Evidence map›Paper›PMID 40299830›Full record

ArticlePloS one2025

Increasing hospital administrative efficiency via optimized office automation systems: A PDCA cycle approach.

Ruiwen Lu, Yiming Zhang, Likang Luo, Quan Yuan, Shuihong Chen, Na Lv, Fenfang Zheng, Guofang Deng, Yuefeng Ma, Jie Xu and 2 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ruiwen LuOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yiming ZhangOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Likang LuoDepartment of Artificial Intelligence and Information, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Quan YuanThe School of Management, Zhejiang University, Hangzhou, Zhejiang, China.
Shuihong ChenDepartment of Quality Management Office, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Na LvDepartment of Quality Management Office, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Fenfang ZhengOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Guofang DengOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yuefeng MaOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jie XuDepartment of Artificial Intelligence and Information, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Zexin ChenOffice of Scientific Research, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Fengjiang ZhangOffice of Party and Government Affairs, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID 0009-0002-4913-5865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing hospital administrative efficiency is crucial with regard to efforts to manage competitive pressures in healthcare. Despite extensive research on healthcare optimization, the role of Office Automation Systems (OASs) in administrative efficiency in the hospital context remains underexplored. This study addresses this gap by employing the plan-do-check-act (PDCA) cycle to improve administrative processes at the Second Affiliated Hospital, Zhejiang University School of Medicine.

methodsAs part of this study, the root causes of inefficiencies in administrative processes were analyzed, and a PDCA cycle approach was implemented to address these inefficiencies. Monthly training sessions were conducted, and rankings were displayed to raise awareness of this topic among employees. A total of 41 high-frequency processes were monitored and divided into the categories of medical research, clinical use, and daily administrative work. Data were collected from 2021 to 2023, and statistical analyses were conducted via SPSS and R software.

resultsSignificant improvements in process implementation times were observed. With regard to clinical use processes, the median number of days required to process hospitalization and discharge procedures decreased from 4.33 in 2021 to 0.04 in 2023 (P < 0.001). Similarly, the number of days spent processing software requests decreased from 75.98 in 2021 to 31.33 in 2023 (P < 0.001). With respect to medical research processes, the median number of days required for animal laboratory access training applications decreased from 10.87 to 1.24 days (P < 0.01). Daily administrative processes also significantly improved, such that the median number of days spent on processing fixed asset disposal applications decreased from 5.96 to 4.21 (P < 0.01).

conclusionThe application of the PDCA cycle significantly improved the efficiency of hospital administrative processes, thus highlighting the potential of this approach to serve as a valuable tool for continuous efforts to improve hospital administration. This study provides a framework for other hospitals seeking to increase their administrative efficiency via digital transformation and continuous quality improvement.

Indexed as

AutomationEfficiency, OrganizationalHospital AdministrationHumans

Identifiers

PMID40299830
PMCPMC12040115

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.