Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-05-12T10:08:14.664Z Has data issue: false hasContentIssue false
  • English
  • Français

PATIENT-CENTERED DECISION MAKING: LESSONS FROM MULTI-CRITERIA DECISION ANALYSIS FOR QUANTIFYING PATIENT PREFERENCES

Published online by Cambridge University Press:  26 December 2017

Kevin Marsh ,
J. Jaime Caro ,
Erica Zaiser ,
James Heywood  and
Alaa Hamed
Kevin Marsh
Affiliation:
Eviderakevin.marsh@evidera.com
J. Jaime Caro
Affiliation:
Evidera, Waltham, Massachusetts; McGill University
Erica Zaiser
Affiliation:
Evidera
James Heywood
Affiliation:
Patients Like Me
Alaa Hamed
Affiliation:
Sanofi Genzyme Patient Outcomes and Medical Economics, Genzyme
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Patient preferences should be a central consideration in healthcare decision making. However, stories of patients challenging regulatory and reimbursement decisions has led to questions on whether patient voices are being considered sufficiently during those decision making processes. This has led some to argue that it is necessary to quantify patient preferences before they can be adequately considered.

Methods: This study considers the lessons from the use of multi-criteria decision analysis (MCDA) for efforts to quantify patient preferences. It defines MCDA and summarizes the benefits it can provide to decision makers, identifies examples of MCDAs that have involved patients, and summarizes good practice guidelines as they relate to quantifying patient preferences.

Results: The guidance developed to support the use of MCDA in healthcare provide some useful considerations for the quantification of patient preferences, namely that researchers should give appropriate consideration to: the heterogeneity of patient preferences, and its relevance to decision makers; the cognitive challenges posed by different elicitation methods; and validity of the results they produce. Furthermore, it is important to consider how the relevance of these considerations varies with the decision being supported.

Conclusions: The MCDA literature holds important lessons for how patient preferences should be quantified to support healthcare decision making.

