Electronic health record (EHR) systems within tribal healthcare facilities offer a rich but underutilized data source about medication adherence and its potential to predict HbA1c. The purpose of this analysis was to describe data quality issues for medication events in a dataset extracted from a large tribal healthcare EHR system. This analysis was a critical step prior to conducting a study of medication adherence among American Indians with type 2 diabetes (T2D). The EHR dataset from 2017-2021 included 10,506 patients who were enrolled adult tribal citizens, had T2D, and had at least one health system encounter. Tribal pharmacy refill data were linked to the EHR data and included information such as prescription regimen (e.g., drug, dose, route of administration, quantity), date new medication prescribed, date picked up, and refill dates. Data quality indicators were: duplicate data, missing data (e.g., dispense and fill dates, RX number), and data inconsistent with eligibility criteria. The mean age of the sample was 60.2 ±13.4 years and 52% were female. Out of the 1,400,593 patient medication fill/refill events we excluded 64,015 medication events due to a) duplicate prescription fill data (n=112); b) no dispense date (n=1,081); c) no order date (n=0); d) no fill date (n=0); e) no RX number (n=0); f) missing number of days dispensed (n=0); g) missing drug (n=0); h) missing dose (n=0); and i) data extracted not meeting the eligibility criteria (n=62,822). The date the medication was prescribed was not extracted from the EHR. Discovering these results was labor intensive taking more than 6 months. This analysis revealed few data-related concerns. With the exception of one missing variable, the dataset includes high quality data from which we will calculate medication adherence among American Indians with T2D. Disclosure L.Scarton: None. T.N.Nelson: None. A.Devaughan circles: None. A.Legaspi: None. D.J.Wilkie: Other Relationship; eNursing llc. Funding National Institutes of Health (1R01NR020386-01)
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