The design of patient decision support interventions: addressing the theory-practice gap

Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory–practice gap and to examine how a...

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Published inJournal of evaluation in clinical practice Vol. 17; no. 4; pp. 565 - 574
Main Authors Elwyn, Glyn, Stiel, Mareike, Durand, Marie-Anne, Boivin, Jacky
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.08.2011
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ISSN1356-1294
1365-2753
1365-2753
DOI10.1111/j.1365-2753.2010.01517.x

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Abstract Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory–practice gap and to examine how a range of selected decision‐making theories could inform the design and evaluation of decision support interventions. Methods  We reviewed the decision‐making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory‐based outcome measures. Results  Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy‐trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational–emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory‐building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory–practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions. Discussion  Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.
AbstractList Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory–practice gap and to examine how a range of selected decision‐making theories could inform the design and evaluation of decision support interventions. Methods  We reviewed the decision‐making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory‐based outcome measures. Results  Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy‐trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational–emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory‐building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory–practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions. Discussion  Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.
Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory-practice gap and to examine how a range of selected decision-making theories could inform the design and evaluation of decision support interventions.BACKGROUNDAlthough an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory-practice gap and to examine how a range of selected decision-making theories could inform the design and evaluation of decision support interventions.We reviewed the decision-making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory-based outcome measures.METHODSWe reviewed the decision-making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory-based outcome measures.Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy-trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational-emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory-building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory-practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions.RESULTSEight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy-trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational-emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory-building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory-practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions.Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.DISCUSSIONInitiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.
Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory-practice gap and to examine how a range of selected decision-making theories could inform the design and evaluation of decision support interventions. We reviewed the decision-making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory-based outcome measures. Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy-trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational-emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory-building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory-practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions. Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.
Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue the importance of using theory to guide design. The aim of this work was to address this theory–practice gap and to examine how a range of selected decision‐making theories could inform the design and evaluation of decision support interventions. Methods  We reviewed the decision‐making literature and selected relevant theories. We assessed their key principles, theoretical pathways and predictions in order to determine how they could inform the design of two core components of decision support interventions, namely, information and deliberation components and to specify theory‐based outcome measures. Results  Eight theories were selected: (1) the expected utility theory; (2) the conflict model of decision making; (3) prospect theory; (4) fuzzy‐trace theory; (5) the differentiation and consolidation theory; (6) the ecological rationality theory; (7) the rational–emotional model of decision avoidance; and finally, (8) the Attend, React, Explain, Adapt model of affective forecasting. Some theories have strong relevance to the information design (e.g. prospect theory); some are more relevant to deliberation processes (conflict theory, differentiation theory and ecological validity). None of the theories in isolation was sufficient to inform the design of all the necessary components of decision support interventions. It was also clear that most work in theory‐building has focused on explaining or describing how humans think rather than on how tools could be designed to help humans make good decisions. It is not surprising therefore that a large theory–practice gap exists as we consider decision support for patients. There was no relevant theory that integrated all the necessary contributions to the task of making good decisions in collaborative interactions. Discussion  Initiatives such as the International Patient Decision Aids Standards Collaboration influence standards for the design of decision support interventions. However, this analysis points to the need to undertake more work in providing theoretical foundations for these interventions.
Author Durand, Marie-Anne
Boivin, Jacky
Elwyn, Glyn
Stiel, Mareike
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  surname: Elwyn
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  surname: Stiel
  fullname: Stiel, Mareike
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  givenname: Marie-Anne
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  givenname: Jacky
  surname: Boivin
  fullname: Boivin, Jacky
  organization: Professor, Cardiff Fertility Studies Research Group, School of Psychology, Cardiff University, Cardiff, UK
BackLink https://www.ncbi.nlm.nih.gov/pubmed/20698918$$D View this record in MEDLINE/PubMed
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O'Connor, A., Tugwell, P., Wells, G. A., Elmslie, T., Jolyy, E., Hollingworth, G., McPherson, R, Bunn, H, Graham, I & Drake, E. (2000) A decision aid for women considering hormone replacement therapy after menopause: decision support framework and evaluation. Patient Education and Counseling, 33, 267-279.
