Evidence map›Paper›PMID 27638908›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2017

National trends in safety performance of electronic health record systems in children's hospitals.

Juan D Chaparro, David C Classen, Melissa Danforth, David C Stockwell, Christopher A Longhurst

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Implementing Large-Scale Data-Driven Quality Improvement in Assisted Living.Journal of the American Medical Directors Association · 2022
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  12. Review
  13. Impact of a Process Change on Prevalence of Prescribed Unmeasurable Liquid Doses in a Neonatal Intensive Care Unit.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2020
    Article
  14. Article
  15. Structured override reasons for drug-drug interaction alerts in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  16. Article
  17. Article
  18. Engineering technology resilience through informatics safety science.Journal of the American Medical Informatics Association : JAMIA · 2017
    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

5 authors.

Juan D ChaparroDepartments of Biomedical Informatics and Pediatrics, University of California San Diego, and Rady Children's Hospital, San Diego, California, USA.
David C ClassenUniversity of Utah School of Medicine, Salt Lake City, Utah, USA.
Melissa DanforthThe Leapfrog Group, Washington, DC, USA.
David C StockwellChildren's National Health System and George Washington University School of Medicine, Washington, DC, USA.
Christopher A LonghurstDepartments of Biomedical Informatics and Pediatrics, University of California, San Diego, USA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
AHRQ HHS R01 HS023696NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Objective: To evaluate the safety of computerized physician order entry (CPOE) and associated clinical decision support (CDS) systems in electronic health record (EHR) systems at pediatric inpatient facilities in the US using the Leapfrog Group's pediatric CPOE evaluation tool. Methods: The Leapfrog pediatric CPOE evaluation tool, a previously validated tool to assess the ability of a CPOE system to identify orders that could potentially lead to patient harm, was used to evaluate 41 pediatric hospitals over a 2-year period. Evaluation of the last available test for each institution was performed, assessing performance overall as well as by decision support category (eg, drug-drug, dosing limits). Longitudinal analysis of test performance was also carried out to assess the impact of testing and the overall trend of CPOE performance in pediatric hospitals. Results: Pediatric CPOE systems were able to identify 62% of potential medication errors in the test scenarios, but ranged widely from 23-91% in the institutions tested. The highest scoring categories included drug-allergy interactions, dosing limits (both daily and cumulative), and inappropriate routes of administration. We found that hospitals with longer periods since their CPOE implementation did not have better scores upon initial testing, but after initial testing there was a consistent improvement in testing scores of 4 percentage points per year. Conclusions: Pediatric computerized physician order entry (CPOE) systems on average are able to intercept a majority of potential medication errors, but vary widely among implementations. Prospective and repeated testing using the Leapfrog Group's evaluation tool is associated with improved ability to intercept potential medication errors.

Indexed as

Decision Support Systems, ClinicalHospitals, PediatricMedical Order Entry SystemsDrug Therapy, Computer-AssistedHospital Bed CapacityHumansMedication Errorscomputerized physician order entrycomputer simulationelectronic health recordsmedication order entrypatient safetysoftware testing

Identifiers

PMID27638908
PMCPMC7651940

What OpenQuestion holds

Textmetadata
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.