Evidence map›Paper›PMID 29403575›Full record

ArticleOnline journal of public health informatics2017

Meaningful Use and Electronic Laboratory Reporting: Challenges Health Information Technology Vendors Face in Kentucky.

Michael Abisa, MHI, MBI

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Design and Implementation of Data Exchange Formats for Molecular Detection of Drug-Resistant Tuberculosis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    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

3 authors.

Michael AbisaUniversity of Kentucky, Lexington, Kentucky, USA.
MHI
MBI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo explore the challenges Health Information Technology (HIT) vendors face to satisfy the requirements for Meaningful Use (MU) and Electronic Laboratory Reporting (ELR) of reportable diseases to the public health departments in Kentucky. METHODOLOGY: A survey was conducted of Health Information Exchange (HIE) vendors in Kentucky through the Kentucky Health Information Exchange (KHIE). The survey was cross-sectional. Data were collected between February and March 2014. Participants were recruited from KHIE vendors. Participants received online survey link and by email and asked to submit their responses. Vendors' feedback were summarized and analyzed to identify their challenges. Out of the 55 vendors who received the survey, 35(63.64%) responded.

resultsOf the seven transport protocol options for ELR, vendors selected virtual private network (VPN) as the most difficult to implement (31.7%). Secure File Transfer Protocol (SFTP) was selected as preferred ELR transport protocol (31.4%). Most of the respondents, 80% responded that they do not have any challenge with the Health Level 7 (HL7) standard implementation guide required by MU for 2014 ELR certification.

conclusionThe study found that the most difficult transport protocol to implement for ELR is VPN and if vendors have preference, they would use SFTP for ELR over KHIE choice of VPN and Simple Object Access Protocol (SOAP). KHIE vendors do not see any variability in what is reportable by different jurisdiction and also it is not difficult for them to detect what is reportable from one jurisdiction verse the other.

Indexed as

Electronic Laboratory ReportingHealth Information ExchangeHealth Information TechnologyHealth Level 7Meaningful UseTransport Protocol

Identifiers

PMID29403575
PMCPMC5790429

What OpenQuestion holds

Textmetadata
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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.