PROJECT SUMMARY
Although electronic prescriptions (e-prescriptions) have improved patient safety in some ways, they have
introduced new types of errors that can lead to significant harm to patients up to 4.39 million times each year.
During e-prescription processing, medication errors occur when the medication prescribed by a physician is a
different ingredient, strength, or dosage form compared to the medication dispensed by the pharmacist. Major
reasons these unsafe e-prescription transactions exist include mismatching free-text drug descriptions with
National Drug Codes (NDCs) and erroneous product selection in computer software used by prescribers and
pharmacists. These unsafe e-prescription transactions are resulting in dangerous medication errors that may
lead to significant patient harm. To systematically identify unsafe e-prescription transactions, we have
developed a system, called SAVE-Rx, that uses e-prescription transaction data to automatically identify cases
where the NDC prescribed does not match either the free-text drug description (i.e., an e-prescribing error) or
the NDC of the medication dispensed by the pharmacy. Our system matches NDCs and free-text drug
descriptions on the basis of medication ingredient, strength, and dosage form using standardized drug
terminology from National Library of Medicine’s RxNorm. Unsafe e-prescription transactions are then reviewed
by a safety team and communicated just-in-time to healthcare organizations, through existing e-prescription
workflows, before they can cause harm to patients. Our long-term goal is to improve medication safety through
the strategic use of automation in medication prescribing and dispensing workflows. The overall objective of
this study is to determine the effect of implementing just-in-time feedback on the prevention of medication
errors during e-prescription transactions between healthcare organizations. Aim 1 refines the design of a just-
in-time feedback intervention, SAVE-Rx, and its implementation guide to identify unsafe e-prescription
transactions. Aim 2 evaluates the implementation of the just-in-time feedback intervention for preventing
unsafe e-prescription transactions. Our hypothesis is that we can improve the use of the intervention and alert
performance by actively discontinuing alerts for safe mismatching pairs. Aim 3 determines the effects of the
just-in-time feedback intervention on medication safety outcomes. Our hypothesis is that just-in-time feedback
to healthcare organizations about unsafe e-prescription transactions will prevent clinically significant
medication safety events. Accomplishing these aims will have a positive impact by testing a tool that is capable
of actively monitoring and communicating useful feedback to healthcare organizations about unsafe e-
prescription transactions on the more than 2 billion new e-prescription transmitted each year in the United
States. At the end of this study, we expect to prevent at least 6,000 medication safety events as a result of
implementing SAVE-Rx monitoring on 44 million e-prescription transactions.
