We presented the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. The proposed method provided a convenient tool to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9, with various effect sizes. The Strengthening Translational Research in Diverse Enrollment (STRIDE) study was used as an example.