Evidence map›Paper›PMID 31290396›Full record

ArticleJMIR diabetes2019

Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study.

Alain Giordanengo, Eirik Årsand, Ashenafi Zebene Woldaregay, Meghan Bradway, Astrid Grottland, Gunnar Hartvigsen, Conceição Granja, Torbjørn Torsvik, Anne Helen Hansen

Abstract read
In one paragraph

Article in JMIR diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Human Factors and Organizational Issues.Yearbook of medical informatics · 2020
    Review
  9. Article
  10. Article
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

9 authors.

Alain GiordanengoDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.ORCID http://orcid.org/0000-0002-1498-7083
Eirik ÅrsandNorwegian Centre for E-health Research, Tromsø, Norway.ORCID http://orcid.org/0000-0002-9520-1408
Ashenafi Zebene WoldaregayDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.ORCID http://orcid.org/0000-0002-9464-3407
Meghan BradwayNorwegian Centre for E-health Research, Tromsø, Norway.ORCID http://orcid.org/0000-0003-4540-225X
Astrid GrottlandNorwegian Centre for E-health Research, Tromsø, Norway.ORCID http://orcid.org/0000-0002-7568-7703
Gunnar HartvigsenDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.ORCID http://orcid.org/0000-0001-8771-9867
Conceição GranjaNorwegian Centre for E-health Research, Tromsø, Norway.ORCID http://orcid.org/0000-0002-3028-8899
Torbjørn TorsvikNorwegian Centre for E-health Research, Tromsø, Norway.ORCID http://orcid.org/0000-0002-6515-2545
Anne Helen HansenDepartment of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway.ORCID http://orcid.org/0000-0002-2731-6916

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntroducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users-the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data.

objectiveThe objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians.

methodsThe design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research.

resultsWe created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested.

conclusionsDisplaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians' need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

Indexed as

dashboarddecision support systemdiabetesmHealthself-collected health data

Identifiers

PMID31290396
PMCPMC6647758

What OpenQuestion holds

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LicenceCC BY
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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.