Evidence map›Paper›PMID 40882153›Full record

ArticleJMIR medical informatics2025

Patient-Centered Outpatient Process Optimization System Based on Intelligent Guidance in a Large Tertiary Hospital in China: Implementation Report.

Xiaoyi Wang, Rujia Zhang, Zhihong Gao, Mengxuan Xia, Songjia Zhang, Lizheng Ge, Yuyang Zhu, Haojie Jin, Shenglian Pan, Manman Zheng and 2 more

Abstract read
In one paragraph

Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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  3. Observational
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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.

Xiaoyi Wang *School of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0000-0003-2833-7695
Rujia Zhang *School of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0000-0002-6857-9202
Zhihong GaoInformation Technology Section, The First Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0009-0005-6168-0091
Mengxuan XiaInformation Technology Section, The First Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-8270-4444
Songjia ZhangSchool of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0009-0009-0488-0440
Lizheng GeSchool of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0009-0003-5145-9022
Yuyang ZhuCixi Biomedical Research Institute, Wenzhou Medical University, Wenzhou, China.ORCID 0009-0005-5271-3146
Haojie JinSchool of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0009-0005-8645-9203
Shenglian PanAcupuncture and Tuina Department, Wenzhou Hospital of Traditional Chinese Medicine, Wenzhou, China.ORCID 0009-0005-9236-9837
Manman Zheng *Graduate School of Wenzhou Medical University, Wenzhou, China.ORCID 0009-0004-7435-022X
Chun Chen *School of Medical Humanities and Management, Wenzhou Medical University Tea Mountain Campus, Tongren Building 7B304, Ou Hai District, Wenzhou, 325035, China, 86 577 8669 9590.ORCID 0000-0002-5149-988X
Xiangyang Zhang *Medical Affairs Department, First Affiliated Hospital of Wenzhou Medical University, Shangcai Village, Nanbaixiang Street, Ouhai District, Wenzhou, Zhejiang, 325000, China, 0577-55578282.ORCID 0000-0003-0528-5474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In large tertiary hospitals across China, outpatient patients often encounter the "three longs and one short" (long registration time, long waiting time, long time for medicine collection, and short time for medical treatment) phenomenon. This scenario contributes to suboptimal patient experiences and declining satisfaction with health care services. To address the issue of long waiting times, many hospitals in China have implemented a range of measures. However, these measures have only improved individual aspects of the patient experience, with limited overall impact. Currently, there is a lack of comprehensive, intelligent reform for the entire patient service process in the medical system. Therefore, there is an urgent need to integrate and optimize the entire patient service process, providing real-time intelligent guidance within hospitals. This would help reduce waiting times for patients and enhance their satisfaction. Objective: This study aims to introduce a patient-centered intelligent guidance system and report on the impact of its implementation on outpatient waiting times and patient satisfaction in hospitals. Methods: The intelligent guidance system was designed with a patient-centered approach, leveraging internet and big data technologies. The system seamlessly connects various steps of the outpatient medical process, facilitating functions including automated check-in and comprehensive intelligent guidance for patients' medical visits, thus enhancing the efficiency and quality of health care delivery. This system has been implemented in a tertiary hospital in China. To assess the system's effectiveness, we compared outpatient visit data, waiting time data, and patient satisfaction levels between the preimplementation and postimplementation periods from 2019 to 2022. We analyzed the changes in patients' average waiting times and satisfaction levels after the system was implemented. Results: One year after the introduction of the intelligent guidance system, the number of outpatient visits increased from 5,067,958 to 5,456,151. The waiting time for outpatient patients was significantly reduced. The waiting time for consultation decreased by 2.84 minutes (mean 41.14, SD 2.31 min vs mean 38.30, SD 1.89 min; P<.001). The waiting time for examination decreased by 3.35 minutes (mean 47.83, SD 1.10 min vs mean 44.48, SD 1.67 min; P<.001). Consultation time increased to 3.43 minutes (mean 2.85, SD 0.03 min vs mean 3.43, SD 0.26 min; P<.001). After the system was launched, patient satisfaction increased from 89.99% (SD 2.78%) in 2021 to 92.72% (SD 0.18%) in 2022 (P=.005). Conclusions: The patient-centered intelligent guidance system reported in this study proved beneficial for tertiary medical institutions striving to alleviate the outpatient burden caused by prolonged waiting times. Through continuous transformation and upgrading of the outpatient service process centered on patients, the efficiency of outpatient services and patient satisfaction improved. Therefore, the patient-centered principle method and process integration concepts for the system can be further promoted and implemented.

Indexed as

Ambulatory CareOutpatientsPatient-Centered CareTertiary Care CentersChinaFemaleHumansMalePatient SatisfactionWaiting Listsintelligent systemoutpatient servicepatient-centeredpatient satisfactionwaiting time

Identifiers

PMID40882153
PMCPMC12396732

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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.