Whether Public Hospital Reform Affects the Hospital Choices of Patients in Urban Areas: New Evidence from Smart Card Data

Abstract

The effects of public hospital reforms on spatial and temporal patterns of health-seeking behavior have received little attention due to small sample sizes and low spatiotemporal resolution of survey data. Without such information, however, health planners might be unable to adjust interventions in a timely manner, and they devise less-effective interventions. Recently, massive electronic trip records have been widely used to infer people’s health-seeking trips. With health-seeking trips inferred from smart card data, this paper mainly answers two questions: (i) how do public hospital reforms affect the hospital choices of patients? (ii) What are the spatial differences of the effects of public hospital reforms? To achieve these goals, tertiary hospital preferences, hospital bypass, and the efficiency of the health-seeking behaviors of patients, before and after Beijing’s public hospital reform in 2017, were compared. The results demonstrate that the effects of this reform on the hospital choices of patients were spatially different. In subdistricts with (or near) hospitals, the reform exerted the opposite impact on tertiary hospital preference compared with core and periphery areas. However, the reform had no significant effect on the tertiary hospital preference and hospital bypass in subdistricts without (or far away from) hospitals. Regarding the efficiency of the health-seeking behaviors of patients, the reform positively affected patient travel time, time of stay at hospitals, and arrival time. This study presents a time-efficient method to evaluate the effects of the recent public hospital reform in Beijing on a fine scale.

Equity in Health-Seeking Behavior of Groups Using Different Transportations

“…[ 7 , 8 ]. Such data have long been criticized for their limited sample size, high cost, and inefficiency [ 9 , 10 ]. The recent advances in data collection and analysis methods allow for investigation into human mobilities and urban dynamics with a higher resolution using big data, such as smart card, taxi trajectory, and mobile phone data [ 11 , 12 , 13 ].…”