Factors Contributing to the Enrollment of Ambiguous Medical Insurance Cases: A Retrospective Study at a Tertiary Hospital in Beijing

Abstract

This study aimed to identify and evaluate factors associated with the enrollment of ambiguous medical insurance cases at a tertiary care hospital in Beijing. A retrospective analysis was conducted on 386 medical insurance cases with ambiguous classification or coverage status, documented between 2020 and 2022. Multiple regression analysis was performed using SPSS version 26 to assess the effects of policy implementation, the quality of medical records, and personnel awareness on enrollment accuracy. The primary contributing factors to ambiguity in insurance case enrollment were insufficient understanding of policy requirements (33%), non-standardized medical records (30%), and operational errors (35%). Regression analysis indicated that both the level of policy implementation ( β = −0.42, p < 0.01) and the integrity of medical records ( β = −0.38, p < 0.05) were statistically significant predictors of enrollment accuracy. Enhancing training related to policy interpretation, improving the quality control of medical records, and optimizing information systems may contribute to a reduction in ambiguous medical insurance case enrollment. These strategies can support more accurate claims processing and efficient use of insurance resources.