Communicating health information with visual displays.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
31
01
2023
accepted:
28
03
2023
medline:
24
5
2023
pubmed:
9
5
2023
entrez:
8
5
2023
Statut:
ppublish
Résumé
Well-designed visual displays have the power to convey health messages in clear, effective ways to non-experts, including journalists, patients and policymakers. Poorly designed visual displays, however, can confuse and alienate recipients, undermining health messages. In this Perspective, we propose a structured framework for effective visual communication of health information, using case examples of three common communication tasks: comparing treatment options, interpreting test results, and evaluating risk scenarios. We also show simple, practical ways to evaluate a design's success and guide improvements. The proposed framework is grounded in research on health risk communication, visualization and decision science, as well as our experience in communicating health data.
Identifiants
pubmed: 37156935
doi: 10.1038/s41591-023-02328-1
pii: 10.1038/s41591-023-02328-1
doi:
Types de publication
Journal Article
Review
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1085-1091Subventions
Organisme : CDC HHS
Pays : United States
Informations de copyright
© 2023. Springer Nature America, Inc.
Références
Tversky, B. Visualizing thought. Top. Cogn. Sci. 3, 499–535 (2011).
doi: 10.1111/j.1756-8765.2010.01113.x
pubmed: 25164401
Larkin, J. H. & Simon, H. A. Why a diagram is (sometimes) worth ten thousand words. Cogn. Sci. 11, 65–100 (1987).
doi: 10.1111/j.1551-6708.1987.tb00863.x
COVID Crisis Group. Lessons from the COVID War: an Investigative Report (PublicAffairs, 2023).
Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M. & Hullman, J. The science of visual data communication: what works. Psychol. Sci. Public Interest 22, 110–161 (2021).
doi: 10.1177/15291006211051956
pubmed: 34907835
Hildon, Z., Allwood, D. & Black, N. Impact of format and content of visual display of data on comprehension, choice and preference: a systematic review. Int. J. Qual. Health Care 24, 55–64 (2012).
doi: 10.1093/intqhc/mzr072
Sibrel, S. C., Rathore, R., Lessard, L. & Schloss, K. B. The relation between color and spatial structure for interpreting colormap data visualizations. J. Vis. 20, 7 (2020).
doi: 10.1167/jov.20.12.7
pubmed: 33201220
pmcid: 7683863
Spicer, J., Zhu, J. Q., Chater, N. & Sanborn, A. N. Perceptual and cognitive judgments show both anchoring and repulsion. Psychol. Sci. 33, 1395–1407 (2022).
doi: 10.1177/09567976221089599
pubmed: 35876741
Zhang, Y. et al. Mapping the landscape of COVID-19 crisis visualizations. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 1–23 (ACM, 2021).
Zikmund-Fisher, B. J. et al. Blocks, ovals, or people? Icon type affects risk perceptions and recall of pictographs. Med. Decis. Making 34, 443–453 (2014).
doi: 10.1177/0272989X13511706
pubmed: 24246564
Hullman, J., Qiao, X., Correll, M., Kale, A. & Kay, M. In pursuit of error: a survey of uncertainty visualization evaluation. IEEE Trans. Vis. Comput. Graph. 25, 903–913 (2019).
doi: 10.1109/TVCG.2018.2864889
National Cancer Institute Office of Communications and Education. Making Data Talk: a Workbook https://www.cancer.gov/publications/health-communication/making-data-talk.pdf (2011).
Centers for Disease Control and Prevention. Visual Communication Resources https://www.cdc.gov/healthliteracy/developmaterials/visual-communication.html (2023).
Kahneman, D. Thinking, Fast and Slow (Farrar, Straus and Giroux, 2013).
Fischhoff, B. & Broomell, S. B. Judgment and decision making. Annu. Rev. Psychol. 71, 331–355 (2020).
doi: 10.1146/annurev-psych-010419-050747
pubmed: 31337275
Fischhoff, B. The sciences of science communication. Proc. Natl Acad. Sci. USA 110, 14033–14039 (2013).
doi: 10.1073/pnas.1213273110
pubmed: 23942125
pmcid: 3752164
Bruine De Bruin, W. & Bostrom, A. Assessing what to address in science communication. Proc. Natl Acad. Sci. USA 110, 14062–14068 (2013).
doi: 10.1073/pnas.1212729110
pubmed: 23942122
pmcid: 3752171
von Winterfeldt, D. & Edwards, W. Decision Analysis and Behavioral Research (Cambridge University Press, 1986).
Centers for Disease Control and Prevention. Health Literacy https://www.cdc.gov/healthliteracy/index.html (2023).
Santana, S. et al. Updating health literacy for Healthy People 2030: defining its importance for a new decade in public health. J. Public Health Manag. Pract. 27, S258–S264 (2021).
doi: 10.1097/PHH.0000000000001324
pubmed: 33729194
pmcid: 8435055
Tversky, A. & Kahneman, D. Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).
doi: 10.1126/science.185.4157.1124
pubmed: 17835457
Ericsson, K. A. & Simon, H. A. Protocol Analysis: Verbal Reports as Data (MIT Press, 1993).
