Eight Tips to Successful Data Migration During an EHR Change

Guest post by Calvin Chock, vice president, product management and engineering, McKesson Specialty Health.

Calvin ChockSwitching from one electronic health record (EHR) system to another is no small task. One of the biggest hurdles is the transition of legacy data to the new system, but good understanding of the process and a strong technology vendor relationship can help overcome this challenge and lead to a successful EHR migration.

Many practices are currently confronted with the need to change EHR systems. In some cases, their current system is simply outdated or may not be certified to meet Meaningful Use requirements. Others need an option that will satisfy their needs to support value-based care models. Whatever the reason, all practices want to find a solution that not only meets their operational and patient care needs, but also minimizes disruption to the practice during the transition.

Conceptually, most EHRs capture the same types of information, however, when EHRs were first introduced, there was no standard industry terminology for diagnoses, regimens or allergies. Each system created its own logical categories or terms, which makes it difficult to automatically map data from one system to another.

As an example, many older EHRs have a category for patients with seafood allergies. In EHRs today, that allergy is more accurately broken down as either a shellfish or non-shellfish allergy. During a data migration, this difference means the allergy information cannot be automatically synced to the new system. It requires someone to review the information and make a decision about how to categorize it.

Oncology EHRs are quite extensive because of the complexity of the diseases and treatments. There may be as many as 50 different categories into which varying types of data is stored, which means when categories cannot be automatically mapped by the IT team, extensive manual labor is required by the practice’s staff to make sure data has transferred correctly and to re-categorize data that doesn’t neatly fit into a category in the new system.

In broad terms there are three types of data migrations. The first, migration to a newer generation of an EHR system already in use, is the easiest to complete. If, for example, a practice is upgrading to a newer version of their current EHR system, chances are much of the legacy data could be automatically mapped to the new version. This type of migration can often occur over a weekend with minimal down time and disruption to patient care.

The second type is migration to an EHR of a cooperating vendor. Although migration from one vendor’s EHR system to another’s is more complicated, if the original EHR’s vendor is willing to share information about their data with the new vendor, much of the mapping process can be finalized before the actual migration occurs.

The third, and most difficult, type is migration to an EHR without vendor cooperation. When the vendor of the current EMR system is not willing to share information about how data is stored, the new vendor must figure out how to interpret the data before any mapping can be done – a task made even more challenging when the older EHR system is not able to export data so that it can be read.

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