Evidence map›Paper›PMID 29997138›Full record

ArticleBMJ open2018

Using smartphone-based virtual patients to assess the quality of primary healthcare in rural China: protocol for a prospective multicentre study.

Jing Liao, Yaolong Chen, Yiyuan Cai, Nan Zhan, Sean Sylvia, Kara Hanson, Hong Wang, Judith N Wasserheit, Wenjie Gong, Zhongliang Zhou and 5 more

Abstract read
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Using virtual patient to assess primary health workersZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2021
    Article
  5. Article
  6. 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

15 authors.

Jing LiaoSun Yat-sen Global Health Institute, School of Public Health and Institute of State Governance, Sun Yat-sen University.
Yaolong ChenEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Yiyuan CaiSchool of Public Health, Guizhou Medical University, Guiyang, China.
Nan ZhanDepartment of Health Management, School of Health Management, Inner Mongolia Medical University, Hohhot, China.
Sean SylviaDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Kara HansonDepartment of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.
Hong WangHealth Economics, Financing & Systems, Bill & Melinda Gates Foundation, Seattle, Washington, USA.
Judith N WasserheitDepartments of Global Health, Medicine, and Epidemiology, Schools of Medicine and Public Health, University of Washington, Seattle, Washington, USA.
Wenjie GongXiangya School of Public Health, Central South University, Changsha, China.
Zhongliang ZhouSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Jay PanWest China School of Public Health, Sichuan University, Chengdu, China.
Xiaohui WangSchool of Public Health, Lanzhou University, Lanzhou, China.
Chengxiang TangSchool of Public Administration, Guangzhou University, Guangzhou, China.
Wei ZhouHospital Administration Institute, Xiangya Hospital, Central South University, Changsha, China.
Dong XuSun Yat-sen Global Health Institute, School of Public Health and Institute of State Governance, Sun Yat-sen University.

Funding

China-Rochester Suicide Research Training Program (CRSRT)D43TW009101 · FIC · UNIVERSITY OF ROCHESTER · PI CAINE, ERIC D. · 2011 to 2015
$1.2M
FIC NIH HHS D43 TW009101
6 · The paper itself

Abstract

introductionValid and low-cost quality assessment tools examining care quality are not readily available. The unannounced standardised patient (USP), the gold standard for assessing quality, is costly to implement while the validity of clinical vignettes, as a low-cost alternative, has been challenged. Computerised virtual patients (VPs) create high-fidelity and interactive simulations of doctor-patient encounters which can be easily implemented via smartphone at low marginal cost. Our study aims to develop and validate smartphone-based VP as a quality assessment tool for primary care, compared with USP. METHODS AND ANALYSIS: The study will be implemented in primary health centres (PHCs) in rural areas of seven Chinese provinces, and physicians practicing at township health centres and village clinics will be our study population. The development of VPs involves three steps: (1) identifying 10 VP cases that can best represent rural PHCs' work, (2) designing each case by a case-specific development team and (3) developing corresponding quality scoring criteria. After being externally reviewed for content validity, these VP cases will be implemented on a smartphone-based platform and will be tested for feasibility and face validity. This smartphone-based VP tool will then be validated for its criterion validity against USP and its reliability (ie, internal consistency and stability), with 1260 VP/USP-clinician encounters across the seven study provinces for all 10 VP cases. ETHICS AND DISSEMINATION: Sun Yat-sen University: No. 2017-007. Study findings will be published and tools developed will be freely available to low-income and middle-income countries for research purposes.

Indexed as

Computer SimulationQuality of Health CareSmartphoneChinaFeasibility StudiesHumansMulticenter Studies as TopicPrimary Health CareProspective StudiesResearch DesignRural Health ServicesValidation Studies as Topiccare quality assessment toolprimary carequality in health care

Identifiers

PMID29997138
PMCPMC6089284

What OpenQuestion holds

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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.