Evidence map›Paper›PMID 31438942›Full record

ArticleBMC medical informatics and decision making2019

Health timeline: an insight-based study of a timeline visualization of clinical data.

Andres Ledesma, Niranjan Bidargaddi, Jörg Strobel, Geoffrey Schrader, Hannu Nieminen, Ilkka Korhonen, Miikka Ermes

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. ACE: the Advanced Cohort Engine for searching longitudinal patient records.Journal of the American Medical Informatics Association : JAMIA · 2021
    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

7 authors.

Andres LedesmaTampere University, Faculty of Medicine and Health Technology, Tampere, Finland. andres.ledesma@tut.fi.ORCID 0000-0003-1932-573X
Niranjan BidargaddiCollege of Medicine & Public Health, Flinders University, Medical Sciences Rd, Adelaide, Australia.
Jörg StrobelDiscipline of Psychiatry, The Queen Elizabeth Hospital, South Australia, University of Adelaide, 28 Woodville Rd, Woodville South, Adelaide, Australia.
Geoffrey SchraderCountry Health Local Health Network, South Australia, and Flinders University, Medical Sciences Rd, Adelaide, Australia.
Hannu NieminenTampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
Ilkka KorhonenTampere University, Faculty of Medicine and Health Technology, Tampere, Finland.
Miikka ErmesVTT Technical Research Center Ltd, Tekniikankatu 1, Tampere, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increasing complexity and volume of clinical data poses a challenge in the decision-making process. Data visualizations can assist in this process by speeding up the time required to analyze and understand clinical data. Even though empirical experiments show that visualizations facilitate clinical data understanding, a consistent method to assess their effectiveness is still missing.

methodsThe insight-based methodology determines the quality of insights a user acquires from the visualization. Insights receive a value from one to five points based on a domain-specific criteria. Five professional psychiatrists took part in the study using real de-identified clinical data spanning 4 years of medical history.

resultsA total of 50 assessments were transcribed and analyzed. Comparing a total of 558 insights using Health Timeline and 576 without, the mean value using the Timeline (1.7) was higher than without (1.26; p<0.01), similarly the cumulative value with the Timeline (11.87) was higher than without (10.96: p<0.01). The average time required to formulate the first insight with the Timeline was higher (13.16 s) than without (7 s; p<0.01). Seven insights achieved the highest possible value using Health Timeline while none were obtained without it.

conclusionsThe Health Timeline effectively improved understanding of clinical data and helped participants recognize complex patterns from the data. By applying the insight-based methodology, the effectiveness of the Health Timeline was quantified, documented and demonstrated. As an outcome of this exercise, we propose the use of such methodologies to measure the effectiveness of visualizations that assist the clinical decision-making process.

Indexed as

Clinical Decision-MakingData DisplayPsychiatryAdultFemaleHumansMaleTime FactorsClinical dataData visualizationElectronic health recordHealth informaticsInsight-based methodology

Identifiers

PMID31438942
PMCPMC6704521

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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.