Health Providers’ Perceptions of Novel Approaches to Visualizing Integrated Health Information

Summary

Objectives: We evaluated the design of three novel visualization techniques for integrated health information with health care providers in older adult care. Through focus groups, we identified generalizable themes related to the visualization and interpretation of health information. Using these themes we address challenges with visualizing integrated health information and provide recommendations for designers.

Methods: We recruited ten health care providers to participate in three focus groups. We applied a qualitative descriptive approach to code and extract themes related to the visualization of graphical displays.

Results: We identified a set of four common themes across focus groups related to: 1) Trust in data for decision-making; 2) Per -ceived level of detail for visualization (sub-themes: holistic, individual components); 3) Cognitive issues (subthemes: training and experience; cognitive overload; contrast); and 4) Application of visual displays. Furthermore, recommendations are provided as part of the iterative design process for the visualizations.

Conclusions: Data visualization of health information is an important component of care, impacting both the accuracy and speed of decision making. There are both functional and cognitive elements to consider during the development of appropriate visualizations that integrate different components of health.

Keywords

Aged - health services - consumer health information - data display

• References

• 1 Bertin J. Graphics and graphic information-processing. de Gruyter: 1981.
• 2 Tufte ER. The visual display of quantitative information. Graphics Press; 1983.
• 3 Lurie NH, Mason C. Visual Representation: Implications for Decision Making. SSRN eLibrary (Internet, cited Aug 3, 2012). Available from. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=987295.
• 4 Speier C. The influence of information presentation formats on complex task decision-making performance. International Journal of Human-Computer Studies 2006; 64 (11) 1115-1131.
• 5 Hoeke JO, Bonke B, Van Strik R, Gelsema ES. Evaluation of techniques for the presentation of laboratory data: support of pattern recognition. Methods Inf Med 2000; 39 (01) 88-92.
• 6 Feldman-Stewart D, KocReferencesovski N, McConnell BA, Brundage MD, Mackillop WJ. Perception of quantitative information for treatment decisions. Med Decis Making 2000; 20 (02) 228-238.
• 7 Feldman-Stewart D, Brundage MD, Zotov V. Further insight into the perception of quantitative information: judgments of gist in treatment decisions. Med Decis Making 2007; 27 (01) 34-43.
• 8 Zachry M, Thralls C. An Interview with Edward R. Tufte. Technical Communication Quarterly 2004; 13 (04) 447-462.
• 9 Cleveland WS, McGill R. Graphical Perception and Graphical Methods for Analyzing Scientific Data. Science 1985; 229 4716 828-833.
• 10 Pinker, Steven. A Theory of Graph Comprehension. Artificial intelligence and the future of testing. Erlbaum; 1990.
• 11 Carpenter PA, Shah P. A model of the perceptual and conceptual processes in graph comprehension. Journal of Experimental Psychology: Applied 1998; 4 (02) 75-100.
• 12 Sandelowski M. Whatever happened to qualitative description?. Res Nurs Health 2000; 23 (04) 334-340.
• 13 Hudson R. Emerging Crisis: The Geriatric Care Workforce. National Academy on an Aging Society 2003; 13: 2
• 14 Boyd AD, Funk EA, Schwartz SM, Kaplan B, Keenan GM. Top EHR challenges in light of the stimulus. Enabling effective interdisciplinary, intradisciplinary and cross-setting communication. J Healthc Inf Manag 2010; 24 (01) 18-24.
• 15 Tsiknakis M, Katehakis D, Orphanoudakis S. A health information infrastructure enabling secure access to the life-long multimedia electronic health record. International Congress Series. 2004. 1268 289-294.
• 16 An J, Wu Z, Chen H, Lu X, Duan H. Level of detail navigation and visualization of electronic health records. 3rd International Conference on Biomedical Engineering and Informatics (BMEI). 2010: 2516-2519.
• 17 An J, Lu X, Duan H. Integrated Visualization of Multi-Modal Electronic Health Record Data. The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008. ICBBE. 2008: 640-643.
• 18 Thompson HJ, Demiris G, Rue T, Shatil E, Wilamowska K, Zaslavsky O. et al A Holistic Approach to Assess Older Adults’ Wellness Using e-Health Technologies. Telemedicine and e-Health 2011; 17 (10) 794-800.
• 19 Dunn HL. High-Level Wellness for Man and Society. Am J Public Health Nations Health 1959; 49 (06) 786-792.
• 20 Adams TB, Bezner JR, Drabbs ME, Zambarano RJ, Steinhardt MA. Conceptualization and measurement of the spiritual and psychological dimensions of wellness in a college population. J Am Coll Health 2000; 48 (04) 165-173.
• 21 Becker CM, Glascoff MA, Mitchell T, Durham TW, Arnold W. Assessing Perceived Health and Associated Health promoting Behaviors: An Investigation of Methods Used to Assess Health Status. Journal of Applied Social Psychology 2007; 37 (02) 227-242.
• 22 Bruhn JG, Cordova FD, Williams JA, Fuentes RG. The wellness process. Journal of Community Health 1977; 2 (03) 209-221.
• 23 Demiris G, Thompson HJ, Reeder B, Wilamowska K, Zaslavsky O. Using informatics to capture older adults’ wellness. Int J Med Inform (Internet). 2011 Apr 7 (cited May 17, 2011). Available from. http://www.ncbi.nlm.nih.gov/pubmed/21482182.
• 24 Goldschmidt AJ, Luz CJ, Giere W, Lüdecke R, Jonas D. Multi-dimensional visualisation of laboratory findings and functional test results for analysing the clinical course of disease in medicine. Methods Inf Med 1995; 34 (03) 302-308.
• 25 Horn W, Popow C, Unterasinger L. Support for fast comprehension of ICU data: visualization using metaphor graphics. Methods Inf Med 2001; 40 (05) 421-424.
• 26 Le T, Wilamowska K, Demiris G, Thompson H. Integrated data visualisation: an approach to capture older adults’ wellness. Int J Electron Healthc 2012; 7 (02) 89-104.
• 27 Elting LS, Martin CG, Cantor SB, Rubenstein EB. Influence of data display formats on physician investigators’ decisions to stop clinical trials: prospective trial with repeated measures. BMJ 1999; 318 7197 1527-1531.
• 28 Krueger RA, Casey MA. Focus Groups: A Practical Guide for Applied Research, Third Edition 3rd ed. Sage Publications, Inc. 2000
• 29 Morgan DL. Reconsidering the Role of Interaction in Analyzing and Reporting Focus Groups. Qual Health Res 2010; 20 (05) 718-722.
• 30 Boyatzis RE. Transforming qualitative information: thematic analysis and code development. Thousand Oaks, CA: Sage Publications; 1998.
• 31 Miles MB, Huberman AM. Qualitative data analysis: an expanded sourcebook. Thousand Oaks: Sage Publications; 1994.
• 32 Krippendorff K. Content analysis: an introduction to its methodology. Sage. 2004.
• 33 Morgan DL. Focus Groups As Qualitative Research. SAGE. 1997.
• 34 Klimov D, Shahar Y, Taieb-Maimon M. Intelligent interactive visual exploration of temporal associations among multiple time-oriented patient records. Methods Inf Med 2009; 48 (03) 254-262.