Preventing Wrong-Drug and Wrong-Patient Errors with Indication Alerts in CPOE Systems

Project: Research project

Project Details


Wrong-drug and wrong-patient errors occur at a rate of roughly one per thousand orders in inpatient and outpatient settings, resulting in millions of potentially harmful errors annually in the US. Both types of errors have been difficult to prevent. Accurate problem lists help prevent wrong-drug and wrong-patient errors by enabling clinical decision support to point out when orders are not consistent with the problem list, but problem lists are often inaccurate. Indication alerts prompt prescribers to add new problems to the problem list when a drug order does not match the problem list. Indication alerts increase situation awareness and encourage self-interception of errors caused by similar names and confusing CPOE interfaces. Self-interception occurs in two ways: (a) abandon-and-reorder—a prescriber starts then abandons an incorrect order before signing it, and then re-orders for the correct drug or patient; or (b) retract-and-reorder—a prescriber cancels an incorrect order soon after signing it, and then re-orders for the correct drug or patient. Indication alerts are associated with self-interception of both wrong-drug and wrong-patient errors and with improvement of problem lists. Given the potential for harm and the opportunity for quality improvement, it is important to develop, deploy, and test effective strategies to prevent wrong-drug and wrong-patient errors and to improve the completeness of problem lists. The long term objective of this research program is to improve medication safety by reducing the frequency of wrong-drug and wrong-patient errors and improving the quality of problem lists in all settings of care. The short-term objective is to evaluate indication alerts as a CPOE-based strategy for preventing wrong-drug and wrong-patient order entry errors and for improving the completeness of problem lists. To achieve these objectives we will carry out experiments to test the following hypotheses: H1. Indication alerts will increase the rate of
Effective start/end date9/30/169/29/22


  • Agency for Healthcare Research and Quality (5R01HS024945-05)


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