Evidence map›Paper›PMID 27364029›Full record

ArticleSouthern medical journal2016

When Should ED Physicians Use an HIE? Predicting Presence of Patient Data in an HIE.

Christine Marie Carr, Steven Howard Saef, Jingwen Zhang, Zemin Su, Cathy L Melvin, Jihad S Obeid, Wenle Zhao, J Christophe Arnaud, Justin Marsden, Adam B Sendor and 3 more

Abstract read
In one paragraph

Article in Southern medical journal, 2016. 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. 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

13 authors.

Christine Marie CarrFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Steven Howard SaefFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Jingwen ZhangFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Zemin SuFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Cathy L MelvinFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Jihad S ObeidFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Wenle ZhaoFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
J Christophe ArnaudFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Justin MarsdenFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Adam B SendorFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Leslie LenertFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
William P MoranFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.
Patrick D MauldinFrom the Departments of Medicine, Public Health Sciences, the Division of General Internal Medicine and Geriatrics, the South Carolina Clinical and Translational Research Institute, and the Center for Biomedical Informatics, Medical University of South Carolina, Charleston.

Funding

South Carolina Clinical & Translational Research Institute (SCTR)UL1TR001450 · NCATS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI BRADY, KATHLEEN T., FLUME, PATRICK A · 2015 to 2024
$41.1M
South Carolina Clinical & Translational Research Institute (SCTR)UL1TR000062 · NCATS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI BRADY, KATHLEEN T. · 2012 to 2014
$9.4M
NCATS NIH HHS UL1 TR000062NCATS NIH HHS UL1 TR001450
6 · The paper itself

Abstract

objectivesHealth information exchanges (HIEs) make possible the construction of databases to characterize patients as multisystem users (MSUs), those visiting emergency departments (EDs) of more than one hospital system within a region during a 1-year period. HIE data can inform an algorithm highlighting patients for whom information is more likely to be present in the HIE, leading to a higher yield HIE experience for ED clinicians and incentivizing their adoption of HIE. Our objective was to describe patient characteristics that determine which ED patients are likely to be MSUs and therefore have information in an HIE, thereby improving the efficacy of HIE use and increasing ED clinician perception of HIE benefit.

methodsData were extracted from a regional HIE involving four hospital systems (11 EDs) in the Charleston, South Carolina area. We used univariate and multivariable regression analyses to develop a predictive model for MSU status.

resultsFactors associated with MSUs included younger age groups, dual-payer insurance status, living in counties that are more rural, and one of at least six specific diagnoses: mental disorders; symptoms, signs, and ill-defined conditions; complications of pregnancy, childbirth, and puerperium; diseases of the musculoskeletal system; injury and poisoning; and diseases of the blood and blood-forming organs. For patients with multiple ED visits during 1 year, 43.8% of MSUs had ≥4 visits, compared with 18.0% of non-MSUs (P < 0.0001).

conclusionsThis predictive model accurately identified patients cared for at multiple hospital systems and can be used to increase the likelihood that time spent logging on to the HIE will be a value-added effort for emergency physicians.

Indexed as

Emergency Service, HospitalHealth Information ExchangeAdultCost SavingsElectronic Health RecordsFemaleHumansMaleMedical OveruseMedical Record LinkageMiddle AgedQuality ImprovementSouth Carolina

Identifiers

PMID27364029
PMCPMC4933325

What OpenQuestion holds

Textmetadata
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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.