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Research Objective: To improve access to specialty care, NYC Health + Hospitals (H + H), the nation's largest public health system serving over 1 million New Yorkers, implemented eConsult in 2016 – an electronic health record (EHR) integrated referral management system. A pre‐ and post‐intervention study that was conducted early on in the pilot showed that the eConsult implementation was associated with substantial reduction in wait times for specialty care appointments. However, due to an enterprise EHR upgrade and referral operational change during the prior study period, comparative evidence controlling for time‐varying factors is needed to support inference. Now that eConsult has been expanded further, we have a more stable study sample to evaluate the impact of eConsult on access to specialty care. Study Design: We used a quasi‐experimental difference‐in‐difference approach with data from January 2018 to December 2020. Data from March–May 2020 was excluded due to the NYC COVID‐19 first wave outbreak. Non‐eConsult clinics (controls) were matched to each eConsult clinic based on specialty and parallel trend. Our primary outcome was the percentage of referrals scheduled for an appointment occurring within 30 days. Secondary outcomes were monthly number of referrals requiring appointment and the percentage of referrals resolved without appointment. We performed bivariate comparison to assess any differences during pre and post implementation for both eConsult and control groups. eConsult main effect was estimated by multi‐level log‐binomial model with random intercepts of comparison groups and clinic as well as fixed effects of eConsult program and pre/post periods. Generalized estimating equations (GEE) were also run for each comparison pair to investigate variation among specialties. Population Studied: We identified 125,708 referrals in 15 eConsult clinics and 20 non‐eConsult clinics across 10 hospitals at NYC H + H. Nine specialties were identified: hematology, dermatology, otolaryngology, general surgery, neurology, orthopedic surgery, podiatry, pulmonary, and rehabilitation. Principal Findings: After implementation, the monthly number of referrals requiring appointments decreased 27.7% in eConsult clinics (271 to 196, p < 0.001) but increased 16% in control clinics (243 to 281, p < 0.001); within 30 days, the scheduled appointment rate increased by 30% in eConsult clinics (25.4% to 40.7%, p < 0.001) but decreased by 7% in control clinics (29.5% to 27.4%, p < 0.001). In eConsult clinics, 7% of referrals were resolved without an appointment (ranged 1% to 22%). Multi‐level model showed the adjusted eConsult program effect is RR = 1.53 (95% CI: 1.48–1.59). GEE models showed effect sizes differ by comparison pairs (RR ranged 0.73–7.06,10 out of 13 pairs showed improvement). Conclusions: The increased rate of appointments received within 30 days and the reduction of assumed unnecessary visits were greater among eConsult clinics, varied by specialty. The eConsult rate in this sample is substantially smaller than the overall H + H eConsult rate as well as that reported from other health systems, indicating that the reduction in referral visit demand might not be the only mediator. Programmatic engagement to streamline referral workflow and referring provider behavior change might also be driving factors. Implications for Policy or Practice: eConsult implementation could lead to improved access to specialty care in addition to other programmatic enhancements, ongoing support and monitoring of referral management workflows and practitioner edification value. [ABSTRACT FROM AUTHOR]