Teleconsultation in China, primarily based on appointment systems, often face walk-ins who have not scheduled in advance, leading to delays for scheduled patients. To minimize waiting and overtime costs, we propose a two-stage static scheduling model for optimal patient allocation. Additionally, a dynamic update model is developed for scheduled patients, integrated with a rolling horizon optimization strategy, resulting in a Greedy-based Rolling Horizon Optimization (GRHO) approach. We introduce two insertion principles for walk-ins: GRHO and GRHO*. The SAA-VNS-Integer L-Shaped (SVILS) algorithm is employed to solve the static model, and results show that GRHO* outperforms both GRHO and SVILS. GRHO* reduces waiting time by up to 67.86%, and increases teleconsultation room resource utilization to 89.29%, offering valuable insights for teleconsultation management.