Determination of Eligibility for Influenza Research: A Clinical Informatics Approach

BACKGROUND: A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for a test-negative, case-control study to determine influenza vaccine effectiveness, to improve enrollment over manual records review. Further testing may enhance the CIA for increased efficiency. METHODS: The CIA generated a daily screening list by querying all medical record databases for patients admitted in the last 3 days, using specified terms and diagnosis codes located in admission notes, emergency department notes, chief complaint upon registration, or presence of a respiratory viral panel charge or laboratory result (RVP). Classification and regression tree analysis (CART) and multivariable logistic regression were used to refine the algorithm. RESULTS: Using manual records review, 204 patients (<4/day) were approached and 144 were eligible in the 2014–2015 season compared with 3531 (12/day) patients who were approached and 1136 who were eligible in the 2016–2017 season using a CIA. CART analysis identified RVP as the most important indicator from the CIA list for determining eligibility, identifying 65%–69% of the samples and predicting 1587 eligible patients. RVP was confirmed as the most significant predictor in regression analysis, with an odds ratio (OR) of 4.9 (95% confidence interval [CI], 4.0–6.0). Other significant factors were indicators in admission notes (OR, 2.3 [95% CI, 1.9–2.8]) and emergency department notes (OR, 1.8 [95% CI, 1.4–2.3]). CONCLUSIONS: This study supports the benefits of a CIA to facilitate recruitment of eligible participants in clinical research over manual records review. Logistic regression and CART identified potential eligibility screening criteria reductions to improve the CIA’s efficiency.