Evidence map›Paper›PMID 31438137›Full record

ArticleStudies in health technology and informatics2019

Designing Tailored Displays for Clinical Practice Feedback: Developing Requirements with User Stories.

Veena Panicker, Dahee Lee, Marisa Wetmore, James Rampton, Roger Smith, Michelle Moniz, Zach Landis-Lewis

Abstract read
In one paragraph

Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

7 authors.

Veena PanickerDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
Dahee LeeDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
Marisa WetmoreDepartment of Obstetrics and Gynecology, Michigan Medicine, Ann Arbor, MI, USA.
James RamptonDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
Roger SmithDepartment of Obstetrics and Gynecology, Michigan Medicine, Ann Arbor, MI, USA.
Michelle MonizDepartment of Obstetrics and Gynecology, Michigan Medicine, Ann Arbor, MI, USA.
Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.

Funding

A knowledge-based message tailoring systemK01LM012528 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANDIS-LEWIS, ZACHARY · 2017 to 2019
$523k
NLM NIH HHS K01 LM012528
6 · The paper itself

Abstract

Improving visualizations in clinical quality reports and dashboards may improve the visualization influence on clinical practice. Tailored displays could accommodate individual and situational differences, but these diplays introduce complex requirements across healthcare professionals and teams. We applied user stories, a method for managing complex software requirements, to a user-centered design process for tailored visual displays about postpartum contraception care. We mapped user stories to tailored displays to identify the quantity of displays that were supported by each user story. We developed 9 tailored displays and 11 user stories. Displays varied in their mappings to user stories (mean 5, max 9, min 0), revealing differences in healthcare professionals and teams preferences and information needs. User stories and user-centered design may be useful for healthcare organizations to manage complex requirements of tailored displays in clinical practice feedback.

Indexed as

Delivery of Health CareSoftwareFeedbackFemaleHealth PersonnelHumansFeedbackquality improvementsoftware design

Identifiers

PMID31438137
PMCPMC8011596

What OpenQuestion holds

Textmetadata
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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.