Keywords

MCDAMethodologyPatient voicePatient preferencesPatient centered care
Type
Policies
Information
International Journal of Technology Assessment in Health Care , Volume 34 , Issue 1 , 2018 , pp. 105 - 110
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Facey, K, Boivin, A, Gracia, J, et al. Patients' perspectives in health technology assessment: A route to robust evidence and fair deliberation. Int J Technol Assess Health Care. 2010;26:334–340.CrossRefGoogle ScholarPubMed
2. Fleurence, R, Selby, JV, Odom-Walker, K, et al. How the Patient-Centered Outcomes Research Institute is engaging patients and others in shaping its research agenda. Health Aff (Millwood). 2013;32:393–400.Google Scholar
3. Fowler, FJ Jr, CA, Levin, Sepucha, KR. Informing and involving patients to improve the quality of medical decisions. Health Aff (Millwood). 2011;30:699–706.Google Scholar
4. Egbrink, MO, Ijzerman, M. The value of quantitative patient preferences in regulatory benefit-risk assessment. J Mark Access Health Policy. 2014;2:22761.Google Scholar
5. Frosch, DL. The patient is the most important member of the team. BMJ. 2015;350:g7767.Google Scholar
6. Agoritsas, T, Heen, AF, Brandt, L, et al. Decision aids that really promote shared decision making: The pace quickens. BMJ. 2015;350:g7624.CrossRefGoogle ScholarPubMed
7. Pollack, A. FDA panel recommends M.S. drug despite lethal risk. The New York Times. New York, NY; 2006.Google Scholar
8. Fagerlin, A, Zikmund-Fisher, BJ, Ubel, PA. Helping patients decide: Ten steps to better risk communication. J Natl Cancer Inst. 2011;103:1436–1443.Google Scholar
9. Los Angeles Times. FDA panel backs ‘pink Viagra’ for sexual dysfunction in women. Los Angeles Times; 2015.Google Scholar
10. Patient-Centered Outcomes Research Institute (PCORI). National priorities for research and research agenda. 2012. https://www.pcori.org/research-results/research-we-support/national-priorities-and-research-agenda (accessed November 29, 2017).Google Scholar
11. European Medicines Agency (EMA). Pilot phase to involve patients in benefit/risk discussions at CHMP meetings. EMA/372554/2014 – rev. 1. 2014. http://www.ema.europa.eu/docs/en_GB/document_library/Other/2014/09/WC500173509.pdf (accessed November 29, 2017).Google Scholar
12. Food and Drug Administration (FDA). FAQs about the patient representative program. 2015. https://www.fda.gov/ForPatients/About/ucm412529.htm (accessed November 29, 2017).Google Scholar
13. deBronkart, D. From patient centred to people powered: Autonomy on the rise. BMJ. 2015;350:h148.Google Scholar
14. Silverstein A. Patient commentary: What I need to self manage my care. BMJ. 2015;350:h248.Google Scholar
15. Braddock, CH III. The emerging importance and relevance of shared decision making to clinical practice. Med Decis Making. 2010;30: 5S-7S.Google Scholar
16. Kravitz, RL, Melnikow, J. Engaging patients in medical decision making. BMJ. 2001;323:584–585.Google Scholar
17. Davies, E, Cleary, PD. Hearing the patient's voice? Factors affecting the use of patient survey data in quality improvement. Qual Saf Health Care. 2005;14:428-332.Google Scholar
18. Ubel PA. Beyond costs and benefits: Understanding how patients make health care decisions. Oncologist. 2010;15 (Suppl 1):5–10.Google Scholar
19. Weernink, MGM, Janus, SIM, van Til, JA, et al. A systematic review to identify the use of preference elicitation methods in healthcare decision making. Pharm Med. 2014;28:175–185.Google Scholar
20. Marsh, K, IJzerman, M, Thokala, P, et al. Multiple criteria decision analysis for health care decision making-emerging good practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force. Value Health. 2016;19:125–137.Google Scholar
21. Belton, V, Stewart, TJ. Multiple criteria decision analysis: An integrated approach. The Netherlands: Kluwer Academic Publishers; 2002.Google Scholar
22. Dodgson, JS, Spackman, M, Pearman, A, et al. Multi-criteria analysis: A manual. London, UK: Department for Communities and Local Government; 2009.Google Scholar
23. Baltussen, R, Niessen, L. Priority setting of health interventions: The need for multi-criteria decision analysis. Cost Eff Resour Alloc. 2006; 4:14.Google Scholar
24. Devlin, NJ, Sussex, J. Incorporating multiple criteria in HTA: Methods and processes. 2011. https://www.ohe.org/publications/incorporating-multiple-criteria-hta-methods-and-processes (accessed November 29, 2017).Google Scholar
25. Marsh, K, Lanitis, T, Neasham, D, et al. Assessing the value of healthcare interventions using multi-criteria decision analysis: A review of the literature. Pharmacoeconomics. 2014;32:345–365.Google Scholar
26. Thokala, P, Duenas, A. Multiple criteria decision analysis for health technology assessment. Value Health. 2012;15:1172–1181.Google Scholar
27. Danner, M, Hummel, JM, Volz, F, et al. Integrating patients' views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences. Int J Technol Assess Health Care. 2011;27:369–375.Google Scholar
28. Hummel, MJ, Volz, F, van Manen, JG, et al. Using the analytic hierarchy process to elicit patient preferences: Prioritizing multiple outcome measures of antidepressant drug treatment. Patient. 2012;5:225–237.Google Scholar
29. Muhlbacher, AC, Bridges, JF, Bethge, S, et al. Preferences for antiviral therapy of chronic hepatitis C: A discrete choice experiment. Eur J Health Econ. 2017;18:155–165.Google Scholar
30. Dolan, JG. Shared decision-making–transferring research into practice: The Analytic Hierarchy Process (AHP). Patient Educ Couns. 2008;73:418–425.Google Scholar
31. Mulley, AG, Trimble, C, Elwyn, G. Stop the silent misdiagnosis: Patients' preferences matter. BMJ. 2012;345:e6572.Google Scholar
32. Marewski, JN, Gigerenzer, G. Heuristic decision making in medicine. Dialogues Clin Neurosci. 2012;14:77–89.Google Scholar
33. Airoldi, M, Morton, A, Smith, J, Bevan, G. Working paper no. 7. Healthcare prioritisation at the local level: A socio-technical approach. 2011. https://pdfs.semanticscholar.org/5146/262fe06cdbae5159562ece5e9e652966a30c.pdf (accessed November 29, 2017).Google Scholar
34. Goetghebeur, MM, Wagner, M, Khoury, H, et al. Combining multicriteria decision analysis, ethics and health technology assessment: Applying the EVIDEM decision-making framework to growth hormone for Turner syndrome patients. Cost Eff Resour Alloc. 2010;8:4.Google Scholar
35. Sussex, J, Rollet, P, Garau, M, et al. A pilot study of multicriteria decision analysis for valuing orphan medicines. Value Health. 2013;16:1163–1169.Google Scholar
36. Youngkong, S, Teerawattananon, Y, Tantivess, S, et al. Multi-criteria decision analysis for setting priorities on HIV/AIDS interventions in Thailand. Health Res Policy Syst. 2012;10:6.Google Scholar
37. Dolan, JG. Patient priorities in colorectal cancer screening decisions. Health Expect. 2005;8:334–344.Google Scholar
38. Dolan, JG, Boohaker, E, Allison, J, et al. Patients' preferences and priorities regarding colorectal cancer screening. Med Decis Making. 2013;33:59–70.Google Scholar
39. Hummel, JM, Snoek, GJ, van Til, JA, van Rossum, W, Ijzerman, MJ. A multicriteria decision analysis of augmentative treatment of upper limbs in persons with tetraplegia. J Rehabil Res Dev. 2005;42:635–644.Google Scholar
40. Broekhuizen, H, Groothuis-Oudshoorn, CG, Hauber, AB, Jansen, JP, IJzerman, MJ. Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis. BMC Med Inform Decis Mak. 2015;15:102.Google Scholar
41. De Montis, A, De Toro, P, Droste-Franke, B, et al. Assessing the quality of different MCDA methods. In: Getzner, M, Spash, C, Stagl, S, eds. Alternatives for environmental evaluation. Abingdon, Oxon: Routledge; 2005.Google Scholar
42. Thokala, P, Devlin, N, Marsh, K, et al. Multiple criteria decision analysis for health care decision making–An introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value Health. 2016;19:1–13.Google Scholar
43. Keeney, RL. Common mistakes in making value trade-offs. Oper Res. 2002;50:935–945.Google Scholar