Evidence map›Paper›PMID 42851816›Full record

ReviewFrontiers in digital health2026

Current status, challenges and future development of self-service appointment platforms in hospital radiology departments in China: a narrative review.

Peng-Wei Shu, Ke-Ke Lu, Shi-Hao Xie, Zi-Qiao Lei, Qing Fu

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Peng-Wei Shu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ke-Ke Lu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shi-Hao Xie *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zi-Qiao Lei *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qing Fu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, China has accelerated the implementation of precision medicine strategy, integrating accuracy, individualized care and evidence-based decision-making into clinical diagnosis and treatment. Meanwhile, the expansion of smart-hospital and the requirement of Level-5 electronic medical record (EMR) certification have made digital upgrading an indispensable component of radiological services. As a result, self-service appointment is increasingly replacing conventional manual registration and has become an important part of the digital infrastructure of modern radiology departments, with an increasing proportion of administrative pressure, and enhance overall operational performance. Nevertheless, their effectiveness remains restricted by several deficiencies, including limited differentiation among examination workflows, insufficient risk assessment before imaging procedures, inadequate accessibility for older adults, and delayed information exchange between heterogeneous systems; therefore, future improvement should focus on three major directions: (a) AI-supported scheduling tailored to individual patient needs, (b) closed-loop management covering the entire examination process, and (c) coordinated appointment mechanisms involving multiple clinical disciplines, thereby advancing radiology services toward a more refined, intelligent, and patient-centered model.

Indexed as

artificial intelligencedigital healthhospital information systemno-showonline schedulingradiologyself-service appointment

Identifiers

PMID42851816
PMCPMC13646530

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.