The Impact of DIP on the Operational Performance of a Dental Hospital Based on The Interrupted Time Series Model
- VernacularTitle:基于间断时间序列模型的DIP对口腔医院运营绩效影响的研究
- Author:
Rong YANG
1
;
Yiru LUO
;
Jing GUO
Author Information
1. 深圳市口腔医院 广东 深圳 518022
- Publication Type:Journal Article
- Keywords:
diagnosis-intervention packet;
refinement management;
dental hospital;
operational performance;
Interrupted Time Series Model
- From:
Chinese Hospital Management
2024;44(12):42-45
- CountryChina
- Language:Chinese
-
Abstract:
Objective It aims to explore the impact of Diagnosis-Intervention Packet (DIP)refinement management on the operational performance of a dental specialty hospital,providing empirical evidence for further optimization of hospital management.Methods Operational performance data from a dental specialty hospital in Shenzhen,Guangdong Province,were collected from January 2022 to June 2024.An interrupted time series analysis was employed to assess the impact of DIP refinement management on hospital operational performance. Results Following the implementation of DIP refined management,the hospital's per capital output significantly increased by 4058.64 yuan(P<0.001)and continued to rise by 1292.44 yuan monthly(P<0.001).The proportion of consumables exhibited a slight decline,with a monthly decrease of 0.78 percentage point (P=0.04).The readmission rate significantly decreased by 1.13 percentage point (P=0.03),with a continuous monthly reduction of 0.15 percentage point (P=0.04).Patient satisfaction showed a slight upward trend,with a monthly increase of 0.61 percentage point (P=0.04).Additionally,changes in the cost-effectiveness ratio,return on assets,drug ratio,average length of stay,bed occupancy rate,cure rate,and the number of medical insurance violations were not statistically significant (P>0.05).Conclusion DIP refined management demonstrated significant economic benefits and improvements in service quality within the specialized dental hospital,though its impact on service efficiency was limited.Future efforts should focus on enhancing data-driven management decisions,optimizing personalized treatment pathways,and establishing patient feedback mechanisms to steadily improve operational performance.
