Evidence map›Paper›PMID 24892063›Full record

ArticleTheScientificWorldJournal2014

Patterns of nonemergent visits to different healthcare facilities on the same day: a nationwide analysis in Taiwan.

Meng-Hsuan Wu, Meng-Ju Wu, Li-Fang Chou, Tzeng-Ji Chen

Abstract read
In one paragraph

Article in TheScientificWorldJournal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Characteristics of doctor-shoppers: a systematic literature review.Journal of market access & health policy · 2019
    Article
  2. 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

4 authors.

Meng-Hsuan WuInstitute of Hospital and Health Care Administration, School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.
Meng-Ju WuFaculty of Medicine, Semmelweis University, Budapest 1094, Hungary.
Li-Fang ChouDepartment of Public Finance, National Chengchi University, Taipei 116, Taiwan.
Tzeng-Ji ChenInstitute of Hospital and Health Care Administration, School of Medicine, National Yang-Ming University, Taipei 112, Taiwan ; Department of Family Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan.ORCID 0000-0002-8350-0232

Funding

HSRD VA HIR 10-001
6 · The paper itself

Abstract

Doctor shopping is a common phenomenon in many countries. However, patterns of switching healthcare facilities on the same day were little known. The data were obtained from the longitudinal cohort datasets (LHID2010) of Taiwan's National Health Insurance Research Database in 2010. Of 1,000,000 persons of the cohort with 13,276,928 nonemergent visits, 185,347 patients had visited different healthcare facilities within one day, with a total of 672,478 visits and 337,260 switches between facilities in 329,073 patient-days. While 63.0% (n = 212,590) of all switches occurred between facilities of the same accreditation level, 14.1% (n = 47,664) moved from lower to higher level, and 22.8% (n = 77,006) moved in the opposite direction. In 33,689 switches, patients moved to the same specialty of another facility. In 48,324 switches, patients moved to another facility with the same diagnosis, and the most frequent diagnoses were diseases of the digestive system (11,148) and diseases of the respiratory system (10,393). In a densely populated country without strict referral regulation, a high percentage of Taiwanese people had the experience of visiting different healthcare facilities on the same day. The system of family physicians as personal doctors and gatekeepers to healthcare might ameliorate the harmful impact.

Indexed as

Cohort StudiesFemaleHealth FacilitiesHumansMaleTaiwanUtilization Review

Identifiers

PMID24892063
PMCPMC4032646

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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.