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MIIC participates in the American Immunization Registry Association (AIRA) Data at Rest (DAR) assessment to measure the quality of existing data in an immunization information system (IIS). For the standard assessment, immunization and client information for MIIC clients aged 0-2 years are assessed for completeness, validity, and timeliness across 45 measures.
MIIC participated in DAR in the fall of 2024 and 2025 as part of our continued efforts to improve data quality. We met expectations for over 90% of the measures, indicating that MIIC data is largely reliable. This also resulted in Minnesota ranking number one in data quality, meaning we had the highest assessment score across all jurisdictions participating in DAR for both 2024 and 2025.
While we met expectations for over 90% of the measures, there are a few data quality areas to improve upon:
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Completeness of client phone number and email address: For both years, MIIC had just over 80% phone number completeness and around 30% email address completeness for the measured cohorts. AIRA’s expectation is at least 90% completeness for each.
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Completeness of vaccine lot expiration date: In 2024, MIIC had just under 98% completeness for vaccine lot expiration dates, although we did improve to meet AIRA’s 99% expectation threshold in 2025.
- Age-based validity of a vaccine (i.e., given at an improbable age): While MIIC was under the 1% threshold in 2024, 1.3% of measured immunizations were improbable shots based on age of administration in the 2025 cohort. These are situations such as adult hepatitis B vaccine being given to a child or rotavirus vaccine being given to a 2-year-old.
The MIIC data quality team plans to use these results to inform future data quality projects and MIIC programs. As providers, please continue to send complete, valid, and timely data to MIIC. When possible, send phone number, email, and lot expiration date data, and ensure the correct vaccines are being reported to MIIC.
Thank you to all of our providers and MIIC users for helping us achieve this! We know we cannot improve MIIC data quality without this collaboration and these results rely on our providers continually sending good data, monitoring their data in MIIC, and working with us to make corrections when issues are identified.
In MIIC, an alias, or “also known as” (AKA), is created when you change the first name, last name, or birth date on a client’s record. On the client search screen, AKAs are presented as check marks in the ‘AKA’ column for possible matches. When you click on an AKA, you are directed to the MIIC record that AKA is linked to, with a note at the top of the screen indicating the AKA you clicked on.
The purpose of this function is to allow users to find MIIC records saved under a client’s former name or nickname(s). When used properly, it can improve both the use and completeness of MIIC records and prevent fragmented records for the same client under different names.
Unfortunately, “bad” AKAs are also created in MIIC in several ways. Occasionally, a data entry mistake occurs when entering a client’s name or birth date incorrectly. When a user tries to fix this mistake, an AKA is created with the original data entry error. Another common scenario occurs when a user types directly onto a client record when attempting to search for another client in MIIC, changing the first client’s information instead of searching in MIIC for the second client. In this case, the AKA represents the actual client the MIIC record belongs to while the record is now saved under a second client’s information.
Bad AKAs are more frequently created and reported during respiratory illness season due to the higher volume of immunization data coming into MIIC during this time. Please verify that you and any of your staff searching in the MIIC user interface are clicking ‘manage client’ to start a new search and not typing directly onto a MIIC record to incorrectly change client information in MIIC. The creation of bad AKAs can not only make it difficult to find your patient’s information in MIIC but lead to further bad client merging in MIIC, which results in a lot of coordination and time to correct.
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