Merton, R. K. The focused interview and focus groups: continuities and discontinuities. Public Opin. Q. 51, 550–566 (1987).
doi: 10.1086/269057
Galesic, M. & Garcia-Retamero, R. Graph literacy: a cross-cultural comparison. Med. Decis. Making 31, 444–457 (2011).
doi: 10.1177/0272989X10373805
pubmed: 20671213
Peters, E. Innumeracy in the Wild: Misunderstanding and Misusing Numbers (Oxford University Press, 2020).
Peters, E. et al. Numeracy and decision making. Psychol. Sci. 17, 407–413 (2006).
doi: 10.1111/j.1467-9280.2006.01720.x
pubmed: 16683928
Fagerlin, A. et al. Measuring numeracy without a math test: development of the subjective numeracy scale. Med. Decis. Making 27, 672–680 (2007).
doi: 10.1177/0272989X07304449
pubmed: 17641137
Drummond, C. & Fischhoff, B. Development and validation of the scientific reasoning scale. J. Behav. Decis. Mak. 30, 26–38 (2017).
doi: 10.1002/bdm.1906
Parker, A. M., Bruine de Bruin, W., Fischhoff, B. & Weller, J. Robustness of decision-making competence: evidence from two measures and an 11-year longitudinal study. J. Behav. Decis. Mak. 31, 380–391 (2018).
doi: 10.1002/bdm.2059
pubmed: 30083026
Nutbeam, D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot. Int. 15, 259–267 (2000).
doi: 10.1093/heapro/15.3.259
Morgan, K. & Fischhoff, B. Mental models for scientists communicating with the public. Issues Sci. Technol. 39, 58–61 (2023).
Nickerson, R. S. How we know —and sometimes misjudge—what others know: imputing one’s own knowledge to others. Psychol. Bull. 125, 737–759 (1999).
doi: 10.1037/0033-2909.125.6.737
Tullis, J. G. & Feder, B. The ‘curse of knowledge’ when predicting others’ knowledge. Mem. Cogn. https://doi.org/10.3758/s13421-022-01382-3 (2022).
doi: 10.3758/s13421-022-01382-3
Woloshin, K. K., Ruffin, M. T. 4th & Gorenflo, D. W. Patients’ interpretation of qualitative probability statements. Arch. Fam. Med. 3, 961–966 (1994).
doi: 10.1001/archfami.3.11.961
pubmed: 7804478
Bryant, G. D. & Norman, G. R. Expressions of probability: words and numbers. N. Engl. J. Med. 302, 411 (1980).
doi: 10.1056/NEJM198002143020717
pubmed: 7351941
Schwartz, L. M., Woloshin, S. & Welch, H. G. Using a drug facts box to communicate drug benefits and harms: two randomized trials. Ann. Intern. Med. 150, 516–527 (2009).
doi: 10.7326/0003-4819-150-8-200904210-00106
pubmed: 19221371
Schwartz, L. M. & Woloshin, S. The drug facts box: improving the communication of prescription drug information. Proc. Natl Acad. Sci. USA 110, 14069–14074 (2013).
doi: 10.1073/pnas.1214646110
pubmed: 23942130
pmcid: 3752172
Schwartz, L. M., Woloshin, S. & Welch, H. G. The drug facts box: providing consumers with simple tabular data on drug benefit and harm. Med. Decis. Making 27, 655–662 (2007).
doi: 10.1177/0272989X07306786
pubmed: 17873258
Reyna, V. F. A scientific theory of gist communication and misinformation resistance, with implications for health, education, and policy. Proc. Natl Acad. Sci. USA 118, e1912441117 (2021).
doi: 10.1073/pnas.1912441117
pubmed: 32312815
pmcid: 8054009
Woloshin, S. & Schwartz, L. M. Communicating data about the benefits and harms of treatment: a randomized trial. Ann. Intern. Med. 155, 87–96 (2011).
doi: 10.7326/0003-4819-155-2-201107190-00004
pubmed: 21768582
Kahneman, D. & Tversky, A. On the psychology of prediction. Psychol. Rev. 80, 237–251 (1973).
doi: 10.1037/h0034747
Woloshin, S., Dewitt, B., Krishnamurti, T. & Fischhoff, B. Assessing how consumers interpret and act on results from at-home COVID-19 self-test kits: a randomized clinical trial. JAMA Intern. Med. 182, 332–341 (2022).
Director’s Guild of America, Screen Actors Guild–American Federation of Television and Radio Artists, International Alliance of Theatrical Stage Employees & Teamsters Motion Picture & Theatrical Trade Division. The Safe Way Forward: a Joint Report of the DGA, SAG-AFTRA, IATSE and Teamsters’ Committees for COVID-19 Safety Guidelines https://www.sagaftra.org/files/sa_documents/ProductionSafetyGuidelines_June2020EditedP.pdf (2023).
Rodriguez, V. L., Fischhoff, B. & Davis, A. L. Risk heatmaps as visual displays: opening movie studios after the COVID‐19 shutdown. Risk Anal. https://doi.org/10.1111/risa.14017 (2022).
doi: 10.1111/risa.14017
pubmed: 36115696
Moreland, K. Why we use bad color maps and what you can do about it. Electron. Imaging 28, art00022 (2016).
doi: 10.2352/ISSN.2470-1173.2016.16.HVEI-133