Whose Preferences Matter? A Patient-Centered Approach for Eliciting Treatment Goals

Nananda F Col, Andrew J Solomon, Vicky Springmann, Calvin P Garbin, Carolina Ionete, Lori Pbert, Enrique Alvarez, Brenda Tierman, Ashli Hopson, Christen Kutz, Idanis Berrios Morales, Carolyn Griffin, Glenn Phillips, Long H Ngo, Nananda F Col, Andrew J Solomon, Vicky Springmann, Calvin P Garbin, Carolina Ionete, Lori Pbert, Enrique Alvarez, Brenda Tierman, Ashli Hopson, Christen Kutz, Idanis Berrios Morales, Carolyn Griffin, Glenn Phillips, Long H Ngo

Abstract

Background: Patients facing a high-stakes clinical decision are often confronted with an overwhelming array of options. High-quality decisions about treatment should reflect patients' preferences as well as their clinical characteristics. Preference-assessment instruments typically focus on pre-selected clinical outcomes and attributes chosen by the investigator.

Objective: We sought to develop a patient-centered approach to elicit and compare the treatment goals of patients with multiple sclerosis (MS) and healthcare providers (HCPs).

Methods: We conducted five nominal group technique (NGT) meetings to elicit and prioritize treatment goals from patients and HCPs. Five to nine participants in each group responded silently to one question about their treatment goals. Responses were shared, consolidated, and ranked to develop a prioritized list for each group. The ranked lists were combined. Goals were rated and sorted into categories. Multidimensional scaling and hierarchical cluster analysis were used to derive a visual representation, or cognitive map, of the data and to identify conceptual clusters, reflecting how frequently items were sorted into the same category.

Results: Five NGT groups yielded 34 unique patient-generated treatment goals and 31 unique HCP-generated goals. There were differences between patients and HCPs in the goals generated and how they were clustered. Patients' goals tended to focus on the impact of specific symptoms on their day-to-day lives, whereas providers' goals focused on slowing down the course of disease progression.

Conclusions: Differences between the treatment goals of patients and HCPs underscore the limitations of using HCP- or investigator-identified goals. This new adaptation of cognitive mapping is a patient-centered approach that can be used to generate and organize the outcomes and attributes for values clarification exercises while minimizing investigator bias and maximizing relevance to patients.

Keywords: clinical decision making; cognitive mapping; hierarchical cluster analysis; multidimensional scaling; multiple sclerosis; nominal group technique; preference assessment; preference sensitive care; shared decision making; values clarification.

Figures

Figure 1
Figure 1
Study design and sample.
Figure 2
Figure 2
Ratings of patients’ treatment goals. Patients v. HCPs (ranked according to differences in rating).
Figure 3
Figure 3
Patients’ cognitive map.
Figure 4
Figure 4
Health care providers’ cognitive map.

