Physician assessment of disease activity in XXXX subtypes. Analysis of data extracted from electronic medical records.

@entity1 @entity625 @entity158 @entity4932
OBJECTIVE: Although electronic medical records (EMRs) have facilitated care for @entity1 with @entity4932 ( @entity4932 ), analyses of treatment outcomes have required paper based or manually re-entered data. We have started EMR discrete data entry for @entity4932 @entity1 visits, including joint examination and global assessment, by physician and @entity1 . In this preliminary study, we extracted data from the EMR to Xenobase    (TransMed Systems, Inc., Cupertino, CA), an application permitting cohort analyses of the relationship between global assessment to joint examination and subtype. METHODS: During clinic visits, data were entered into discrete fields in ambulatory visit forms in the EMR (EpicCare   , Epic Systems, Verona, WI). Data were extracted using Clarity Reports, then de-identified and uploaded for analyses to Xenobase   . Parameters included joint examination, ILAR diagnostic classification, physician global assessment, @entity1 global assessment, and @entity1 @entity158 score. Data for a single visit for each of 160 @entity1 over a 2 month period, beginning March, 2010, were analyzed. RESULTS: In systemic @entity4932 @entity1 , strong correlations for physician global assessment were found with @entity158 score, joint count and @entity1 assessment. In contrast, physician assessment for @entity1 with persistent oligoarticular and rheumatoid factor negative @entity1 showed strong correlation with joint counts, but only moderate correlation with @entity158 scores and @entity1 global assessment. Conversely, for @entity4932 @entity1 , physician assessment correlated strongly with @entity158 scores, and moderately with joint count and @entity1 global assessment. @entity4932 @entity1 , the smallest group studied, showed moderate correlation for all three measures. @entity1 global assessment for systemic @entity1 showed strong correlations with @entity158 scores and joint count, similar to data for physician assessment. For polyarticular and @entity4932 @entity1 , correlation of @entity1 global assessment with @entity158 scores was strong. Moderate correlations were found between @entity1 global assessment and joint count in oligoarticular and polyarticular @entity1 . CONCLUSION: Data extraction from the EMR is feasible and useful to evaluate @entity4932 @entity1 for indicators of treatment responsiveness. In this pilot study, we found correlates for physician global assessment of @entity625 differed, according to disease subtype. Further data extraction and analyses will determine if these findings can be confirmed, and will assess other outcome measures, compare longitudinal responses to treatment, and export extracted data to multi-center databases.
