Evidence map›Paper›PMID 41586576›Full record

ArticleJournal of public health dentistry2026

Improving the Oral Healthcare Experience for Hong Kong and Macao Residents: A Practical Exploration of Specialized Nurse-Led Oral Care Clinics.

Han Zhao, Huanhuan Wang, Lisha Huang, Fan Xie, Junhui Yan

Abstract read
In one paragraph

Article in Journal of public health dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Han ZhaoDepartment of Stomatology, Shenzhen Luohu People's Hospital, Shenzhen, China.
Huanhuan WangDepartment of Stomatology, Shenzhen Luohu People's Hospital, Shenzhen, China.
Lisha HuangNursing Department, Shenzhen Luohu People's Hospital, Shenzhen, China.
Fan XieDepartment of Stomatology, Shenzhen Luohu People's Hospital, Shenzhen, China.
Junhui YanNursing Department, Shenzhen Luohu People's Hospital, Shenzhen, China.ORCID https://orcid.org/0009-0004-5939-1138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to evaluate the effectiveness of specialized oral nursing clinics for residents of Hong Kong and Macau. By optimizing the outpatient process to enhance treatment efficiency and improve the healthcare experience of Hong Kong and Macau patients in mainland China, this research provides evidence-based insights to address the challenges of low cross-regional treatment efficiency and complex procedures.

methodsA specialized oral nursing clinic was established by a hospital group, implementing an innovative "one center, multiple branches" referral model. This model integrates the Oral Medicine Center with community health clinics to optimize the allocation of healthcare resources. The outpatient nurses are all specialized oral nurses with prescriptive authority. A cluster sampling method was used to compare data from January to March 2023 (before implementation) and January to March 2024 (after implementation). The evaluation indicators included patient volume, waiting time for consultations, expected treatment completion rate, and patient satisfaction.

resultsAfter implementation, the proportion of patients from Hong Kong and Macau increased from 2.0% to 6.8% (p < 0.01). The average waiting time for consultations decreased from 40.16 ± 17.39 min to 33.19 ± 15.61 min (p < 0.01). The expected treatment completion rate increased from 83.6% to 90.1% (p < 0.01), and patient satisfaction improved from 90.9% to 96.9% (p < 0.01).

conclusionThe specialized oral nursing clinic, combined with the "one center, multiple branches" referral model, effectively simplifies the consultation process, improves service efficiency, and enhances patient satisfaction. This approach is innovative, scientifically sound, and replicable, providing practical evidence for the joint development and sharing of medical resources in the Greater Bay Area.

trial registrationNot applicable.

Indexed as

Dental ClinicsNursingOral HealthHong KongHumansMacauPatient SatisfactionReferral and ConsultationTime FactorsWaiting Listscross‐regional oral medical servicesdental outpatient servicesHong Kong and Macao residentsnursing human resource management and specialty nurse trainingspecialist nurse nursing clinic

Identifiers

PMID41586576
PMCPMC12972214

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.