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The Patient - Patient-Centered Outcomes Research

, Volume 8, Issue 6, pp 499–505 | Cite as

Balance Sheets Versus Decision Dashboards to Support Patient Treatment Choices: A Comparative Analysis

  • James G. DolanEmail author
  • Peter J. Veazie
Short Communication

Abstract

Background

Growing recognition of the importance of involving patients in preference-driven healthcare decisions has highlighted the need to develop practical strategies to implement patient-centered shared decision-making. The use of tabular balance sheets to support clinical decision-making is well established. More recent evidence suggests that graphic, interactive decision dashboards can help people derive deeper a understanding of information within a specific decision context. We therefore conducted a non-randomized trial comparing the effects of adding an interactive dashboard to a static tabular balance sheet on patient decision-making.

Methods

The study population consisted of members of the ResearchMatch registry who volunteered to participate in a study of medical decision-making. Two separate surveys were conducted: one in the control group and one in the intervention group. All participants were instructed to imagine they were newly diagnosed with a chronic illness and were asked to choose between three hypothetical drug treatments, which varied with regard to effectiveness, side effects, and out-of-pocket cost. Both groups made an initial treatment choice after reviewing a balance sheet. After a brief “washout” period, members of the control group made a second treatment choice after reviewing the balance sheet again, while intervention group members made a second treatment choice after reviewing an interactive decision dashboard containing the same information. After both choices, participants rated their degree of confidence in their choice on a 1 to 10 scale.

Results

Members of the dashboard intervention group were more likely to change their choice of preferred drug (10.2 versus 7.5 %; p = 0.054) and had a larger increase in decision confidence than the control group (0.67 versus 0.075; p < 0.03). There were no statistically significant between-group differences in decisional conflict or decision aid acceptability.

Conclusion

These findings suggest that clinical decision dashboards may be an effective point-of-care decision-support tool. Further research to explore this possibility is warranted.

Keywords

Balance Sheet Decision Confidence Decisional Conflict Decisional Conflict Scale Prefer Treatment Option 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by Grant 1 K24 RR024198-02 from the National Heart, Lung, and Blood Institute. The funding agency did not influence the design, conduct, or reporting of the study.

James Dolan helped design, conduct, analyze, and report the study presented here. He received grant-related salary support and has no other conflicts of interest to report. He is the overall guarantor of the study description and results as presented.

Peter Veazie helped design, conduct, analyze, and report the study presented here. He also received grant-related salary support and has no other conflicts of interest to report.

Shirley X. L. Li played a major role in conducting this study. The authors are grateful for her contributions.

Supplementary material

40271_2015_111_MOESM1_ESM.docx (13 kb)
Supplementary material 1 (DOCX 13 kb)

