Evidence mapPaperPMID 37704021Full record

ArticleApplied clinical informatics2023

Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs.

Edna Shenvi, Aziz Boxwala, Dean Sittig, Courtney Zott, Edwin Lomotan, James Swiger, Prashila Dullabh

Open access · bronzeAbstract readScoping Review
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 17% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Edna ShenviElimu Informatics, El Cerrito, California, United States.
Aziz BoxwalaElimu Informatics, El Cerrito, California, United States.
Dean SittigMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States.
Courtney ZottNORC at the University of Chicago, Bethesda, Maryland, United States.
Edwin LomotanCenter for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland, United States.
James SwigerCenter for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, Maryland, United States.
Prashila DullabhNORC at the University of Chicago, Bethesda, Maryland, United States.
Agency for Healthcare Research and Quality · USUniversity of Chicago · USThe University of Texas Health Science Center at Houston · US

Funding

Agency for Healthcare Research and Quality Contract no.: 75Q80120D00018 for the Clinical Deci
6 · The paper itself

Abstract

backgroundPatient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear.

objectivesIdentify best practices in visualizations of physiologic PGHD, for designing a software application as a PC CDS tool.

methodsWe performed a scoping review of studies of PGHD dashboards that involved clinician users in design or evaluations. We included only studies that used physiologic PGHD from single patients for usage in decision-making.

resultsWe screened 468 titles and abstracts, 63 full-text papers, and identified 15 articles to include in our review. Some research primarily sought user input on PGHD presentation; other studies garnered feedback only as a side effort for other objectives (e.g., integration with electronic health records [EHRs]). Development efforts were often in the domains of chronic diseases and collected a mix of physiologic parameters (e.g., blood pressure and heart rate) and activity data. Users' preferences were for data to be presented with statistical summaries and clinical interpretations, alongside other non-PGHD data. Recurrent themes indicated that users desire longitudinal data display, aggregation of multiple data types on the same screen, actionability, and customization. Speed, simplicity, and availability of data for other purposes (e.g., documentation) were key to dashboard adoption. Evaluations were favorable for visualizations using common graphing or table formats, although best practices for implementation have not yet been established.

conclusionAlthough the literature identified common themes on data display, measures, and usability, more research is needed as PGHD usage grows. Ensuring that care is tailored to individual needs will be important in future development of clinical decision support.

Indexed as

Electronic Health RecordsText MessagingHumansSoftwareSurveys and Questionnaires

Identifiers

PMID37704021
PMCPMC10665122
OpenAlexW4386697875

What Socratic 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 Socratic graph.