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Using data visualization to detect patterns in whole-person health data.

Robin R Austin1, Michelle A Mathiason1, Karen A Monsen1

  • 1School of Nursing, University of Minnesota, Minneapolis, MN, USA.

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|June 19, 2022
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Summary
This summary is machine-generated.

Older adults generally have more strengths than challenges and needs. Exploratory data analysis (EDA) using visualization patterns revealed this, along with an unexpected finding about needs in a subset of this population.

Keywords:
Data visualizationsHealth information technologyNursing informatics

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Area of Science:

  • Gerontology and Public Health
  • Data Science and Visualization

Background:

  • Whole-person health encompasses environmental, psychosocial, and physical well-being, alongside behaviors, strengths, challenges, and needs.
  • Assessing whole-person health in older adults (65+) is crucial for understanding their comprehensive health status.
  • Standardized data collection instruments, like the Omaha System, facilitate interrelationship analysis.

Purpose of the Study:

  • To apply visualization pattern detection and testing to deidentified data for discovering patterns in whole-person health among adults aged 65 and older.
  • To explore the relationships between strengths, challenges, and needs within this demographic using exploratory data analysis (EDA).

Main Methods:

  • Collected deidentified data via a mobile application using a simplified version of the Omaha System.
  • Employed an iterative visualization pattern detection process including bubble charts, parallel coordinates, box plots, and alluvial flow diagrams.
  • Formulated and tested six hypotheses derived from six discovered visualization patterns.

Main Results:

  • All six hypotheses were supported, indicating significant patterns in whole-person health data.
  • Adults 65+ demonstrated more strengths than challenges, and more challenges than needs (p < 0.001).
  • A subset of older adults with many, but not all, strengths unexpectedly showed significantly higher needs (p = 0.04).

Conclusions:

  • Visualization pattern detection is effective for identifying complex patterns in whole-person health data.
  • Standardized terminology and data interrelationships are key to successful pattern discovery in health assessments.
  • The methodology offers a robust approach for future EDA research on diverse datasets.