There are two types of underlying cause of data errors: x Systematic errors are errors that can be attributed to a flaw or discrepancy in adherence to standard operating procedures or systems. x Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measures of a constant attribute or quantity are taken. If the diagnosis coding errors were the result of poor handwriting or transcription errors, they would be considered random errors. Carelessness rather than lack of training leads to random errors (Art, DeKeizer, & Scheffer, 2002).
Preventing, Detecting, and Fixing Data Errors Both systematic and random errors lead to poor-quality data and information. Errors that are not preventable need to be detected so that they can be corrected.
Framework for ensuring data quality in a centralized health care database is published by Arts, DeKeizer, and Scheffer (2002) as follow:
Data Error Prevention x Compose a minimum set of necessary data items x Define data and data characteristic in a data dictionary x Develop a data collection protocol x Create user friendly data entry forms or interface x Compose data checks x Create a quality assurance plan x Train and motivate users