Maichou Lor1, Theresa A Koleck1, Suzanne Bakken1,2,3. 1. School of Nursing, Columbia University, New York City, New York USA. 2. Department of Biomedical Informatics, Columbia University, New York City, New York USA. 3. Data Science Institute, Columbia University, New York City, New York, USA.
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.
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.
Authors: Anne M Walling; Nancy L Keating; Katherine L Kahn; Sydney Dy; Jennifer W Mack; Jennifer Malin; Neeraj K Arora; John L Adams; Anna Liza M Antonio; Diana Tisnado Journal: J Oncol Pract Date: 2016-05-24 Impact factor: 3.840
Authors: Jacqueline Vaughn; Donruedee Kamkhoad; Ryan J Shaw; Sharron L Docherty; Arvind P Subramaniam; Nirmish Shah Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497
Authors: Victoria L Tiase; Sarah E Wawrzynski; Katherine A Sward; Guilherme Del Fiol; Catherine Staes; Charlene Weir; Mollie R Cummins Journal: Appl Clin Inform Date: 2021-07-21 Impact factor: 2.342