Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders.

BACKGROUND

Previous research developed a new method for locating prescribing errors in rapidly discontinued electronic medication orders. Although effective, the prospective design of that research hinders its feasibility for regular use.

OBJECTIVES

Our objectives were to assess a method to retrospectively detect prescribing errors, to characterize the identified errors, and to identify potential improvement opportunities.

METHODS

Electronically submitted medication orders from 28 randomly selected days that were discontinued within 120 minutes of submission were reviewed and categorized as most likely errors, nonerrors, or not enough information to determine status. Identified errors were evaluated by amount of time elapsed from original submission to discontinuation, error type, staff position, and potential clinical significance. Pearson's chi-square test was used to compare rates of errors across prescriber types.

RESULTS

In all, 147 errors were identified in 305 medication orders. The method was most effective for orders that were discontinued within 90 minutes. Duplicate orders were most common; physicians in training had the highest error rate ( < 0.001), and 24 errors were potentially clinically significant. None of the errors were voluntarily reported.

CONCLUSION

It is possible to identify prescribing errors in rapidly discontinued medication orders by using retrospective methods that do not require interrupting prescribers to discuss order details. Future research could validate our methods in different clinical settings. Regular use of this measure could help determine the causes of prescribing errors, track performance, and identify and evaluate interventions to improve prescribing systems and processes.