Changes to Database

The new architecture prescribed two sources of medical data: for regular workflows and for search functionality. These changes produced a ripple effect on the existing stored procedures, requiring significant modifications to database.

The changes were made in two steps. In Step 1, the users, who are authorized by the medical facility, reviewed the list of ICD-9 codes in practice by frequency of use, upcoming patient visits and common problems. They made relevant ICD-10 assignments and got approval. This action necessitated retrieving data from the database in variety of ways in order to provide user-friendly displays.

Architecture analysis for the EHR product

Using their extensive hands-on EHR product engineering experience, the HealthAsyst team examined the impact of the transition on the “grass-roots level,” of usage and began work on these critical modules.

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Impact Analysis of the EHR product

The HealthAsyst team became intricately involved in design discussions to assess the impact of the transition on the company’s EHR Clinical Exchange Document. It was determined that almost every module of the product would be impacted -- not just for current processing, but also for historical and family problems. The HealthAsyst team assisted the client in prioritizing the steps needed for mapping the codes between ICD-9 and ICD-10. HealthAsyst also helped establish a time schedule for the conversion process.

Inbound and Outbound Data Challenges

Many additional configuration requirements popped up at the point of impact analysis due to the state of readiness of the partner systems. They send and receive data to/from EHR, and since ICD-10 readiness was not taking an immediate priority on their roadmap, this forced the EHR team to provide configurability to accept and send ICD data in both -9 and -10 formats. This triggered changes in message formats.

The HealthAsyst team helped determine the order in which inbound data would be accepted, matched and stored in the local database.

In Step 2, the approved mapping was applied to the patient data. This required extensive searching for ICD-9 codes in active situations and replacing every occurrence with mapped ICD-10 codes. The changes were applied in a strictly controlled conversion environment, which involved serially running database batch jobs all night. These intelligently programmed batch jobs ended the night’s conversion at a logical step before users logged-in the next morning so as to prevent them from experiencing screen delays.