Summary
Background: Users of electronic health record (EHR) systems frequently prescribe doses outside
recommended dose ranges, and tend to ignore the alerts that result. Since some of
these dosing errors are the result of system design flaws, analysis of large overdoses
can lead to the discovery of needed system changes.
Objectives: To develop database techniques for detecting and extracting large overdose orders
from our EHR. To identify and characterize users’ responses to these large overdoses.
To identify possible causes of large-overdose errors and to mitigate them.
Methods: We constructed a data mart of medication-order and dosing-alert data from a quaternary
pediatric hospital from June 2011 to May 2013. The data mart was used along with a
test version of the EHR to explain how orders were processed and alerts were generated
for large (>500%) and extreme (>10,000%) overdoses. User response was characterized
by the dosing alert salience rate, which expresses the proportion of time users take
corrective action.
Results: We constructed an advanced analytic framework based on workflow analysis and order
simulation, and evaluated all 5,402,504 medication orders placed within the 2 year
timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8%
of orders generated a visible alert, with ¼ of these related to overdosing. Alerts
presented to trainees had higher salience rates than those presented to senior colleagues.
Salience rates were low, varying between 4–10%, and were lower with larger overdoses.
Extreme overdoses fell into eight causal categories, each with a system design mitigation.
Conclusions: Novel analytic systems are required to accurately understand prescriber behavior
and interactions with medication-dosing CDS. We described a novel analytic system
that can detect apparent large overdoses ( of these large overdoses can be mitigated
by system changes. EHR design should prospectively mitigate these errors.
Citation: Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis of electronic medication
orders with large overdoses: Opportunities for mitigating dosing errors. Appl Clin
Inf 2014; 5: 25–45 http://dx.doi.org/10.4338/ACI-2013-08-RA-0057
Keywords
Electronic health record - electronic medical record - medical order entry system
- CPOE - clinical decision support systems
