Literature DB >> 28460091

The impact of home care nurses' numeracy and graph literacy on comprehension of visual display information: implications for dashboard design.

Dawn Dowding1,2, Jacqueline A Merrill1, Nicole Onorato2, Yolanda Barrón2, Robert J Rosati3, David Russell2.   

Abstract

Objective: To explore home care nurses' numeracy and graph literacy and their relationship to comprehension of visualized data. Materials and
Methods: A multifactorial experimental design using online survey software. Nurses were recruited from 2 Medicare-certified home health agencies. Numeracy and graph literacy were measured using validated scales. Nurses were randomized to 1 of 4 experimental conditions. Each condition displayed data for 1 of 4 quality indicators, in 1 of 4 different visualized formats (bar graph, line graph, spider graph, table). A mixed linear model measured the impact of numeracy, graph literacy, and display format on data understanding.
Results: In all, 195 nurses took part in the study. They were slightly more numerate and graph literate than the general population. Overall, nurses understood information presented in bar graphs most easily (88% correct), followed by tables (81% correct), line graphs (77% correct), and spider graphs (41% correct). Individuals with low numeracy and low graph literacy had poorer comprehension of information displayed across all formats. High graph literacy appeared to enhance comprehension of data regardless of numeracy capabilities. Discussion and
Conclusion: Clinical dashboards are increasingly used to provide information to clinicians in visualized format, under the assumption that visual display reduces cognitive workload. Results of this study suggest that nurses' comprehension of visualized information is influenced by their numeracy, graph literacy, and the display format of the data. Individual differences in numeracy and graph literacy skills need to be taken into account when designing dashboard technology.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Keywords:  clinical; clinical dashboard; decision support systems; graph literacy; health literacy; numeracy; nursing informatics

Mesh:

Year:  2018        PMID: 28460091     DOI: 10.1093/jamia/ocx042

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  8 in total

1.  Using Feedback Intervention Theory to Guide Clinical Dashboard Design.

Authors:  Dawn Dowding; Jacqueline Merrill; David Russell
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

2.  Information visualizations of symptom information for patients and providers: a systematic review.

Authors:  Maichou Lor; Theresa A Koleck; Suzanne Bakken
Journal:  J Am Med Inform Assoc       Date:  2019-02-01       Impact factor: 4.497

3.  Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change.

Authors:  Yasmina Okan; Eva Janssen; Mirta Galesic; Erika A Waters
Journal:  Med Decis Making       Date:  2019-03-08       Impact factor: 2.583

4.  Best practices for data visualization: creating and evaluating a report for an evidence-based fall prevention program.

Authors:  Srijesa Khasnabish; Zoe Burns; Madeline Couch; Mary Mullin; Randall Newmark; Patricia C Dykes
Journal:  J Am Med Inform Assoc       Date:  2020-02-01       Impact factor: 4.497

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

Authors:  Veena Panicker; Dahee Lee; Marisa Wetmore; James Rampton; Roger Smith; Michelle Moniz; Zach Landis-Lewis
Journal:  Stud Health Technol Inform       Date:  2019-08-21

6.  What was visualized? A method for describing content of performance summary displays in feedback interventions.

Authors:  Dahee Lee; Veena Panicker; Colin Gross; Jessica Zhang; Zach Landis-Lewis
Journal:  BMC Med Res Methodol       Date:  2020-04-23       Impact factor: 4.615

7.  A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial.

Authors:  Zach Landis-Lewis; Allen Flynn; Allison Janda; Nirav Shah
Journal:  JMIR Res Protoc       Date:  2022-05-10

8.  Remote symptom monitoring integrated into electronic health records: A systematic review.

Authors:  Julie Gandrup; Syed Mustafa Ali; John McBeth; Sabine N van der Veer; William G Dixon
Journal:  J Am Med Inform Assoc       Date:  2020-11-01       Impact factor: 4.497

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.