Evidence map›Paper›PMID 30603690›Full record

ArticleHeliyon2018

Health referral system user acceptance model in Indonesia.

Putu Wuri Handayani, Ibad Rahadian Saladdin, Ave Adriana Pinem, Fatimah Azzahro, Achmad Nizar Hidayanto, Dumilah Ayuningtyas

Abstract read
In one paragraph

Article in Heliyon, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

6 authors.

Putu Wuri HandayaniFaculty of Computer Science, Universitas Indonesia, Indonesia.
Ibad Rahadian SaladdinFaculty of Computer Science, Universitas Indonesia, Indonesia.
Ave Adriana PinemFaculty of Computer Science, Universitas Indonesia, Indonesia.
Fatimah AzzahroFaculty of Computer Science, Universitas Indonesia, Indonesia.
Achmad Nizar HidayantoFaculty of Computer Science, Universitas Indonesia, Indonesia.
Dumilah AyuningtyasFaculty of Public Health, Universitas Indonesia, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aims to identify a user acceptance model for the health referral system in Indonesia. The following factors classified into dimensions of organization, technology, process, and individual, were examined: patient centricity, regulation, data security, integration, responsiveness, effectiveness, efficiency, personal beliefs, and social influence.

methodsQuantitative data processing methods were used, including the online distribution of questionnaires to a total of 283 valid respondents who had previously used health referrals. Data processing was performed according to the ordinal logistic regression method using IBM SPSS Statistics 24.0 software.

resultsThe user acceptance model fit with a significance of 0.084, while only regulation, data security, integration, responsiveness, effectiveness, efficiency, personal beliefs, and social influence significantly influenced the patients' acceptance of health referrals.

conclusionsThis study may build awareness in the community regarding the health referral system along with the ideal factors that encourage patients to utilize health referrals. In addition, the provision of health services by health facilities and regulators may take these factors into account so they may provide fair and equitable services for all the people of Indonesia; for example, providers and regulators can improve the utilization of information technology and guidebooks on the health referral system to facilitate communication and standardization among health facilities.

Indexed as

Public health

Identifiers

PMID30603690
PMCPMC6304450

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.