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Telemedicine expanded rapidly in the United States during the COVID-19 public health emergency as Medicare and state Medicaid programs relaxed coverage restrictions. Diabetes affects about 37 million Americans, and key outcomes such as HbA1c, blood pressure, and LDL cholesterol are routinely tracked in electronic health records. However, the causal impact of telemedicine expansion on these outcomes remains uncertain, as simple pre–post comparisons are confounded by concurrent trends such as the pandemic and seasonal variation. Randomized policy experiments are impractical, leaving a gap in high-quality causal evidence. We argue that Bayesian structural time series (BSTS) applied to state-level EHR aggregates provides a strong alternative. BSTS constructs a synthetic counterfactual from similar states, modeling trend and seasonality to estimate what outcomes would have been without telemedicine expansion. This allows clearer separation of policy effects from underlying time dynamics and produces interpretable estimates with uncertainty bounds. Unlike difference-in-differences, BSTS does not rely on parallel trends assumptions that may be violated in this context. It offers a transparent framework for causal inference using routinely available aggregated data. Policymakers should prioritize such causal methods when evaluating whether telemedicine expansions should become permanent rather than relying on descriptive before–after analyses.

Telemedicine expanded rapidly during COVID-19 as Medicare and states relaxed long-standing restrictions. Diabetes outcomes (HbA1c, blood pressure, LDL cholesterol) are routinely tracked in EHRs, yet causal evidence that telemedicine improves these outcomes remains limited. Pre-post analyses cannot separate telemedicine effects from confounding time trends such as seasonality and pandemic-related changes, while randomized state-level policy trials are infeasible. This leaves a key evidence gap for policy decisions. Bayesian structural time series (BSTS) using state-level EHR aggregates is the most suitable approach for causal inference, constructing counterfactual outcomes from similar donor states. BSTS accounts for trends, seasonality, and autocorrelation, and uses synthetic control principles to reduce confounding. It also provides uncertainty estimates and works with routinely collected aggregate data. Policymakers should require BSTS-based evidence before making telemedicine coverage permanent. Researchers should apply these methods to existing policy variation and share data and code. States should build routine EHR-based monitoring systems for diabetes outcomes. Causal evaluation of telemedicine is feasible now using existing data and methods. Relying on pre-post studies or waiting for randomized trials delays actionable evidence needed for policy decisions.