Assessing Data Quality: From Concordance, through Correctness and Completeness, to Valid Manipulatable Representations

The papers by Stein et al.1 and Aronsky and Haug2 address the quality of the data found in clinical record systems. Stein et al. approach the problem as one of internal consistency. Their paper explores concordance within record systems, exploring the extent to which evidence found in one part of a clinical database is consistent with evidence found in another part. Specifically, they examine agreement between entries in a free-text narrative field with data found in coded fields. Aronsky and Haug examine concordance across two different clinical record systems—the HELP computerized clinical record system and a reference standard consisting of the sum of all information available in the paper chart and the computerized clinical record. Aronsky and Haug complement their appraisal of concordance with an outcome evaluation, determining the level of agreement in clinical severity indexes resulting from the information contained in the different record systems.Stein et al. use the measure of internal concordance to alert users of a computerized record system to the fact that they may get misleading answers unless they query each field that might contain a piece of information and resolve any discrepancies. Aronsky and Haug argue the need for equivalence in recommendations based on the clinical record, regardless of which form …