Background Screening decreases colorectal cancer (CRC) incidence and mortality, yet almost half of age-eligible patients are not screened at recommended intervals. Objective To determine whether interventions using electronic health records (EHRs), automated mailings, and stepped increases in support improve CRC screening adherence over 2 years. Design 4-group, parallel-design, randomized, controlled comparative effectiveness trial with concealed allocation and blinded outcome assessments. (ClinicalTrials.gov: NCT00697047) Setting 21 primary care medical centers. Patients 4675 adults aged 50 to 73 years not current for CRC screening. Intervention Usual care, EHR-linked mailings (“automated”), automated plus telephone assistance (“assisted”), or automated and assisted plus nurse navigation to testing completion or refusal (“navigated”). Interventions were repeated in year 2. Measurements The proportion of participants current for screening in both years, defined as colonoscopy or sigmoidoscopy (year 1) or fecal occult blood testing (FOBT) in year 1 and FOBT, colonoscopy, or sigmoidoscopy (year 2). Results Compared with those in the usual care group, participants in the intervention groups were more likely to be current for CRC screening for both years with significant increases by intensity (usual care, 26.3% [95% CI, 23.4% to 29.2%]; automated, 50.8% [CI, 47.3% to 54.4%]; assisted, 57.5% [CI, 54.5% to 60.6%]; and navigated, 64.7% [CI, 62.5% to 67.0%]; P < 0.001 for all pair-wise comparisons). Increases in screening were primarily due to increased uptake of FOBT being completed in both years (usual care, 3.9% [CI, 2.8% to 5.1%]; automated, 27.5% [CI, 24.9% to 30.0%]; assisted, 30.5% [CI, 27.9% to 33.2%]; and navigated, 35.8% [CI, 33.1% to 38.6%]). Limitation Participants were required to provide verbal consent and were more likely to be white and to participate in other types of cancer screening, limiting generalizability. Conclusion Compared with usual care, a centralized, EHR-linked, mailed CRC screening program led to twice as many persons being current for screening over 2 years. Assisted and navigated interventions led to smaller but significant stepped increases compared with the automated intervention only. The rapid growth of EHRs provides opportunities for spreading this model broadly. Primary Funding Source National Cancer Institute, National Institutes of Health.

Objective We review uses of electronic healthcare data algorithms, measures of their accuracy, and reasons for prioritizing one measure of accuracy over another. Study design and setting We use real studies to illustrate the variety of uses of automated healthcare data in epidemiologic and health services research. Hypothetical examples show the impact of different types of misclassification when algorithms are used to ascertain exposure and outcome. Results High algorithm sensitivity is important for reducing the costs and burdens associated with the use of a more accurate measurement tool, for enhancing study inclusiveness, and for ascertaining common exposures. High specificity is important for classifying outcomes. High positive predictive value is important for identifying a cohort of persons with a condition of interest but that need not be representative of or include everyone with that condition. Finally, a high negative predictive value is important for reducing the likelihood that study subjects have an exclusionary condition. Conclusion Epidemiologists must often prioritize one measure of accuracy over another when generating an algorithm for use in their study. We recommend researchers publish all tested algorithms—including those without acceptable accuracy levels—to help future studies refine and apply algorithms that are well-suited to their objectives.

Agency for Healthcare Research and Quality.