ArticleApplied clinical informatics2016
Visual Analytics for Pattern Discovery in Home Care. Clinical Relevance for Quality Improvement.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.Applied clinical informatics · 2024Article
- Using data visualization to detect patterns in whole-person health data.Research in nursing & health · 2022Article
- Usability Testing of an Interactive Dashboard for Surgical Quality Improvement in a Large Congenital Heart Center.Applied clinical informatics · 2019Article
- Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care.Applied clinical informatics · 2019Article
- Ontology-Based Interactive Visualization of Patient-Generated Research Questions.Applied clinical informatics · 2019Article
- Timely Data for Targeted Quality Improvement Interventions: Use of a Visual Analytics Dashboard for Bronchiolitis.Applied clinical informatics · 2019Article
- Intergenerational Decision Making: The Role of Family Relationships in Medical Decision Making.MDM policy & practiceReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVisualization can reduce the cognitive load of information, allowing users to easily interpret and assess large amounts of data. The purpose of our study was to examine home health data using visual analysis techniques to discover clinically salient associations between patient characteristics with problem-oriented health outcomes of older adult home health patients during the home health service period.
methodsKnowledge, Behavior and Status ratings at discharge as well as change from admission to discharge that was coded using the Omaha System was collected from a dataset on 988 de-identified patient data from 15 home health agencies. SPSS Visualization Designer v1.0 was used to visually analyze patterns between independent and outcome variables using heat maps and histograms. Visualizations suggesting clinical salience were tested for significance using correlation analysis.
resultsThe mean age of the patients was 80 years, with the majority female (66%). Of the 150 visualizations, 69 potentially meaningful patterns were statistically evaluated through bivariate associations, revealing 21 significant associations. Further, 14 associations between episode length and Charlson co-morbidity index mainly with urinary related diagnoses and problems remained significant after adjustment analyses. Through visual analysis, the adverse association of the longer home health episode length and higher Charlson co-morbidity index with behavior or status outcomes for patients with impaired urinary function was revealed.
conclusionsWe have demonstrated the use of visual analysis to discover novel patterns that described high-needs subgroups among the older home health patient population. The effective presentation of these data patterns can allow clinicians to identify areas of patient improvement, and time periods that are most effective for implementing home health interventions to improve patient outcomes.
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