Visualization of Electronic Health Record Data for Decision-Making in Diabetes and Congestive Heart Failure

Abstract

Objective The aim of this study is to study the impact of graphical representation of health record data on physician decision-making to inform the design of health information technology.

Materials and Methods We conducted a within participants crossover design study using a simulated electronic health record (EHR) in which we presented cases with and without visualized data designed to highlight important clinical trends or relationships, followed by assessment of the impact on decision-making about next steps for patients with chronic diseases. We then asked whether trends were observed and about usability and satisfaction using validated usability questions and asked open-ended questions as well. Time to answer questions was also collected.

Results Twenty-one primary care providers participated in the study, including five for testing only and sixteen for the full study. Questions about clinical assessment or next actions were answered correctly 55% of the time. Regarding objective trends in the data, participants described noticing the trends 85% of the time. Differences in noticing trends or difficulty level of questions were not statistically significant. Satisfaction with the tool was high and participants agreed strongly that it helped them make better decisions without adding to the time it took.

Discussion The simulation allowed us to test the impact of a visualization on clinician practice in a realistic setting. Designers of EHRs should consider the ways information presentation can affect decision-making.

Conclusion Testing visualization tools can be done in a clinically realistic context. Providers desire visualizations and believe that they help them make better and faster decisions.

Keywords

visualization - decision-making - diabetes - congestive heart failure - electronic health record

Protection of Human and Animal Subjects

This research was reviewed and deemed exempt by the hospital's institutional review board.

