Translating Data From an Electronic Prescribing and Medicines Administration System Into Knowledge

Abstract

Background: Electronic Prescribing and Medicines Administration (EPMA) systems are being widely implemented to facilitate medication safety improvement. However, translating the resulting big data into actionable knowledge has received relatively little attention.Objective: To use routinely collected EPMA data in the study of exact time discrepancy between physicians' order and nurses' administration of systemic antibiotics. We evaluated first and follow-up dose administration and dose intervals and examined multi-factorial determinants in ordering and administration explaining potential discrepancy. Methods:We conducted an observational study of electronic health records for all medical patient stays with antibiotic treatment from January to June 2018 (n=4392) in a large Belgian tertiary care hospital. Using an EPMA system with Barcode Medication Administration, we calculated time discrepancy between order and administration of first doses (n=6233), followup doses (n=87 960) and dose intervals. Multiple logistic regression analysis estimated the association between time discrepancy and various determinants in ordering and administration.Results: Time discrepancy between physician order and nurse administration was less than 30 minutes for 48.7% of first doses and 61.7% of follow-up doses with large variation across primary diagnoses. Greater dose intervals, oral versus intravenous administration, and order diversion from regular nurse administration rounds showed strongest association with less timely administration. Conclusion:Electronic Prescribing and Medicines Administration systems show huge potential to generate actionable knowledge. Concerning antibiotics treatment, having physicians' orders coincide with regular nurse administration rounds whenever clinically appropriate, 4 further taking contextual factors into account, could potentially improve antibiotics administration timeliness.