Evans, D. (2002) The search hypothesis of emotion. The British Journal for the Philosophy of Science, 53, 497-509.
Elwyn, G., O'Connor, A., Stacey, D., et al. (2006) The International Patient Decision Aids Standards Collaboration. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ, 333, 417-421.
Elwyn, G. & Miron-Shatz, T. (2009) Deliberation before determination: the definition and evaluation of good decision making. Health Expectations, 13, 139-147.
Elwyn, G., Frosch, D. & Rollnick, S. (2009) Dual equipoise shared decision-making: definitions for decision and behaviour support interventions. Implementation Science, 4, 75.
Reyna, V. & Brainerd, C. (1990) Fuzzy processing in transitivity development. Annals of Operations Research, 37-63.
Tversky, A. & Kahneman, D. (1981) The framing of decisions and the psychology of choice. Science, 211, 453-458.
Robinson, A. & Thomson, R. G. (2000) The potential use of decision analysis to support shared decision making in the face of uncertainty: the example of atrial fibrillation and warfarin anticoagulation. Quality in Health Care, 9, 238-244.
Bekker, H., Thornton, J. G., Airey, C. M., Connelly, J. B., Hewison, J., Robinson, M. B., Lilleyman, J., Macintosh, M., Maule, A. J. & Pearman, A. D. (1999) Informed decision making: an annotated bibliography and systematic review. Health Technology Assessment, 3 (1), 1-156.
Gigerenzer, G. & Selten, R. (2001) Bounded Rationality: The Adaptive Toolbox. Cambridge, MA: MIT Press.
Bekker, H. L., Legare, F., Stacey, D., O'Connor, A. & Lemyre, L. (2003) Is anxiety a suitable measure of decision aid effectiveness: a systematic review? Patient Education and Counseling, 50, 255-262.
Baron, J. (2000) Thinking and Deciding. Cambridge: Cambridge University Press.
Kahneman, D. (2003) A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58 (9), 697-720.
Bekker, H. L., Hewison, J. & Thornton, J. G. (2003) Understanding why decision aids work: linking process with outcome. Patient Education and Counseling, 50, 323-329.
O'Connor, A. M., Bennett, C., Stacey, D., et al. (2007) Do patient decision aids meet effectiveness criteria of the international patient decision aid standards collaboration? A systematic review and meta-analysis. Medical Decision Making, 27, 554-574.
Charles, C., Gafni, A., Whelan, T. & O'Brien, M. A. (2005) Treatment decision aids: conceptual issues and future directions. Health Expectations, 8, 114-125.
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decisions under risk. Econometrica, 47, 313-327.
Durand, M. A., Stiel, M., Boivin, J. & Elwyn, G. (2008) Where is the theory? Evaluating the theoretical frameworks described in decision support technologies. Patient Education and Counseling, 71, 125-135.
Johnson-Laird, P. N. & Shafir, E. (eds) (1994) Reasoning and Decision-Making. Cambridge: Blackwells.
Ratliff, A., Angell, M., Dow, R. W., et al. (1999) What is a good decision? Effective Clinical Practice, 2, 185-197.
Wilson, T. D. & Schooler, J. W. (1991) Thinking too much: introspection can reduce the quality of preferences and decisions. Journal of Personality and Social Psychology, 60 (2), 181-192.
Brehaut, J. C., O'Connor, A. M., Wood, T. J., Hack, T. F., Siminoff, L., Gordon, E. & Feldman-Stewart, D. (2003) Validation of a decision regret scale. Medical Decision Making, 23, 281-292.
Joseph-Williams, N., Edwards, A. & Elwyn, G. (2010) The importance and complexity of regret in the measurement of 'good' decisions: a systematic review and a content analysis of existing assessment instruments. Health Expectations (in press).
Gigerenzer, G. & Goldstein, D. G. (1996) Reasoning the Fast and Frugal Way: Models of Bounded Rationality. Psychological Review, 103, 650-669.
