Addressing Missing Data in Multisite Learning Health Systems: Statistical Imputation Using MICE

A learning health system (LHS), as defined by the Institute of Medicine, is an organizational approach that integrates research and practice in a feedback loop, ensuring that knowledge gained from practice directly informs improvements in care and policy. These systems are increasingly using client
data collected in real-world settings to enhance clinical knowledge, innovation, and quality of care. However, data collected in service settings are prone to data quality challenges, including higher rates of missingness than controlled research settings, requiring innovative statistical solutions to reduce biases associated with missing data.

The National Institute of Mental Health's Early Psychosis Intervention Network (EPINET) is an LHS comprising over 100 clinics across the United States, embedded within 8 regional scientific hubs, that provide Coordinated Specialty Care (CSC) services to individuals experiencing a first episode of psychosis (FEP). All EPINET clinics administer a Core Assessment Battery (CAB) that measures several key domains of FEP treatment and recovery, which treatment teams use to inform clinical decision-making and measure client progress. CAB data consolidated across EPINET clinics comprise a rich national data set, providing a valuable resource for FEP researchers.

This paper discusses the application of multiple imputation by chained equations (MICE) to handle missing data in the consolidated CAB dataset. We present data on fractions of missing information (FMI) within each regional hub and in aggregate across hubs, and the results from MICE for generating imputed cross-hub CAB data. These findings may be helpful for other researchers, such as those working within learning health systems, to handle missing data collected from multisite service settings in which site is a determinant of missingness.

missingness

MICE

imputation

mental health records

Learning Health Systems 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2025