Evidence map›Paper›PMID 31409330›Full record

ArticleBMC health services research2019

Improving outpatient satisfaction by extending expected waiting time.

Wei-Min Ma, Hui Zhang, Neng-Li Wang

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Wei-Min MaSchool of Economics and Management, Tongji University, Shanghai, 200092, China.
Hui ZhangSchool of Economics and Management, Tongji University, Shanghai, 200092, China. graduation2018@sina.com.
Neng-Li WangThe Center for Pediatric Liver Diseases, Children's Hospital of Fudan University, Shanghai, 201102, China.
Tongji University · CNChildren's Hospital of Fudan University · CN

Funding

National Natural Science Foundation of China 71371141
6 · The paper itself

Abstract

backgroundLong waiting times result in low satisfaction. Although several methods are used to shorten the actual waiting time (AWT) in large hospitals of China, the outpatients still have a long actual waiting time. This study aimed to explore whether satisfaction could be improved by extending the expected waiting time (EWT) instead of shortening the AWT.

methodsIn October 2016, 257 students in grade one voluntarily participated in this study. They came from 6 classes, which were randomly divided into two groups: 3 classes comprised the control group (n = 125) and 3 classes comprised the experimental group (n = 132). Unfavorable information (UI) was given to the experimental group alone. Six distinct questionnaires were designed to explore the effects of UI on EWT and the effects of an extended EWT on satisfaction. Satisfaction scores ranged from 0 to 100: 0-25, very dissatisfied; 26-50, dissatisfied; 51-75, satisfied; 76-100, very satisfied. Each participant finished one of the 6 questionnaires online. Of the 257 questionnaires, 233 were valid.

resultsBefore UI was given, the initial EWT (T

conclusionsEWT was found to be extended greatly by UI. An extended EWT could improve satisfaction scores.

Indexed as

Health Care SurveysWaiting ListsAdolescentFemaleHumansMaleOutpatientsPatient SatisfactionTime FactorsYoung AdultActual waiting timeExpected waiting timeProspect theorySatisfaction scoreUnfavorable information

Identifiers

PMID31409330
PMCPMC6693195
OpenAlexW2968696986

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.