References

    1. Barry M, Edgman-Levitan S. Shared decision making—the pinnacle of patient-centered care. N Engl J Med. 2012;366:780–1.
    1. Palace J. Partnership and consent in MS treatment choice. J Neurol Sci. 2013;335:5–8.
    1. Witteman HO, Scherer LD, Gavaruzzi T, et al. Design Features of Explicit Values Clarification Methods: A Systematic Review. Med Decis Making. 2016; 36:453–71.
    1. Peters E, Dieckmann N, Dixon A, Hibbard JH, Mertz CK. Less is more in presenting quality information to consumers. Med Care Res Rev. 2007;64:169–90.
    1. Hibbard JH, Jewett JJ. What type of quality information do consumers want in a health care report card? Med Care Res Rev. 1996;53:28–47.
    1. Slovic P. The construction of preference. Am Psychol. 1995;50:64–371.
    1. Nabozny JA, Kruser JM, Steffens NM, et al. Constructing high stakes decisions: It’s better to die trying. Ann Surg. 2016;263:64–70.
    1. Fagerlin A, Pignone M, Abhyankar P, et al. Clarifying values: an updated review. BMC Med Inform Decision Mak. 2013;13(Suppl 2):S8.
    1. Stacey D, Légaré F, Col NF, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2014. (1). Art. No.: CD001431.
    1. Witteman H, Gavaruzzi T, Scherer L, et al. Effects of design features of explicit values clarification methods: A systematic review. Med Decis Making. 2016;36:760–76.
    1. Green P, Krieger A, Wind Y. Thirty years of conjoint analysis: reflections and prospects. Interfaces. 2001;31:S56–S73.
    1. Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003;2:55–64.
    1. Lenert LA, Sherbourne CD, Reyna VF. Utility elicitation using single item questions compared with a computerized interview. Med Decis Making. 2001;21:97–104.
    1. Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211:453–8.
    1. Peters E. Affect and Emotion (Chapter 10). In: Fischhoff Baruch, Brewer Noel T., Downs Julie S. eds. Communicating Risks and Benefits: An Evidence-Based User’s Guide. US Department of Health and Human Services, Food and Drug Administration, Risk Communication Advisory Committee and Consultants; 2011. Available from:
    1. Dolan JG. Patient priorities in colorectal cancer screening decisions. Health Expect. 2005;8:334–44.
    1. Lee CN, Dominik R, Levin CA, et al. Development of instruments to measure the quality of breast cancer treatment decisions. Health Expect. 2010;13:258–72.
    1. Shalowitz DI, Garrett-Mayer E, Wendler D. The accuracy of surrogate decision makers: A systematic review. Arch Intern Med. 2006;166:493–7.
    1. Seckler AB, Meier DE, Mulvihill M, Cammer Paris BE. Substituted judgment: How accurate are proxy predictions? Ann Intern Med. 1991;115:92–8.
    1. Coast J, Al-Janabi H, Sutton EJ, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. 2012;21:730–41.
    1. Marshall D, Bridges JF, Hauber B. Conjoint analysis application in health—how are studies being designed and reported? An update on current practice in the published literature between 2005 and 2008. Patient. 2010;3:249–56.
    1. Hollin IL, Young C, Hanson C, Bridges JF, Peay H. Developing a patient-centered benefit-risk survey: A community-engaged process. Value Health. 2016;19:751–7.
    1. Delbecq AL, Van de Ven AH, Gustafson DH. Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. Middleton: Green Briar Press; 1975.
    1. Potter M, Gordon S, Hamer P. The Nominal Group Technique: A useful consensus methodology in physiotherapy research. N Z J Physiother. 2004;32:126–30.
    1. Gallagher M, Hares T, Spencer J, Bradshaw C, Webb I. The nominal group technique: a research tool for general practice? Fam Pract. 1993;10:76–81.
    1. Scott D, Deadneck D. The nominal group technique applications for training needs. Train Dev. 1982:26–33.
    1. Schiffman SS, Reynolds ML, Young FW. Introduction to multidimensional scaling: theory, methods, and applications. New York: Academic Press; 1981.
    1. Fitzgerald LF, Hubert LJ. Multidimensional scaling: Some possibilities for counseling psychology. J Counsel Psychol. 1987;34:469–80.
    1. Kruskal JB, Wish M. Multidimensional Scaling. Beverly Hills: Sage Publications; 1978.
    1. Aldenderfer MS, Blashfield RK. Cluster analysis. Beverly Hills: Sage Publications; 1984.
    1. Delbecq AL, Van de Ven AH. A group process model for problem identification and program planning. J Appl Behav Sci. 1971;7:466–92.
    1. Ramagopalan SV, Sadovnick AD. Epidemiology of multiple sclerosis. Neurol Clin. 2011; 29:207.
    1. Heesen C, Solari A, Giordano A, Kasper J, Kopke S. Decisions on multiple sclerosis immunotherapy: new treatment complexities urge patient engagement. J Neurol Sci. 2011;306:192–7.
    1. Utz KS, Hoog J, Wentrup A, et al. Patient preferences for disease-modifying drugs in multiple sclerosis therapy: a choice-based conjoint analysis. Ther Adv Neurol Disord. 2014;7:263–75.
    1. Johnson FR, Van Houtven G, Özdemir S, et al. Multiple sclerosis patients—benefit-risk preferences: serious adverse event risks versus treatment efficacy. J Neurol. 2009;256:554–62.
    1. Wilson LS, Loucks A, Gipson G, et al. Patient preferences for attributes of multiple sclerosis disease-modifying therapies: development and results of a ratings-based conjoint analysis. Int J MS Care. 2015;17:74–82.
    1. Wilson L, Loucks A, Bui C, et al. Patient centered decision making: Use of conjoint analysis to determine risk–benefit trade-offs for preference sensitive treatment choices. J Neurol Sci. 2014;344:80–7.
    1. Arroyo R, Sempere AP, Ruiz-Beato E, et al. Conjoint analysis to understand preferences of patients with multiple sclerosis for disease-modifying therapy attributes in Spain: a cross-sectional observational study. BMJ Open 2017;7:e014433.
    1. Healthwise. Healthwise Helps You Make Better Health Decisions. . [Accessed 10 September, 16].
    1. Alonso A, Hernán MA. Temporal trends in the incidence of multiple sclerosis: a systematic review. Neurology. 2008;71:129.
    1. Delp P, Thesen A, Motiwalla J, Seshardi N. Systems tools for project planning. Bloomington: International Development Institute; 1977.
    1. Crenshaw K, Shewchuk RM, Qu H, et al. What should we include in a cultural competence curriculum? Acad Med. 2011;86:333–41.
    1. Dugard P, Todman J, Staines H. Approaching multivariate analysis. A practical introduction. Second Edition. New York: Routledge; 2010.
    1. Hair JF, Jr, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. Fifth Edition. New Jersey: Prentice-Hall; 1998.
    1. Lublin FD, Cofield SS, Cutter GR, et al. Randomized study combining interferon and glatiramer acetate in multiple sclerosis. Ann Neurol. 2013;73:327–40.
    1. Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol. 2013;25;13:37.
    1. Department of Veterans Affairs. Pain management. VHA Directive 2003–021. Available from: . [Accessed 27 September, 2016].
    1. Lublin FD, Reingold SC. Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology. 1996;46:907–11.
    1. Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83:278–86.
    1. Coulter A, Stilwell D, Kryworuchko J, Mullen PD, Ng CJ, van der Weijden T. A systematic development process for patient decision aids. BMC medical informatics and decision making. 2013;13(Suppl 2):S2.
    1. Wensing M, Jung H, Mainz J, Olesen F, Grol R. A systematic review of the literature on patient priorities for general practice care. Part 1: description of the research domain. Soc Sci Med. 1998;47:1573–88.
    1. Speece D. Methodological Issues in Cluster Analysis: How Clusters Become Real. Hillsdale: Erlbaum; 1990.
    1. Elwyn G, O’Connor A, Stacey D, et al. Developing a quality criteria framework for patient decision aids: Online international Delphi consensus process. BMJ. 2006;333:417.
    1. Reuben DB, Tinetti ME. Goal-oriented patient care–an alternative health outcomes paradigm. N Engl J Med. 2012;366:777–9.
    1. Kremer IE, Evers SM, Jongen PJ, van der Weijden T, van de Kolk I, Hiligsmann M. Identification and prioritization of important attributes of disease-modifying drugs in decision making among patients with multiple sclerosis: A nominal group technique and best-worst scaling. PLoS One. 2016. November 3;11(11):e0164862. doi: 10.1371/journal.pone.0164862.

Source: PubMed

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