Translating Data From an Electronic Prescribing and Medicines Administration System Into Knowledge: Application to...

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.

The objective of this study was 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 multifactorial determinants in ordering and administration explaining potential discrepancy.

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), follow-up doses (n=87 960), and dose intervals. Multiple logistic regression analysis estimated the association between time discrepancy and various determinants in ordering and administration.

Time discrepancy between physician order and nurse administration was <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.

EPMA systems show huge potential to generate actionable knowledge. Concerning antibiotic treatment, having physicians' orders coincide with regular nurse administration rounds whenever clinically appropriate, further taking contextual factors into account, could potentially improve antibiotic administration timeliness.