Accuracy of Medical Billing Data Against the Electronic Health Record in the Measurement of Colorectal Cancer Screening Rates release_rev_06a88c3d-e52a-4553-a323-7e6c39e55112

by Vivek A Rudrapatna, Benjamin S Glicksberg, Patrick Avila, Emily Harding-Theobald, Connie Wang, Atul J Butte


Objective: Administrative healthcare data are an attractive source of secondary analysis because of their potential to answer population-health questions. Although these datasets have known susceptibilities to biases, the degree to which they can distort measurements like cancer screening rates are not widely appreciated, nor are their causes and possible solutions. Methods: Using a billing code database derived from our institution's electronic health records (EHR), we estimated the colorectal cancer screening rate of average-risk patients aged 50-74 seen in primary care or gastroenterology clinic in 2016-2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review. Results: Out of 4,611 patients, an analysis of billing data suggested a 61% screening rate. Manual review revealed a positive predictive value of 96% (86-100%), negative predictive value of 21% (15-29%), and a corrected screening rate of 85% (81-90%). Most false negatives occurred due to exams performed outside the scope of the database - both within and outside of our institution - but 21% of false negatives fell within the database's scope. False positives occurred due to incomplete exams and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete exams (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%), and patients declining screening (13%). Conclusions: Although analytics on administrative data are commonly 'validated' by comparison to independent datasets, comparing our naive estimate to the CDC estimate (~60%) would have been misleading. Therefore, regular data audits using the complete EHR are critical to improve screening rates and measure improvement.
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Released as a post by Cold Spring Harbor Laboratory
Release Date 2019-08-13
Publisher Cold Spring Harbor Laboratory
Type  post
Stage   unknown
Date   2019-08-13
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