Analysis of outpatient visits to primary care clinics and its influencing factors using a decision tree model

Objectives: The process of locating health facilities has been studied extensively using mathematical optimization (e.g., covering model); however, few researchers have applied the techniques of data mining to this problem. This study proposes a novel prediction model, based on the Geographic Information System and data mining to assist in the selection of optimum locations for medical clinics. Methods: This study examined 306 medical clinics in Taipei, focusing on those with a high number of outpatients, using 19 variables associated with location decisions. A CART decision tree was used in the development of the model based on the collected variables. Results: The decision tree model indicated that household disposable income has the strongest impact on the number of outpatients, followed by clinic density, and gender. Regression analysis identified age, and distance to the nearest Mass Rapid Transit station as the two factors with a significant effect on the number of outpatients at a given clinic (p<0.001). Conclusions: This study constructed a novel prediction model to aide in identifying the optimal location for a clinic and evaluating options with regard to clinic relocation.