and want the data to be usable outside of the source system, then you will need to merge the data into a harmonized database or dataset. A "harmonized" dataset offers the benefit of storing data from different systems in a discreet and re-useable form for extraction and import to a reporting or other system. EMR conversion is the process of taking data from a legacy system and doing the best you can to map and "fit" the data into the new system. It may sound simple, but it’s not as easy as connecting the pipes, turning on the water, and getting drinking water out the other end.
What Makes Healthcare Data Harmonization Difficult?
What makes data harmonization in healthcare difficult is not the volume, variation, or other “V’s” of Big Data (although they do present their own unique challenges). Data harmonization in healthcare is difficult because of the elevated level of ambiguity and complexity in the data concepts themselves. For example, patient demographic information can be merged easily from one system to another. Demographic concepts (age, gender, address, etc.) are highly certain, or unambiguous. This means there is general agreement as to what “age” means in relation to a patient. It’s easy to identify the field for age in a legacy system and map it to the field for age in the go-forward system. There may be formatting issues at play, MMDDYYYY versus YYYYMMDD for example, but everyone is operating under the same definition for the data point itself. However, the certainty ends there. Once you get past the simplicity of demographic data, you quickly find that financial and clinical data points aren’t as easy to map between systems for a multitude of reasons.
The Challenges of Mapping Clinical and Financial Data for Conversion
First, there is a high-level of ambiguity surrounding clinical and financial data. Much of this ambiguity stems from the lack of standardization in healthcare practices, processes, and payment. Recall any recent article about how healthcare prices are determined — the seeming lack of rhyme or reason — and you can easily see where inconsistency in practices leads to ambiguity of the concepts themselves. Second, converting data from clinical and electronic health records is challenging because not all systems store data in the same discrete format, and many systems use proprietary logic that determines how and where data is stored in the underlying database. Third, the flexibility of commercial EMR’s has been both a blessing and a curse. Many organizations chose to customize their EMR’s to “fit” local operations, realizing too late that they had opted out of a standardized workflow that would have collected data in a clean, consistent, and re-usable way. Some missed the opportunity to critically evaluate business as usual, automating bad business practices that led to bad data collection. Other organizations made poor design choices inserting mandatory fields at inopportune times during patient care that led to workarounds and bad data capture. Local EHR configuration and workflows directly determine how data is captured, and the interoperability, quality, and re-usability of the data for other purposes.
Overcoming the Hurdles of Data Harmonization
If you consider all the issues listed above and multiply those by the number of different data sources you are trying to combine, you can see how harmonizing data is an arduous task. Fortunately, there is a way through the complexity. Seasoned resources who understand workflow can determine how and why your data was created, unlocking the value of how that data can be re-used to support quality improvement, population health, operational efficiency, or whatever business goals drive your institution.
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