Measuring mild cognitive impairment using routinely collected health-care data: A conceptual framework and guidance for embedded pragmatic clinical trials

Mild cognitive impairment (MCI) is increasingly measured using routinely collected health-care data in embedded pragmatic clinical trials. However, accurate ascertainment remains challenging because key diagnostic elements, such as cognitive function and functional status, are often incompletely captured. We identified three broad categories for measuring MCI using routinely collected health-care data: documentation-based approaches, which identify clinician-recognized MCI; clinical criteria-based approaches, which reconstruct the diagnostic construct using available clinical information; and prediction-based approaches, which estimate the likelihood of MCI using statistical or machine learning models. These approaches measure distinct constructs and differ in validity and scalability, and susceptibility to bias. For eligibility and outcome ascertainment, clinical criteria-based approaches should be prioritized whenever feasible. Documentation-based approaches may serve as complementary measures of clinician-recognized MCI, whereas prediction-based approaches are best suited for screening and risk stratification. Continued advances in phenotype development, data standardization, and validation are needed to support accurate and equitable MCI ascertainment.