Data electronically extracted from the electronic health record require validation.
J Perinatol
; 39(3): 468-474, 2019 03.
Article
in En
| MEDLINE
| ID: mdl-30679823
ABSTRACT
OBJECTIVES:
Determine sources of error in electronically extracted data from electronic health records. STUDYDESIGN:
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Information Storage and Retrieval
/
Electronic Health Records
/
Data Accuracy
Type of study:
Observational_studies
/
Prognostic_studies
Limits:
Humans
/
Newborn
Language:
En
Journal:
J Perinatol
Journal subject:
PERINATOLOGIA
Year:
2019
Document type:
Article
Affiliation country:
Estados Unidos