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. 2019 Aug;10(4):743-750.
doi: 10.1055/s-0039-1697595. Epub 2019 Oct 2.

Visual Evidence: Increasing Usability of Systematic Reviews in Health Systems Guidelines Development

Connor J Smith  1 , Rebecca M Jungbauer  1 , Annette M Totten  1
Affiliations

Visual Evidence: Increasing Usability of Systematic Reviews in Health Systems Guidelines Development

Connor J Smith et al. Appl Clin Inform. 2019 Aug.

Abstract

Background: Integration of evidence from systematic reviews is an essential step in the development of clinical guidelines. The current practice for reporting uses a static structure that does not allow for dynamic investigation. A need exists for an alternate reporting modality to facilitate dynamic visualization of results to match different end-users' queries.

Objectives: We developed a dynamic visualization of data from a systematic review using the commercial product Tableau and assessed its potential to permit customized inquiries.

Methods: Data were selected and extracted from a previously completed systematic review. The resulting dataset was then used to develop an interactive, web-based report designed for use by a guidelines development committee.

Results: A novel example of combining existing reporting standards for systematic review data and modern reporting tools was developed to investigate potential benefits of a dynamic report. Demonstrations of the report to clinicians sitting on previous and future guideline committees received positive feedback for its potential benefit in guidelines development. The report received a runner-up award during the design challenge at the 2018 Workshop on Visual Analytics in Health Care.

Conclusion: The use of interactive, accessible data may increase the use of systematic reviews and aid decision makers in developing evidence-based practice changes.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Traditional forest plot in a systematic review (reprinted from Skelly et al, 2018). AC, attention control; CI, confidence interval; MI, minimal intervention; N, number; NE, no exercise; ODI, Oswestry Disability Index; PDI, Pain Disability Index; RDQ, Roland-Morris Disability Questionnaire; SD, standard deviation; SMD, standardized mean difference; UC, usual care; WL, waitlist.
Fig. 2
Fig. 2
Presentation of results in Tableau, simulating a traditional forest plot.
Fig. 3
Fig. 3
Data from the original PDF were extracted and stored in Excel (2016) using a relational structure.
Fig. 4
Fig. 4
Study - level visualization of data extracted from the original report.
Fig. 5
Fig. 5
Summary-level visualization of aggregated data.
Fig. 6
Fig. 6
Tableau dashboard, including the combined Summary and Studies level visualizations; global filters for data are available at the top of the page. Selecting specific output in the Summary level data will filter the visible Studies level data and show only data that were used to calculate the selected Summary output.
Fig. 7
Fig. 7
Example of a Summary level tooltip. Conditionally formatted fields were created using “Calculated Fields” in Tableau.
Fig. 8
Fig. 8
Example of a Study level tooltip. Conditionally formatted fields were created using “Calculated Fields” in Tableau. A hyperlink to the PubMed entry was created using the “Actions” functionality, and appears after the user clicks the data bar in the visualization.
Fig. 9
Fig. 9
A guided “tour” of the dashboard was created using the Tableau Story functionality. This allows the user to be guided through the potential use of the visualization.
See this image and copyright information in PMC

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References

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Funding This project was funded under contract no. HHSA 290–2015–00009-I Task Order 1 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services (HHS). The authors of this abstract are responsible for its content. Statements in the abstract do not necessarily represent the official views of or imply endorsement by AHRQ or HHS. This manuscript is based on the authors' oral presentation at the 9th Annual Workshop on Visual Analytics in Healthcare design challenge in San Francisco on November 3, 2018. The work is derived from a prior project funded by AHRQ as noted above, which was published February 28, 2019 on the AHRQ EHCP web site, available at: https://effectivehealthcare.ahrq.gov/sites/default/files/pdf/methods-health-systems-guidelines-development.pdf .