Impact of slack resources on healthcare costs in tertiary and secondary hospitals: a panel data study of public hospitals in Beijing from 2015 to 2019

Chen Chen; Xinrui Song; Junli Zhu

doi:10.1136/bmjopen-2022-068383

Impact of slack resources on healthcare costs in tertiary and secondary hospitals: a panel data study of public hospitals in Beijing from 2015 to 2019

BMJ Open. 2023 Apr 19;13(4):e068383. doi: 10.1136/bmjopen-2022-068383.

Authors

Chen Chen^{1

2}, Xinrui Song³, Junli Zhu^{4

2}

Affiliations

¹ School of Public Health, Capital Medical University, Beijing, China.
² Research Center for Capital Health Management and Policy, Beijing, China.
³ Beijing Chest Hospital, Capital Medical University, Beijing, China.
⁴ School of Public Health, Capital Medical University, Beijing, China smallying@126.com.

Abstract

Objective: This study aims to explore the relationship between slack resources and cost consumption index in tertiary and secondary hospitals and to provide targeted healthcare resource utilisation recommendations for tertiary and secondary hospital managers.

Design: This is a panel data study of 51 public hospitals in Beijing from 2015 to 2019.

Setting: Tertiary and secondary public hospitals in Beijing. Data envelope analysis was used to calculate the slack resources. Regression models were used to explore the relationship between slack resources and healthcare costs.

Participants: A total of 255 observations were collected from 33 tertiary hospitals and 18 secondary hospitals.

Outcome measures: Slack resources and healthcare costs in tertiary and secondary public hospitals in Beijing from 2015 to 2019. Linear or curve relationship between slack resources and healthcare costs in tertiary and secondary hospitals.

Results: The cost of healthcare in tertiary hospitals has always been higher than in secondary hospitals, and the slack resources in secondary hospitals have always been worse than in tertiary hospitals. For tertiary hospitals, the cubic coefficient of slack resources is significant (β=-12.914, p<0.01) and the R² of cubic regression is increased compared with linear and quadratic regression models, so there is a transposed S-shaped relationship between slack resources and cost consumption index. For secondary hospitals, only the first-order coefficient of slack resources in the linear regression was significant (β=0.179, p<0.05), so slack resources in secondary hospitals were positively related to the cost consumption index.

Conclusions: This study shows that slack resources' impact on healthcare costs differs in tertiary and secondary public hospitals. For tertiary hospitals, slack should be kept within a reasonable range to control excessive growth in healthcare costs. In secondary hospitals, keeping too many slack resources is not ideal, so managers should adopt strategies to improve competitiveness and service transformation.

Keywords: Health economics; Health policy; Organisation of health services; Organisational development; Public health.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Health Care Costs*
Hospitals, Public*
Humans