TY - JOUR
T1 - Causal Evidence for Telemedicine in Diabetes Control: Why Bayesian Structural Time Series on State-Level EHR Aggregates Should Guide Policy
AU - Ivan Petrov
AU - Olga Ivanova
AU - Dmitry Smirnov
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2024
VL - 3
IS - 2
SP - 92
N2 - 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.
UR - https://cirpublications.com/n955173340
ER -