Evidence map›Paper›PMID 25207616›Full record

ArticleInformatics in primary care2014

Can we predict a national profile of non-attendance paediatric urology patients: a multi-institutional electronic health record study.

Ruth A Bush, Vijaya M Vemulakonda, Sean T Corbett, George J Chiang

Abstract read
In one paragraph

Article in Informatics in primary care, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Predicting no-shows in paediatric orthopaedic clinics.BMJ health & care informatics · 2020
    Article
  9. Review
  10. Review
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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

4 authors.

Ruth A BushUniversity of San Diego, San Diego, 92110, USA; Clinical Research Informatics, Rady Children's Hospital-San Diego, 7910 Frost Street, Suite #325, San Diego, CA 92123, USA.
Vijaya M VemulakondaDepartment of Pediatric Urology, Children's Hospital Colorado, 13123 East 16th Avenue, Box 463, Aurora, CO 80045, USA.
Sean T CorbettDivision of Pediatric Urology, University of Virginia, PO Box 800422, Charlottesville, VA 22908, USA.
George J ChiangUniversity of California, San Diego, La Jolla, CA 92093, USA; Clinical Research Informatics, Rady Children's Hospital-San Diego, 7910 Frost Street Suite #325, San Diego, CA 92123, USA.

Funding

Adolescent Disease Autonomy using Patient Portal Technology (ADAPPT)R00HS022404 · AHRQ · UNIVERSITY OF SAN DIEGO · PI BUSH, RUTH A · 2015 to 2017
$747k
Adapting Electronic Medical Record to Measure Medical Outcomes in ASD PopulationsK99HS022404 · AHRQ · RADY CHILDREN'S HOSPITAL - SAN DIEGO · PI BUSH, RUTH A · 2013 to 2014
$282k
AHRQ HHS K99 HS022404AHRQ HHS K99 HS022404-01AHRQ HHS R00 HS022404
6 · The paper itself

Abstract

backgroundNon-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources.

objectiveTo determine the utility of using routinely collected electronic health record (EHR) data for multi-centre analysis of variables predictive of patient noshows (NS) to identify areas for future intervention.

methodsData were obtained from Children's Hospital Colorado, Rady Children's Hospital San Diego and University of Virginia Hospital paediatric urology practices, which use the Epic® EHR system. Data were extracted for all urology outpatient appointments scheduled from 1 October 2010 to 30 September 2011 using automated electronic data extraction techniques. Data included appointment type; date; provider type and days from scheduling to appointment. All data were de-identified prior to analysis. Predictor variables identified using χ(2) and analysis of variance were modelled using multivariate logistic regression.

resultsA total of 2994 NS patients were identified within a population of 28,715, with a mean NS rate of 10.4%. Multivariate logistic regression determined that an appointment with mid-level provider (odds ratio (OR) 1.70 95% CI (1.56, 1.85)) and an increased number of days between scheduling and appointment (15-28 days OR 1.24 (1.09, 1.41); 29+ days OR 1.70 (1.53, 1.89)) were significantly associated with NS appointments.

conclusionWe demonstrated sufficient interoperability among institutions to obtain data rapidly and efficiently for use in 1) interventions; 2) further study and 3) more complex analysis. Demographic and potentially modifiable clinic characteristics were associated with NS to the outpatient clinic. The analysis also demonstrated that available data are dependent on the clinical data collection systems and practices.

Indexed as

Appointments and SchedulesChildElectronic Health RecordsFemaleHealth Information ExchangeHumansInformation Storage and RetrievalMalePatient ComplianceRetrospective StudiesUrology

Identifiers

PMID25207616
PMCPMC5137580

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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.