Algorithm Reduces Neo-Natal ICU Misidentification

Researchers at Montefiore Medical Center and the Albert Einstein College of Medicine have developed a method to reduce misidentification of patients in a neo-natal intensive care unit.


 Researchers at Montefiore Medical Center and the Albert Einstein College of Medicine have developed a method to reduce misidentification of patients in a neo-natal intensive care unit.

The results were presented at the American Academy of Pediatrics national conference in San Diego. The authors observed in their presentation's abstract that mistaken patient identification in computerized physician order entries in NICU's can be especially problematic, given that many patients are often given a non-distinct temporary first name such as Babyboy or Babygirl.

To decrease such errors, the authors conducted a before-after intervention study where an identification re-entry function was added to the CPOE system that required providers enter the patient's initials, age, and gender prior to beginning an order entry session for a patient. They described a validated algorithm designed to measure near-miss wrong-patient errors called the Retract-and-Reorder (RAR) tool. The algorithm identifies orders that are placed on one patient, retracted, and then ordered on another patient in a short period of time.

The authors discovered that during the pre-intervention period from January 2007 to June 2010, there were 303 RAR order sessions identified among 122,000 order sessions (rate of 284 per 100,000 order sessions) in the NICU. In the post-intervention period, from January 2012 to June 2013, there were 94 RAR order sessions identified among 76,639 (rate of 123/100,000 order sessions), which represents a 50.6 percent reduction. This was in contrast, they said, to the 17.8 percent reduction seen in non-neonates, whose RAR rate decreased from 178 RAR events per 100,000 orders to 146 RAR events per 100,000 orders.

The abstract is available here.

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