• References

• 1 Reiser SJ. The clinical record in medicine. Part 1: Learning from cases. Ann Intern Med 1991; 114 (10) 902-907
• 2 U.S. Department of Health & Human Services. Office of the Secretary. Health Information Technology: standards, implementation specifications, and certification criteria for electronic health record technology, 2014 edition; Revisions to the Permanent Certification Program for Health Information Technology. 45 CFR Part 170 RIN 0991–AB82. 2012 . Available at: http://www.gpo.gov/fdsys/pkg/FR-2012-09-04/pdf/2012-20982.pdf . Accessed January 24, 2020
• 3 Jha AK, DesRoches CM, Kralovec PD, Joshi MS. A progress report on electronic health records in U.S. hospitals. Health Aff (Millwood) 2010; 29 (10) 1951-1957
• 4 Jha AK, Ferris TG, Donelan K. , et al. How common are electronic health records in the United States? A summary of the evidence. Health Aff (Millwood) 2006; 25 (06) w496-w507
• 5 Hsiao C-J, Hing E. NCHS data brief: use and characteristics of electronic health record systems among office-based physician practices: United States, 2001–2013. 2014 ; Number 143, January 2014: Available at: http://www.cdc.gov/nchs/data/databriefs/db143.htm . Accessed April, 2014
• 6 Bleich HL, Slack WV. Reflections on electronic medical records: when doctors will use them and when they will not. Int J Med Inform 2010; 79 (01) 1-4
• 7 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
• 8 Walker JM. Influence of data display formats on decisions to stop clinical trials. Paper is misleading, like a sheep dressed in a wolf's clothing. BMJ 1999; 319 (7216): 1070
• 9 Marshall T, Mohammed MA, Rouse A. A randomized controlled trial of league tables and control charts as aids to health service decision-making. Int J Qual Health Care 2004; 16 (04) 309-315
• 10 Tan JKH, Benbasat I. Processing of graphical information: a decomposition taxonomy to match data extraction tasks and graphical representations. Inf Syst Res 1990; 416-439 . Available at: http://connection.ebscohost.com/c/articles/4431032/processing-graphical-information-decomposition-taxonomy-match-data-extraction-tasks-graphical-representations
• 11 Kumar N, Benbasat I. The effect of relationship encoding, task type, and complexity on information representation: an empirical evaluation of 2D and 3D line graphs. Manage Inf Syst Q 2004; 28 (02) 255-281
• 12 Kim Y, Heer J. Assessing effects of task and data distribution on the effectiveness of visual encodings. Comput. Graph. Forum 2018; Available at: https://www.semanticscholar.org/paper/Assessing-Effects-of-Task-and-Data-Distribution-on-Kim-Heer/6979c6e6f385263cfd5dfc34d70e30dddd07778d . Accessed January 24, 2020
• 13 Demiralp Ç, Bernstein MS, Heer J. Learning perceptual kernels for visualization design. IEEE Trans Vis Comput Graph 2014; 20 (12) 1933-1942
• 14 Wu DTY, Chen AT, Manning JD. , et al. Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation. J Am Med Inform Assoc 2019; 26 (04) 314-323
• 15 Samal L, Wright A, Wong BT, Linder JA, Bates DW. Leveraging electronic health records to support chronic disease management: the need for temporal data views. Inform Prim Care 2011; 19 (02) 65-74
• 16 Bauer DT, Guerlain S, Brown PJ. The design and evaluation of a graphical display for laboratory data. J Am Med Inform Assoc 2010; 17 (04) 416-424
• 17 Torsvik T, Lillebo B, Mikkelsen G. Presentation of clinical laboratory results: an experimental comparison of four visualization techniques. J Am Med Inform Assoc 2013; 20 (02) 325-331
• 18 Mishuris RG, Yoder J, Wilson D, Mann D. Integrating data from an online diabetes prevention program into an electronic health record and clinical workflow, a design phase usability study. BMC Med Inform Decis Mak 2016; 16: 88
• 19 Plaisant C. The challenge of information visualization evaluation. Proceedings of the working conference on Advanced visual interfaces. Vol Gallipoli, Italy: ACM; 2004 . Available at: https://dl.acm.org/doi/10.1145/989863.989880 . Accessed January 24, 2020
• 20 Friel SN, Curcio FR, Bright GW. Making sense of graphs: Critical factors influencing comprehension and instructional implications. J Res Math Educ 2001; 32 (02) 124-158
• 21 Tufte ER. Beautiful Evidence. Vol 1. Cheshire, CT: Graphics Press; 2006
• 22 Tufte ER, Graves-Morris PR. The Visual Display of Quantitative Information. Vol 2. Cheshire, CT: Graphics press; 1983
• 23 Tufte ER, Robins D. Visual Explanations. Vol 25. Cheshire, CT: Graphics Press; 1997
• 24 Few S. Information Dashboard Design. Sebastopol, CA: O'Reilly; 2006
• 25 Few S. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press; 2009
• 26 Few S. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Vol 1. Oakland, CA: Analytics Press; 2004
• 27 Google Developers. Google charts. Available at: https://google-developers.appspot.com/chart/interactive/docs/index . Accessed January 24, 2020
• 28 Python Software Foundation. (2013). Python 2.7.5: Anaconda 1.8.0 (x86_64). Available at: http://www.python.org . Accessed January 24, 2020
• 29 R Core Team. (2013). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: http://www.R-project.org/ . ISBN 3–900051–07–0. Accessed January 24, 2020
• 30 StataCorp. Stata Statistical Software: Release 11. College Station, TX: StataCorp LP; 2009
• 31 Lewis JR. IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int J Hum Comput Interact 1995; 7 (01) 57-78
• 32 Shortliffe EH, Cimino JJ. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 4 ed. London: Springer; 2014
• 33 Krein SL, Hofer TP, Kerr EA, Hayward RA. Whom should we profile? Examining diabetes care practice variation among primary care providers, provider groups, and health care facilities. Health Serv Res 2002; 37 (05) 1159-1180
• 34 Brooks JM, Cook EA, Chapman CG. , et al. Geographic variation in statin use for complex acute myocardial infarction patients: evidence of effective care?. Med Care 2014; 52 (Suppl. 03) S37-S44
• 35 Cabana MD, Rand CS, Powe NR. , et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999; 282 (15) 1458-1465
• 36 Elstein AS, Schwartz A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ 2002; 324 (7339): 729-732
• 37 Patel VL, Groen GJ. Knowledge based solution strategies in medical reasoning. Cogn Sci 1986; 10 (01) 91-116
• 38 Thyvalikakath TP, Dziabiak MP, Johnson R. , et al. Advancing cognitive engineering methods to support user interface design for electronic health records. Int J Med Inform 2014; 83 (04) 292-302