Patient misidentification in the neonatal intensive care unit: quantification of risk.
OBJECTIVE
To quantify the potential for misidentification among NICU patients resulting from similarities in patient names or hospital medical record numbers (MRNs).


METHODS
A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system. A patient day was considered at risk for misidentification when the index patient shared a surname, similar-sounding surname, or similar MRN with another patient who was cared for in the NICU on that day.


RESULTS
During the 1-year study period, 12186 days of patient care were provided to 1260 patients. The unit's average daily census was 33.4; the maximum census was 48. Not a single day was free of risk for patient misidentification. The mean number of patients who were at risk on any given day was 17 (range: 5-35), representing just over 50% of the average daily census. During the entire calendar year, the risk ranged from 20.6% to a high of 72.9% of the average daily census. The most common causes of misidentification risk were similar-appearing MRNs (44% of patient days). Identical surnames were present in 34% of patient days, and similar-sounding names were present in 9.7% of days. Twins and triplets contributed one third of patient days in the NICU. After these multiple births were excluded from analysis, 26.3% of patient days remained at risk for misidentification. Among singletons, the contribution to misidentification risk of similar-sounding surnames was relatively unchanged (9.1% of patient days), whereas that of similar MRNs and identical surnames decreased (17.6% and 1.0%, respectively).


CONCLUSIONS
NICU patients are frequently at risk for misidentification errors as a result of similarities in standard identifiers. This risk persists even after exclusion of multiple births and is substantially higher than has been reported in other hospitalized populations.
Gray
James E
JE
Center for Patient Safety in Neonatal Intensive Care, University of Vermont, Burlington, Vermont, USA. jgray@bidmc.harvard.edu
Suresh
Gautham
G
Ursprung
Robert
R
Edwards
William H
WH
Nickerson
Julianne
J
Shiono
Pat H
PH
Plsek
Paul
P
Goldmann
Donald A
DA
Horbar
Jeffrey
J
D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D019300:Medical Errors; D016347:Medical Records Systems, Computerized; D008895:Milk, Human; D010355:Patient Identification Systems; D018570:Risk Assessment