Data electronically extracted from the electronic health record require validation.

OBJECTIVES

Determine sources of error in electronically extracted data from electronic health records.

STUDY DESIGN

Categorical and continuous variables related to early-onset neonatal hypoglycemia were preselected and electronically extracted from records of 100 randomly selected neonates within 3479 births with laboratory-proven early-onset hypoglycemia. Extraction language was written by an information technologist and data validated by blinded manual chart review. Kappa coefficient assessed categorical variables and percent validity continuous variables.

RESULTS

8/23 (35%) categorical variables had acceptable Κappa (1-0.81); 5/23 (22%) had fair-slight agreement, Κappa < 0.40. Notably, "hypoglycemia" had poor agreement, Κappa 0.16. In contrast, 6/8 continuous variables had validity ≥ 94%. After correcting extraction language, 6/9 variables were corrected and inter-rater validation improved. However, "hypoglycemia" was not corrected, remaining an issue.

CONCLUSIONS

Data extraction without validation procedures, especially categorical variables using International Classification of Diseases-9 (ICD-9) codes, often results in incorrect data identification. Electronically extracted data must incorporate built-in validating processes.