Objective: This study aims to provide empirical evidence for the controversy about whether the inference is consistent if alternative hospital market definition methods are employed, and for which definition method is the best alternative to the predicted patient flow approach.Data sources: Collecting data from the discharge data of inpatients and hospital administrative data of Sichuan province in China in the fourth quarter of 2018.Study Design: We employed Herfindahl–Hirschman Index (HHI) as the proxy of market competition used as an example to measure the hospital market structure. Correlation coefficients of HHIs based on different definition methods were assessed. The corresponding coefficient of each HHI estimated in identical regression models was then compared. In addition, since the predicted patient flow method has been argued by the literature of its advantages compared with the previous approaches, we took the predicted patient flow as a reference to compare with the other approaches.Data Extraction Methods: We selected the common diseases with a significant burden, and 11 diseases were included (902,767 hospitalizations).Principal Findings: The correlation coefficients of HHIs based on different market definition methods are all significantly greater than 0, and the coefficients of HHIs are different in identical regression models. Taking the predicted patient flow approach as a reference, we found that the correlation coefficients between HHIs based on fixed radius and predicted patient flow approach is larger than others, and their parameter estimates are all consistent.Conclusion: Although the HHIs based on different definition methods are significantly and positively correlated, the inferences about the effectiveness of market structure would be inconsistent when alternative market definition methods are employed. The fixed radius would be the best alternative when researchers want to use the predicted patient flow method to define the hospital market but are hindered by the data limitations and computational complexity.