Patient's decision and experience in the multi-channel appointment context: An empirical study

BACKGROUND: Long waiting time for treatment in the outpatient department has long been a complaint and has influenced patient's experience. It is critical to schedule patients for doctors to reduce patient's waiting time. Nowadays, multi-channel appointment has been provided for patients to get medical services, especially for those with severe illnesses and remote distance. This study aims to explore the factors that influence patient appointment channel choice in the context of multi-channel appointments, and how channel choice affects the waiting time for offline visiting. METHODS: We collected outpatient appointment records from both online and offline appointment channels to conduct our empirical research. The empirical analysis is conducted in two steps. We first analyze the relationship between appointment channel choice and patient's waiting time and then the relationships between three determinants and appointment channel choice. The ordinary least squares and the logistic regression model are used to obtain the empirical results. RESULTS: Our results show that a patient with an online appointment decision has a shorter consultation waiting time compared with a patient with on-site appointment (β = −0.320, p < 0.001). High-quality resource demand (β = 0.349, p < 0.001), high-severity disease (β = 0.011, p < 0.001), and high non-disease costs (β = 0.039, p < 0.001) create an obvious incentive for patients to make appointments via the Internet. Further, only the effect of non-disease cost on channel choice is lower for patients with multiple visit histories (β = −0.021, p < 0.001). CONCLUSIONS: Our study confirms the effect of Internet use on reducing patient's waiting time. Patients consider both health-related risk factors and cost-related risk factors to make decisions on appointment channels. Our study produces several insights, which have implications for channel choice and patient's behavior literature. More importantly, these insights contribute to the design of appointment systems in hospitals.