BACKGROUND: While electronic health records (EHRs) play a key role in increasing colorectal cancer (CRC) screening by identifying individuals who are overdue, important shortfalls remain. OBJECTIVES: As part of the Strategies and Opportunities to STOP Colon Cancer (STOP CRC) study, we assessed the accuracy of EHR codes in identifying patients eligible for CRC screening. METHODS: We selected a stratified random sample of 800 study participants from 26 participating clinics, in the Pacific Northwest region of the USA. We compared data obtained through codes in the EHR to conduct a manual chart audit. A trained chart abstractor completed the abstraction of eligible and ineligible patients. RESULTS: Of 520 individuals in need of CRC screening, identified via the EHR, 459 were confirmed through chart review (positive predictive value = 88%). Of 280 individuals flagged as up-to-date in their screening per EHR data, 269 were confirmed through chart review (negative predictive value = 96%). Among the 61 patients incorrectly classified as eligible, 83.6% of disagreements were due to evidence of a prior colonoscopy or referral that was not captured in recognizable fields in the EHR. CONCLUSIONS: Our findings highlight importance of better capture of past screening events in the EHR. While the need for better population-based data is not unique to CRC screening, it provides an important example of the use of population-based data not only for tracking care, but also for delivering interventions.
RCT Entities:
BACKGROUND: While electronic health records (EHRs) play a key role in increasing colorectal cancer (CRC) screening by identifying individuals who are overdue, important shortfalls remain. OBJECTIVES: As part of the Strategies and Opportunities to STOP Colon Cancer (STOP CRC) study, we assessed the accuracy of EHR codes in identifying patients eligible for CRC screening. METHODS: We selected a stratified random sample of 800 study participants from 26 participating clinics, in the Pacific Northwest region of the USA. We compared data obtained through codes in the EHR to conduct a manual chart audit. A trained chart abstractor completed the abstraction of eligible and ineligible patients. RESULTS: Of 520 individuals in need of CRC screening, identified via the EHR, 459 were confirmed through chart review (positive predictive value = 88%). Of 280 individuals flagged as up-to-date in their screening per EHR data, 269 were confirmed through chart review (negative predictive value = 96%). Among the 61 patients incorrectly classified as eligible, 83.6% of disagreements were due to evidence of a prior colonoscopy or referral that was not captured in recognizable fields in the EHR. CONCLUSIONS: Our findings highlight importance of better capture of past screening events in the EHR. While the need for better population-based data is not unique to CRC screening, it provides an important example of the use of population-based data not only for tracking care, but also for delivering interventions.
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