Evidence map›Paper›PMID 40910055›Full record

ArticleFrontiers in public health2025

An analysis of operational efficiency and its influencing factors in traditional Chinese medicine hospitals in Shaanxi Province, China.

Jieming Zhang, Yan Li, Huiyang Qu, Penggang Chen

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Jieming ZhangDepartment of Hospital Information Network, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yan LiDepartment of Hospital Information Network, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Huiyang QuDepartment of Hospital Information Network, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Penggang ChenDepartment of Hospital Information Network, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Over the past decade, traditional Chinese medicine (TCM) hospitals in China have experienced significant growth. However, their developmental status remains unclear, posing challenges to the formulation and optimization of relevant healthcare policies. This study aimed to assess the operational efficiency of TCM hospitals in Shaanxi Province and explore its influencing factors, thereby providing empirical evidence to support performance improvement. Methods: A total of 167 TCM hospitals in Shaanxi Province were included in this study. The Bootstrap Data Envelopment Analysis (Bootstrap-DEA) was utilized to calculate bias-corrected operational efficiency scores. Subsequently, Tobit regression analysis was used to identify significant determinants of efficiency, offering a comprehensive understanding of influencing factors. Results: After bias-correction, operational efficiency scores decreased across all hospitals. Tertiary hospitals had the highest mean operational efficiency score (0.457), followed by secondary hospitals (0.374), unclassified hospitals (0.354), and primary hospitals (0.329). Tobit regression indicated that the total number of visits, number of discharged patients, and bed occupancy rate positively influenced efficiency scores ( Conclusion: The development of TCM hospitals in Shaanxi Province remains uneven, and overall operational efficiency is suboptimal. Enhancing efficiency requires targeted strategies, such as optimizing staffing levels, reducing the average length of stay, and improving bed utilization. These findings offer practical insights for policymakers to support the sustainable development of TCM hospitals.

Indexed as

Efficiency, OrganizationalHospitalsMedicine, Chinese TraditionalChinaHumansbootstrap DEA modelhealthcare managementhospital of traditional Chinese medicineoperational efficiencytechnical efficiencyTobit model

Identifiers

PMID40910055
PMCPMC12405229

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.