Nelson, W. L., Han, P. K., Fagerlin, A., Stefanek, M. & Ubel, P. A. (2007) Rethinking the objectives of decision aids: a call for conceptual clarity. Medical Decision Making, 27, 609-618.
Bowen, D. J., Allen, J. D., Vu, T., Johnson, R. E., Fryer-Edwards, K. & Hart, A. Jr (2006) Theoretical foundations for interventions designed to promote informed decision making for cancer screening. Annals of Behavioral Medicine, 32, 202-210.
Anderson, C. J. (2003) The Psychology of Doing Nothing: Forms of Decision Avoidance Result from Reason and Emotion. Psychological Bulletin, 129, 139-167.
Kahneman, D. & Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47, 263-292.
O'Connor, A. M. Llewellyn-Thomas, H. & Stacey, D. (eds) (2005) IPDAS Collaboration Background Document. Ottawa: International Patient Decision Aid Standards (IPDAS) Collaboration.
Dijksterhuis, A., Bos, M. W., Nordgren, L. F. & van Baaren, R. B. (2006) On making the right choice: the deliberation-without-attention effect. Science, 311, 1005-1007.
Janis, I. L. & Mann, L. (1977) Decision-Making: A Psychological Analysis of Conflict, Choice and Commitment. Man, the Reluctant Decision Maker. New York: The Free Press.
Wilson, T. D. & Gilbert, D. T. (2005) Affective Forecasting. Knowing What to Want. Current Directions in Psychological Science, 14, 131-134.
Boudon, R. (1991) What middle-range theories are. Contemporary Sociology, 20, 519-522.
Edwards, A., Elwyn, G. & authors, S. (eds) (2009) Decision Making in Healthcare: Achieving Evidence-Based Patient Choice, 2nd edn. Oxford: Oxford University Press.
von Neumann, J. & Morgenstern, O. (1944) Theory of Games and Economic Behaviour. Princeton, NJ: Princeton University Press.
Gigerenzer, G. & Todd, P. M. (1999) Simple Heuristics That Make Us Smart. New York: Oxford University Press.
O'Connor, A. M., Wennberg, J. E., Legare, F., Llewellyn-Thomas, H. A., Moulton, B. W., Sepucha, K. R., Sodano, A. G. & King, J. S. (2007) Toward the 'tipping point': decision aids and informed patient choice. Health Affairs, 26 (3), 716-725.
Lassiter, G., Lindberg, M., González-Vallejo, C., Bellezza, F.S. & Phillips, N. (2009) The Deliberation-Without-Attention Effect: Evidence for an Artifactual Interpretation. Psychological Science, 20, 671-675.
May, C., Finch, T., Mair, F., et al. (2007) Understanding the implementation of complex interventions in health care: the normalization process model. BMC Health Services Research, 7, 148.
Bekker, H. L., Hewison, J. & Thornton, J. G. (2004) Applying decision analysis to facilitate informed decision making about prenatal diagnosis for Down syndrome: a randomised controlled trial. Prenatal Diagnosis, 24 (4), 265-275.
Pauker, S. P. & Pauker, S. G. (1987) The amniocentesis decision: ten years of decision analytic experience. Birth Defects: Original Article Series, 23, 151-169.
Feldman-Stewart, D., Brennenstuhl, S., Brundage, M. D. & Roques, T. (2006) An explicit values clarification task: development and validation. Patient Education and Counseling, 63, 350-356.
Damasio, A. R. (1994) Descartes' Error: Emotion, Reason, and the Human Brain. London: London Putnam Publishing.
Campbell, N. C., Murray, E., Darbyshire, J., Emery, J., Farmer, A., Griffiths, F., Guthrie, B., Lester, H., Wilson, P. & Kinmonth, A. L. (2007) Designing and evaluating complex interventions to improve health care. BMJ, 334, 455-459.
Sepucha, K., Ozanne, E., Silvia, K., Partridge, A. & Mulley, A. G. (2007) An approach to measuring the quality of breast cancer decisions. Patient Education and Counseling, 65, 261-269.
