A cohort study on physician documentation and the accuracy of administrative data coding to improve passive surveillance of transient ischaemic attacks

Abstract

Background: Administrative health data are valuable in health research and disease surveillance, but have low to moderate sensitivity in identifying transient ischaemic attacks (TIA) in the emergency department (ED). We aimed to identify the predictors of coding accuracy for TIA. Methods: The study population was obtained from two ongoing studies on the diagnosis of TIA, minor stroke and stroke mimic. ED charts were manually reviewed by a stroke neurologist to obtain the clinical diagnosis, patient characteristics and content of physician documentation. Administrative data codes were compared with the chart-adjudicated diagnosis to determine cases of misclassification by administrative data. Univariable regression was used to evaluate candidate predictors of disagreement, and the significant variables were tested in a multivariable model to obtain an adjusted estimate of effect. Results: Among 417 patients (39.1% TIA, 37.2% minor stroke and 23.7% stroke mimics), there were 122 cases of disagreement between adjudications and administrative data codes for the diagnosis of TIA. The majority of disagreement (n=103/122, 84.4%) arose from adjudicated TIA cases that were misclassified as non-TIA in administrative data coding. There were 78 (18.7%) charts with documented uncertain diagnosis, and 73 (17.5%) charts had no definite diagnosis. The relative risk of disagreement between chart adjudication and administrative data coding when the final diagnosis was uncertain or absent was 1.82(1.36, 2.44) and the risk difference was 18.5%. Multivariable logistic regression analyses confirmed this association using different case definition algorithms. Conclusions: In suspected patients with TIA and minor stroke presenting to the ED, physician documentation was the dominant factor in coding accuracy, supporting the concept that physicians are active participants in administrative data coding. Strategies to improve chart documentation are predicted to have a positive effect on coding accuracy.