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 self-intercepted wrong-drug errors during CPOE, as measured by an increase in the sum of abandon-and-reorder (AAR) and retract-and-reorder (RAR) events. H2. Indication alerts will increase the rate of self-intercepted wrong-patient errors during CPOE, as measured by an increase in the sum of AAR and RAR events. H3. Indication alerts will increase the likelihood that problems are placed on the problem list during encounters that include CPOE. To test these hypotheses, we propose studies with the following specific aims: SA1. Develop, implement, and deploy at two health systems the AAR tool and RAR tool to provide a complete assessment of self-intercepted errors during CPOE. SA2. Develop, implement and deploy at two health systems a set of indication alerts for medications that are vulnerable to look-alike and sound-alike (LASA) errors. SA3. Use an interrupted time series design to quantify the effect of indication alerts on the rate of wrong-drug and wrong-patient CPOE errors at two large academic medical centers. SA4. Use an interrupted time series design to quantify the effect of indication alerts on the probability of placing a problem on the problem list during encounters that include CPOE. The project will improve safety and quality for multiple AHRQ priority populations.
|Effective start/end date||9/30/16 → 9/29/21|
- Agency for Healthcare Research and Quality (5R01HS024945-03)