Evidence map›Paper›PMID 25665706›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2015

Novel user interface design for medication reconciliation: an evaluation of Twinlist.

Catherine Plaisant, Johnny Wu, A Zach Hettinger, Seth Powsner, Ben Shneiderman

Abstract readEvaluation Study
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Electronic tools to support medication reconciliation: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017
    Pooled it
  3. Engaging hospital patients in the medication reconciliation process using tablet computers.Journal of the American Medical Informatics Association : JAMIA · 2018
    Trial
  4. Trial
  5. Article
  6. Article
  7. Article
  8. Article
  9. Easy Medication Reconciliation at Hospital Admission: The EzMedRec Decision Support System.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020
    Article
  10. Article
  11. Article
  12. Observational
  13. In Search of Vital Signs: A Comparative Study of EHR Documentation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018
    Article
  14. Observational
  15. Implementation of a Medication Reconciliation Assistive Technology: A Qualitative Analysis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017
    Article
  16. Cognitive Errors in Reconciling Complex Medication Lists.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016
    Article
  17. 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.

Catherine PlaisantHuman-Computer Interaction Lab, University of Maryland, College Park, MD, USA plaisant@cs.umd.edu.
Johnny WuHuman-Computer Interaction Lab and Department of Computer Science, University of Maryland, College Park, MD, USA.
A Zach HettingerNational Center for Human Factors in Healthcare, Medstar Institute for Innovation, Washington, DC, USA.
Seth PowsnerDepartment of Psychiatry & Emergency Medicine, Yale University, and Yale Center for Medical Informatics, New Haven, CT, USA.
Ben ShneidermanHuman-Computer Interaction Lab and Department of Computer Science, University of Maryland, College Park, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe primary objective was to evaluate time, number of interface actions, and accuracy on medication reconciliation tasks using a novel user interface (Twinlist, which lays out the medications in five columns based on similarity and uses animation to introduce the grouping - www.cs.umd.edu/hcil/sharp/twinlist) compared to a Control interface (where medications are presented side by side in two columns). A secondary objective was to assess participant agreement with statements regarding clarity and utility and to elicit comparisons. MATERIAL AND

methodsA 1 × 2 within-subjects experimental design was used with interface (Twinlist or Control) as an independent variable; time, number of clicks, scrolls, and errors were used as dependent variables. Participants were practicing medical providers with experience performing medication reconciliation but no experience with Twinlist. They reconciled two cases in each interface (in a counterbalanced order), then provided feedback on the design of the interface.

resultsTwenty medical providers participated in the study for a total of 80 trials. The trials using Twinlist were statistically significantly faster (18%), with fewer clicks (40%) and scrolls (60%). Serious errors were noted 12 and 31 times in Twinlist and Control trials, respectively. DISCUSSION: Trials using Twinlist were faster and more accurate. Subjectively, participants rated Twinlist more favorably than Control. They valued the novel layout of the drugs, but indicated that the included animation would be valuable for novices, but not necessarily for advanced users. Additional feedback from participants provides guidance for further development and clinical implementations.

conclusionsCognitive support of medication reconciliation through interface design can significantly improve performance and safety.

Indexed as

Audiovisual AidsDrug Therapy, Computer-AssistedEfficiencyUser-Computer InterfaceAttitude to ComputersDatabases as TopicData DisplayHumansMedication ReconciliationSurveys and Questionnaireshuman factorsmedication reconciliationuser-computer interface

Identifiers

PMID25665706
PMCPMC8485926

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.