Evidence map›Paper›PMID 28339698›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2017

Learning from errors: analysis of medication order voiding in CPOE systems.

Thomas G Kannampallil, Joanna Abraham, Anna Solotskaya, Sneha G Philip, Bruce L Lambert, Gordon D Schiff, Adam Wright, William L Galanter

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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Conceptual considerations for using EHR-based activity logs to measure clinician burnout and its effects.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  4. Risk factors associated with medication ordering errors.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  5. Article
  6. Observational
  7. Review
  8. Genomics and electronic health record systems.Human molecular genetics · 2018
    Review
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

8 authors.

Thomas G KannampallilDepartment of Family Medicine, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
Joanna AbrahamDepartment of Biomedical and Health Information Sciences, College of Applied Health Sciences, Northwestern University, Chicago, IL, USA.
Anna SolotskayaDepartment of Medicine, College of Medicine, University of Illinois at Chicago.
Sneha G PhilipDepartment of Medicine, College of Medicine, University of Illinois at Chicago.
Bruce L LambertDepartment of Communication Studies, Center for Communication and Health, Northwestern University.
Gordon D SchiffDivision of General Internal Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Adam WrightDivision of General Internal Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
William L GalanterDepartment of Medicine, College of Medicine, University of Illinois at Chicago.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMedication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors. MATERIALS AND

methodsWe examined medication orders from an academic medical center for a 6-year period (2006-2011; n  = 5 804 150). We categorized orders based on status (void, not void) and clinician-provided reasons for voiding. We used multivariable logistic regression to investigate the association between order voiding and clinician, patient, and order characteristics. We conducted chart reviews on a random sample of voided orders ( n  = 198) to investigate the rate of medication ordering errors among voided orders, and the accuracy of clinician-provided reasons for voiding.

resultsWe found that 0.49% of all orders were voided. Order voiding was associated with clinician type (physician, pharmacist, nurse, student, other) and order type (inpatient, prescription, home medications by history). An estimated 70 ± 10% of voided orders were due to medication ordering errors. Clinician-provided reasons for voiding were reasonably predictive of the actual cause of error for duplicate orders (72%), but not for other reasons. DISCUSSION AND

conclusionMedication safety initiatives require availability of error data to create repositories for learning and training. The voiding function is available in several electronic health record systems, so order voiding could provide a low-effort mechanism for self-reporting of medication ordering errors. Additional clinician training could help increase the quality of such reporting.

Indexed as

Medical Order Entry SystemsMedication Systems, HospitalAcademic Medical CentersFemaleHumansLogistic ModelsMaleMedication ErrorsPatient SafetyRetrospective StudiesCPOEmedication errorsmedication order voidingpatient safety

Identifiers

PMID28339698
PMCPMC7651956

What OpenQuestion holds

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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.