Evidence map›Paper›PMID 35156928›Full record

ArticleJMIR medical informatics2022

State-of-the-art Dashboards on Clinical Indicator Data to Support Reflection on Practice: Scoping Review.

Bernard Bucalon, Tim Shaw, Kerri Brown, Judy Kay

Abstract readScoping Review
In one paragraph

Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

  1. Pooled it
  2. A Systematic Review of Electronic Medical Record Driven Quality Measurement and Feedback Systems.International journal of environmental research and public health · 2022
    Pooled it
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  19. A Taxonomy for Health Information Systems.Journal of medical Internet research · 2024
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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

4 authors.

Bernard Bucalon *Human Centred Technology Cluster, School of Computer Science, The University of Sydney, Darlington, Australia.ORCID https://orcid.org/0000-0001-5528-781X
Tim Shaw *Practice Analytics, Digital Health Cooperative Research Centre, Sydney, Australia.ORCID https://orcid.org/0000-0003-0783-1918
Kerri Brown *Practice Analytics, Digital Health Cooperative Research Centre, Sydney, Australia.ORCID https://orcid.org/0000-0001-5425-5955
Judy Kay *Human Centred Technology Cluster, School of Computer Science, The University of Sydney, Darlington, Australia.ORCID https://orcid.org/0000-0001-6728-2768

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is an increasing interest in using routinely collected eHealth data to support reflective practice and long-term professional learning. Studies have evaluated the impact of dashboards on clinician decision-making, task completion time, user satisfaction, and adherence to clinical guidelines.

objectiveThis scoping review aims to summarize the literature on dashboards based on patient administrative, medical, and surgical data for clinicians to support reflective practice.

methodsA scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in 5 electronic databases (MEDLINE, Embase, Scopus, ACM Digital Library, and Web of Science) to identify studies that met the inclusion criteria. Study selection and characterization were performed by 2 independent reviewers (BB and CP). One reviewer extracted the data that were analyzed descriptively to map the available evidence.

resultsA total of 18 dashboards from 8 countries were assessed. Purposes for the dashboards were designed for performance improvement (10/18, 56%), to support quality and safety initiatives (6/18, 33%), and management and operations (4/18, 22%). Data visualizations were primarily designed for team use (12/18, 67%) rather than individual clinicians (4/18, 22%). Evaluation methods varied among asking the clinicians directly (11/18, 61%), observing user behavior through clinical indicators and use log data (14/18, 78%), and usability testing (4/18, 22%). The studies reported high scores on standard usability questionnaires, favorable surveys, and interview feedback. Improvements to underlying clinical indicators were observed in 78% (7/9) of the studies, whereas 22% (2/9) of the studies reported no significant changes in performance.

conclusionsThis scoping review maps the current literature landscape on dashboards based on routinely collected clinical indicator data. Although there were common data visualization techniques and clinical indicators used across studies, there was diversity in the design of the dashboards and their evaluation. There was a lack of detail regarding the design processes documented for reproducibility. We identified a lack of interface features to support clinicians in making sense of and reflecting on their personal performance data.

Indexed as

data visualizationmobile phonepractice analytics dashboardsprofessional learningreflective practice

Identifiers

PMID35156928
PMCPMC8887640

What OpenQuestion holds

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LicenceCC BY
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.