References

  1. 1.
    Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Légaré F, Thomson R. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2014;1:CD001431.PubMedGoogle Scholar
  2. 2.
    Legare F, Witteman HO. Shared decision making: examining key elements and barriers to adoption into routine clinical practice. Health Aff. 2013;32:276–84.CrossRefGoogle Scholar
  3. 3.
    Elwyn G, Scholl I, Tietbohl C, Mann M, Edwards AG, Clay C, et al. “Many miles to go …”: a systematic review of the implementation of patient decision support interventions into routine clinical practice. BMC Med Inform Decis Mak. 2013;13(Suppl 2):S14.PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Lin GA, Halley M, Rendle KAS, Tietbohl C, May SG, Trujillo L, et al. An effort to spread decision aids in five California primary care practices yielded low distribution, highlighting hurdles. Health Aff. 2013;32:311–20.CrossRefGoogle Scholar
  5. 5.
    Elwyn G, Lloyd A, Joseph-Williams N, Cording E, Thomson R, Durand M, et al. Option grids: shared decision making made easier. Patient Educ Couns. 2013;90:207–12.CrossRefPubMedGoogle Scholar
  6. 6.
    Elwyn G, Frosch D, Thomson R, Joseph-Williams N, Lloyd A, Kinnersley P, et al. Shared decision making: a model for clinical practice. J Gen Intern Med. 2012;27:1361–7.PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Jones LA, Weymiller AJ, Shah N, Bryant SC, Christianson TJH, Guyatt GH, et al. Should clinicians deliver decision aids? Further exploration of the statin choice randomized trial results. Med Decis Mak. 2009;29:468–74.CrossRefGoogle Scholar
  8. 8.
    Légaré F, Ratté S, Stacey D, Kryworuchko J, Gravel K, Graham ID, Turcotte S. Interventions for improving the adoption of shared decision making by healthcare professionals. Cochrane Database Syst Rev. 2010;(5):CD006732.Google Scholar
  9. 9.
    Montori VM, Breslin M, Maleska M, Weymiller AJ. Creating a conversation: insights from the development of a decision aid. PLoS Med. 2007;4:e233.PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Mullan RJ, Montori VM, Shah ND, Christianson TJH, Bryant SC, Guyatt GH, et al. The diabetes mellitus medication choice decision aid: a randomized trial. Arch Intern Med. 2009;169:1560–8.CrossRefPubMedGoogle Scholar
  11. 11.
    Few S. Now you see it: simple visualization techniques for quantitative analysis. Oakland: Analytics Press; 2009.Google Scholar
  12. 12.
    Spiegelhalter D, Pearson M, Short I. Visualizing uncertainty about the future. Science. 2011;333:1393–400.CrossRefPubMedGoogle Scholar
  13. 13.
    Chen C. Information visualization. Wiley Interdiscip Rev Comput Stat. 2010;2:387–403.CrossRefGoogle Scholar
  14. 14.
    Few S. Information dashboard design: the effective visual communication of data. 1st ed. Beijing: O’Reilly; 2006.Google Scholar
  15. 15.
    Watson HJ, Wixom BH. The current state of business intelligence. Computer. 2007;40:96–9.CrossRefGoogle Scholar
  16. 16.
    Yigitbasioglu OM, Velcu O. A review of dashboards in performance management: implications for design and research. Int J Account Inf Syst. 2012;13:41–59.CrossRefGoogle Scholar
  17. 17.
    Dolan JG, Veazie PJ, Russ AJ. Development and initial evaluation of a treatment decision dashboard. BMC Med Inform Decis Mak. 2013;13:51.PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Harris PA, Scott KW, Lebo L, Hassan N, Lighter C, Pulley J. ResearchMatch: a national registry to recruit volunteers for clinical research. Acad Med. 2012;87:66.PubMedCentralCrossRefPubMedGoogle Scholar
  19. 19.
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: development of the subjective numeracy scale. Med Decis Mak. 2007;27:672–80.CrossRefGoogle Scholar
  20. 20.
    Chew LD, Griffin JM, Partin MR, Noorbaloochi S, Grill JP, Snyder A, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23:561–6.PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.
    O’Connor AM. Validation of a decisional conflict scale. Med Decis Mak. 1995;15:25–30.CrossRefGoogle Scholar
  22. 22.
    Tukey JW. Exploratory data analysis. Reading: Addison-Wesley; 1976.Google Scholar
  23. 23.
    SAS Institute, Inc. JMP 10.0.2. Cary: SAS Institute, Inc.; 2012.Google Scholar
  24. 24.
    Hibbard JH, Peters E. Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice. Annu Rev Public Health. 2003;24:413–33.CrossRefPubMedGoogle Scholar
  25. 25.
    Ware C. Information visualization: perception for design. Amsterdam: Elsevier/MK; 2013.Google Scholar
  26. 26.
    Vessey I. Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decis Sci. 1991;22:219–40.CrossRefGoogle Scholar
  27. 27.
    Oppenheimer DM, Frank MC. A rose in any other font would not smell as sweet: effects of perceptual fluency on categorization. Cognition. 2008;106:1178–94.CrossRefPubMedGoogle Scholar
  28. 28.
    Shah AK, Oppenheimer DM. Easy does it: the role of fluency in cue weighting. Judgm Decis Mak. 2007;2:371–9.Google Scholar
  29. 29.
    Reber R, Schwarz N. Effects of perceptual fluency on judgments of truth. Conscious Cognit. 1999;8:338–42.CrossRefGoogle Scholar
  30. 30.
    Hansen J, Dechêne A, Wänke M. Discrepant fluency increases subjective truth. J Exp Soc Psychol. 2008;44:687–91.CrossRefGoogle Scholar
  31. 31.
    Nelson W, Reyna VF, Fagerlin A, Lipkus I, Peters E. Clinical implications of numeracy: theory and practice. Ann Behav Med. 2008;35:261–74.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Public Health Sciences, University of Rochester Medical CenterUniversity of Rochester School of Medicine and DentistryRochesterUSA

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