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

A core principle of medication safety is making sure the right patient gets the right drug, yet wrong-drug and wrong-patient errors persist, even when computerized prescriber order entry (CPOE) systems are used. Accurate problem lists help prevent wrong-drug and wrong-patient errors by allowing the electronic medical record (EMR) to remind prescribers when orders do not match the problem list. However, problem lists are often inaccurate and incomplete, jeopardizing patient safety. Indication alerts prompt prescribers to add new problems to the problem list when a drug order does not match the problem list. Indication alerts also promote self-interception of wrong-drug and wrong-patient errors by increasing situation awareness.

Two types of self-interception events can be measured in an automated way: 1) abandon-and-reorder—a prescriber starts then abandons an incorrect order before signing it, and then reorders for the correct drug or patient; or 2) retract-and-reorder—a prescriber cancels an incorrect order soon after signing it, and then re-orders for the correct drug or patient. Previous work used the abandon-and-reorder and retract-and-reorder methods to measure the effectiveness of several interventions, including indication alerts, in reducing wrong-drug and wrong-patient errors. That work, however, was limited due to small numbers of drugs studied, single study sites with only one commercial EMR, and the use of posttest-only study designs. This project will address limitations in this earlier work and aims to fill important gaps in knowledge about how to prevent wrong-drug and wrong-patient errors and how to improve the completeness of problem lists.

The specific aims of the project are as follows:

• Implement a set of 30-50 indication alerts for medications that are vulnerable to look-alike and sound-alike errors at one hospital in Chicago and six in New York City, using two commercial EMR systems. 
• Conduct an interrupted time series study design to quantify the effect of indication alerts on 1) the combined rate of self-intercepted wrong-drug and wrong-patient CPOE errors and 2) on the rate of each type of error viewed separately. 
• Assess the impact of indication alerts on the probability of adding new diagnoses to the problem list during encounters that include CPOE. 

By achieving the aims of the project, the investigators hope to improve the quality of care by preventing errors and improving problem lists and to advance the technical capabilities of the field by demonstrating how to design and implement indication alerts for a large number of medications within two different EMRs.