Assessing the accuracy of drug profiles in an electronic medical record system of a Washington state hospital.
OBJECTIVE
To determine the accuracy of patients' electronic medical record (EMR) drug profiles, to assess the relationship of copayment status with errors of commission and omission within drug profiles, and to evaluate the association between errors of commission in primary and secondary sections of the drug profiles.


METHODS
Cross-sectional analysis of patients' EMR drug profiles at a 46-bed hospital in Washington state.


METHODS
Patients' drug profiles were compared against hospital staff interviews. Drug profiles listing each medication the patient was taking, without listing drugs the patient was not taking, exhibited perfect accuracy. We evaluated associations between errors of commission and omission, copayment status between errors of commission and omission, and associations between errors of commission in primary and secondary sections of the drug profiles.


RESULTS
Demographics of study patients are similar to previously published research, and accuracy outcomes seem to be better than those of previously published studies. Fifty-six percent of drug profiles in our study hospital exhibited perfect accuracy. Errors of commission and omission were unassociated with copayment status; errors of commission in the primary section of the drug profile were unassociated with errors of commission in the secondary section of the drug profile.


CONCLUSIONS
A medication reconciliation program may have led to a high rate of perfectly accurate drug profiles; while its purpose is to increase accuracy and to decrease errors, it may also assist in reducing adverse drug events. Results show that copayment amounts influence drug utilization; these may be associated with errors of commission and omission and not simply with copayment status.
Platte
Brett
B
Washington State University, Spokane, WA, USA.
Akinci
Fevzi
F
Güç
Yunus
Y
D000368:Aged; D003430:Cross-Sectional Studies; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized / Q000592:standards / Q000706:statistics & numerical data*; D059065:Medication Reconciliation / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D005791:Patient Care / Q000592:standards / Q000706:statistics & numerical data*; D055553:Prescription Drugs; D011787:Quality of Health Care / Q000592:standards / Q000706:statistics & numerical data*; D014861:Washington
D055553:Prescription Drugs