Over 20 million adults in the United States live with High Impact Chronic Pain (HICP), or chronic pain that limits life or work activities for ≥3 months. It is critically important to differentiate people with HICP from those who sustain normal activities although experiencing chronic pain. Therefore, we aim to help clinicians and researchers identify those with HICP by: (1) developing models that identify factors associated with HICP using the 2016 National Health Interview Survey (NHIS) and (2) evaluating the performances of those models overall and by sociodemographic subgroups (sex, age, and race/ethnicity). Our analysis included 32,980 respondents. We fitted logistic regression models with LASSO (a parametric model) and random forest (a nonparametric model) for predicting HICP using the whole sample. Both models performed well. The most important factors associated with HICP were those related to underlying ill-health (arthritis and rheumatism, hospitalizations, and emergency department visits) and poor psychological well-being. These factors can be used for identifying higher-risk sub-groups for screening for HICP. We will externally validate these findings in future work. We need future studies that longitudinally predict the initiation and maintenance of HICP, then use this information to prevent HICP and direct patients to optimal treatments. PERSPECTIVE: Our study developed models to identify factors associated with high-impact chronic pain (HICP) using the 2016 National Health Interview Survey. There was homogeneity in the factors associated with HICP by gender, age and race/ethnicity. Understanding these risk factors is crucial to support the identification of populations and individuals at highest risk for developing HICP and improve access to interventions that target these high-risk subgroups.