O'Connor, A. M., Roston, A., Fiset, V., Tetroe, J., Entwistle, V., Llewellyn-Thomas, H., Holmes-Rovner, M., Barry, M. & Jones, J. (1999) Decision aids for patients facing health treatment or screening decisions: Systematic review. British Medical Journal, 319, 731-734.
O'Connor, A. M. (1995) Validation of a decisional conflict scale. Medical Decision Making, 15, 25-30.
Lewin, K. (1945) The research center for group dynamics at Massachusetts Institute of Technology. Sociometry, 8, 126-136.
Holmes-Rovner, M., Kroll, J., Schmitt, N., Rovner, D. R., Breer, M. L., Rothert, M. L., Padnu, G. & Talarczyk, G. (1996) Patient satisfaction with health care decisions: the satisfaction with decision scale. Medical Decision Making, 16, 58-64.
Zeelenberg, M. & Pieters, R. (2007) A theory of regret regulation 1.0. Journal of Consumer Psychology, 17, 3-18.
O'Connor, A. M., Llewellyn-Thomas, H. A. & Flood, A. B. (2004) Modifying unwarranted variations in health care: shared decision making using patient decision aids. Health Affairs, 7 October 2004 Web Exclusive, doi:10.1377/hlthaff.var.63
Evans, D. & Cruse, P. (2004) Emotion, Evolution and Rationality. Oxford: Oxford University Press.
Reyna, V. F. & Brainerd, C. J. (1991) Fuzzy-trace theory and framing effects in choice: Gist extraction, truncation, and conversion. Journal of Behavior and
1945; 8
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References_xml – reference: Bekker, H., Thornton, J. G., Airey, C. M., Connelly, J. B., Hewison, J., Robinson, M. B., Lilleyman, J., Macintosh, M., Maule, A. J. & Pearman, A. D. (1999) Informed decision making: an annotated bibliography and systematic review. Health Technology Assessment, 3 (1), 1-156.
– reference: Elwyn, G. & Miron-Shatz, T. (2009) Deliberation before determination: the definition and evaluation of good decision making. Health Expectations, 13, 139-147.
– reference: Gigerenzer, G. & Goldstein, D. G. (1996) Reasoning the Fast and Frugal Way: Models of Bounded Rationality. Psychological Review, 103, 650-669.
– reference: Elwyn, G., Frosch, D. & Rollnick, S. (2009) Dual equipoise shared decision-making: definitions for decision and behaviour support interventions. Implementation Science, 4, 75.
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– reference: Wilson, T. D. & Gilbert, D. T. (2005) Affective Forecasting. Knowing What to Want. Current Directions in Psychological Science, 14, 131-134.
– reference: O'Connor, A. M. Llewellyn-Thomas, H. & Stacey, D. (eds) (2005) IPDAS Collaboration Background Document. Ottawa: International Patient Decision Aid Standards (IPDAS) Collaboration.
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– reference: Kahneman, D. (2003) A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58 (9), 697-720.
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  start-page: 697
  issue: 9
  year: 2003
  end-page: 720
  article-title: A perspective on judgment and choice: Mapping bounded rationality
  publication-title: American Psychologist
– volume: 16
  start-page: 58
  year: 1996
  end-page: 64
  article-title: Patient satisfaction with health care decisions: the satisfaction with decision scale
  publication-title: Medical Decision Making
– year: 2005
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– volume: 80
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  year: 1992
  end-page: 168
  article-title: Differentiation and Consolidation Theory of human decision making
  publication-title: Acta Psychologica
– volume: 27
  start-page: 609
  year: 2007
  end-page: 618
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Snippet Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of...
Background  Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of...
Although an increasing number of decision support interventions for patients (including decision aids) are produced, few make explicit use of theory. We argue...
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wiley
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StartPage 565
SubjectTerms decision aids
Decision Support Techniques
decision-making theory
DESIs
Humans
Models, Theoretical
operationalization
Patient Participation
Title The design of patient decision support interventions: addressing the theory-practice gap
URI https://api.istex.fr/ark:/67375/WNG-SJ5Q90M3-P/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1365-2753.2010.01517.x
https://www.ncbi.nlm.nih.gov/pubmed/20698918
https://www.proquest.com/docview/880134652
Volume 17
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