Hospital-reported medical errors in premature neonates

Pediatr Crit Care Med. 2004 Mar;5(2):119-23. doi: 10.1097/01.pcc.0000112370.55948.8e.


Objective: To determine the national rate of hospital-reported medical errors in premature neonates and describe the patient and organizational characteristics associated with their occurrence.

Design: Nonconcurrent, cohort study.

Setting: The Healthcare Cost and Utilization Project (HCUP) contains discharge data collected at community hospitals sited in >20 states.

Patients: All neonatal discharges from the 1997 edition of HCUP were included in these analyses. The definition of prematurity included any hospitalized neonate with a birth weight <2500 g, which corresponds to approximately 37 wks gestation. Medical error was defined as an International Classification of Diseases-9 discharge diagnosis of 996-999 in any of the diagnosis fields associated with the discharge.

Interventions: None.

Measurements and main results: The national rate of hospital-reported medical errors in premature neonates is 1.2 per 100 discharges. There was a significant linear increase in the rate of medical errors based on birth weight (Cochran-Armitage test for trend, p <.001). After we controlled for case mix and organizational characteristics using a logistic regression model, medical errors continued to be associated with birth weight, gender, insurance status, and hospital characteristics.

Conclusions: The rate of hospital-reported medical errors in premature neonates is lower than that reported in both the adult and pediatric populations. Specific patient and organizational characteristics are associated with an increased risk of medical errors. These characteristics may help to identify opportunities to improve patient safety efforts in this vulnerable population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Child, Hospitalized / statistics & numerical data
  • Cohort Studies
  • Databases, Factual
  • Female
  • Hospitals / statistics & numerical data*
  • Humans
  • Infant, Newborn
  • Infant, Premature*
  • Insurance Coverage
  • Insurance, Health
  • Intensive Care Units, Neonatal / statistics & numerical data
  • Male
  • Medical Errors / classification
  • Medical Errors / statistics & numerical data*