The Quality of Routinely Collected Data: Using the "Principal Diagnosis" in Emergency Department Databases as an Example - Liaw, S.; Chen, Huei-yang; Maneze, D.; Taggart, J.; Dennis, S.; Vagholkar, S.; Bunker, J. (Health Informatics Society of Australia, 2012)
Objectives: This paper aims to estimate the reliability of using “principal diagnosis” to identify people with diabetes mellitus (DM), cardiovascular diseases (CVD), and asthma or chronic obstructive pulmonary disease (COPD) in Firstnet, the emergency department (ED) module of the NSW Health Electronic Medical Record (eMR). Methods: A list of patients who attended a community hospital ED in 2009 with a specific “principal diagnosis” of DM, CVD, or asthma/COPD, or inferred based on possible keywords, was generated from Firstnet. This Firstnet list was compared with a list extracted from the underlying eMR database tables, using similar specific and possible coded terms. The concordance for an episode of care and for the overall was calculated. Patients on the Firstnet list who were admitted had their discharge summaries audited to confirm the principal diagnosis. The proportion of admitted patients correctly identified as having one of the chronic diseases was calculated. Results: The Firstnet list contained 2,559 patients with a principal diagnosis of DM, CVD, or asthma/COPD. The concordance (episode) of the Firstnet list with the eMR list were: 87% of CVD cases, 69% of DM and 38% of asthma/COPD cases. The audit of the discharge summaries of the Firstnet patients who were admitted confirmed the diagnosis of DM, asthma/COPD, and CVD for 79%, 66%, and 56% of the patients respectively.Discussion: An empirical method to examine the accuracy of the prinicipal diagnosis in Firstnet is described. The incomplete concordance of diagnoses of the selected chronic diseases generated via different modules of the same information system raises doubts about the reliability of data and information quality collected, stored and used by the eMR. Further research is required to understand the determinants of data quality and develop tools to automate data quality assessment and management. This is particularly important with the increasing use of eMR in routine clinical practice and use of routinely collected clinical data for clinical and research purposes.