Does Perceived Provider Network Strength Foster Telehealth Usage among Insured Populations?

Florent Nkouaga^*

• National Association of Insurance Commissioners, Kansas City, United States

In the wake of the Affordable Care Act's coverage expansions and the COVID-19 pandemic's urgent demand for remote services, telehealth has become a critical gateway to healthcare for underserved and access-challenged populations across the United States. While telehealth offers the potential to reduce barriers related to distance, transportation, and provider shortages, persistent disparities in broadband access, digital literacy, and socioeconomic status continue to shape utilization patterns. Method: Utilizing data from the 2024 Financial Inclusion Survey, this study applies a dual modeling strategy. Single-stage Probit, OLS, and Ologit models are used to analyze telehealth utilization and satisfaction. To address nonrandom selection into telehealth, a Heckman two-stage model—incorporating a cubic polynomial for perceived network adequacy and comprehensive controls including an urban/suburban geographic indicator—is implemented. Results: Telehealth utilization in this nationally representative cross-sectional sample is unevenly distributed, with higher rates observed in urban and suburban communities. The analysis shows that perceived network adequacy has a nonlinear association with telehealth use: moderate adequacy increases utilization, while very high adequacy may reduce it. Administrative convenience and affordability are associated with higher satisfaction in single-stage models; however, these effects are attenuated after correcting for selection bias (Inverse Mills Ratio, β = −0.778, p < 0.05). Discussion: Unobserved factors influencing telehealth adoption also bias satisfaction estimates, highlighting the necessity of correcting for selection effects. The cubic modeling approach effectively captures nonlinear associations between access and satisfaction. Conclusion: Accurate assessment of telehealth's impact requires robust adjustment for selection bias. These findings have significant policy implications for improving network adequacy, digital access, and operational efficiency to ensure equitable telehealth adoption and satisfaction, particularly for underserved and non-urban communities.