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EHR Interoperability Helps, Hinders Immunization Data Quality

Advancements in EHR interoperability had positive and negative effects on immunization data quality in North Dakota.

- Two researchers from the North Dakota Department of Health’s Division of Disease Control recently published their findings on the impact of EHR interoperability on data quality in the state’s immunization information system in Online Journal of Public Health Informatics.

Impact of EHR interoperability on data quality

According to the research, EHR interoperability added to immunization data quality in one area but detract from it in others.

Mary Woinarowicz, MA, and Molly Howell, MPH, evaluated data contributed to the North Dakota Immunization Information System (NDIIS) by two types of providers between 2011 and 2014: providers entering information manually into the system using an online interface and those automatically reporting this data using EHR technology. The pair of researchers analyzed at three-month intervals data on the total number of does administered, when those data were entered into the system, and number of duplicate records added

What they found were a mixed bag of results. The difference in reporting methods did not have a significant impact of the number of doses administered, but the timeliness of entering those data in the NDIIS did change:

Interoperability has had a positive impact on timeliness of doses entered into the NDIIS. Although the percentage of doses coming into the NDIIS within one month has remained fairly consistent, the NDIIS is receiving more data entered the same day that the dose was administered, with fewer doses taking one week or more. Improved timeliness of entry into the NDIIS makes it easier for providers and other NDIIS users to make more informed decisions about a patient’s immunization status, current and future immunization needs and to conduct timelier reminder/recall.

However, EHR interoperability contributed negatively to the creation of duplicate records in the state immunization reporting system. Over a 12-month period, providers using interoperable EHR technology created more than 10.5k duplicate records compared to the 637 duplicate records created by their counterparts.

Woinarowicz & Howell attributed the disparity to the matching criteria:

When electronic messages are sent from an EHR to the NDIIS, the client information is matched based on an exact match of first name, last name and birthdate. If an exact match cannot be found, a new record is created in the NDIIS. There are a lot of different naming conventions in EHR systems with some systems allowing special characters but not spaces, others only allowing spaces, or users entering patients in an EHR with a nickname instead of their full given name. All of the EHR differences can vary from what the NDIIS will allow for names, causing records not to match.

The considerable increase in superfluous records led the start to implement an automated de-duplication for incoming data and raised the need for a similar system to identify and remove duplicated records already in the system.

Another negative impact of EHR interoperability on data quality had to do with the completeness of submitted data resulting causes by differences in the release of  HL7 implementation guidance. Therefore, compliance did not necessarily mean submitted data was of high quality.

For example, the NDIIS requires a client middle name; an EHR may not capture this data element or may automatically send a value of ‘NA,’” they authors explained. “They are still sending the data element in the HL7 message and in a valid format, but the information is not meaningful in the NDIIS. This is also the case with data fields like race and ethnicity and having an ‘unknown’ value sent from an EHR.”

Subsequent changes made to the NDIIS by its vendor have corrected the problem.

The findings of the study led researchers to conclude that more work on the part of NDIIS staff and its vendor is necessary “to help reduce the negative impact of duplicate record creation, as well as, data completeness.”

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