Information visualizations of symptom information for patients and providers: a systematic review. (7th December 2018)

• Record Type: Journal Article
• Authors: Lor, Maichou; Koleck, Theresa A; Bakken, Suzanne
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 2(2019)
• Page Start: 162
• Page End: 171
• Publication Date: 2018-12-07
• Subjects: visualization -- symptom science -- communication; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocy152
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To systematically synthesize the literature on information visualizations of symptoms included as National Institute of Nursing Research common data elements and designed for use by patients and/or healthcare providers. Methods: We searched CINAHL, Engineering Village, PsycINFO, PubMed, ACM Digital Library, and IEEE Explore Digital Library to identify peer-reviewed studies published between 2007 and 2017. We evaluated the studies using the Mixed Methods Appraisal Tool (MMAT) and a visualization quality score, and organized evaluation findings according to the Health Information Technology Usability Evaluation Model. Results: Eighteen studies met inclusion criteria. Ten of these addressed all MMAT items; 13 addressed all visualization quality items. Symptom visualizations focused on pain, fatigue, and sleep and were represented as graphs (n = 14), icons (n = 4), and virtual body maps (n = 2). Studies evaluated perceived ease of use (n = 13), perceived usefulness (n = 12), efficiency (n = 9), effectiveness (n = 5), preference (n = 6), and intent to use (n = 3). Few studies reported race/ethnicity or education level. Conclusion: The small number of studies for each type of information visualization limit generalizable conclusions about optimal visualization approaches. User-centered participatory approaches for information visualization design and more sophisticated evaluation designs are needed to assess which visualization elements work best for which populations in which contexts.