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 in | Journal of evaluation in clinical practice Vol. 17; no. 4; pp. 565 - 574 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.08.2011
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Subjects | |
Online Access | Get full text |
ISSN | 1356-1294 1365-2753 1365-2753 |
DOI | 10.1111/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. |
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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 |
Author_xml | – sequence: 1 givenname: Glyn surname: Elwyn fullname: Elwyn, Glyn email: elwyng@cardiff.ac.uk organization: Professor – sequence: 2 givenname: Mareike surname: Stiel fullname: Stiel, Mareike organization: Doctoral Candidate – sequence: 3 givenname: Marie-Anne surname: Durand fullname: Durand, Marie-Anne organization: Research Associate, Clinical Epidemiology Interdisciplinary Research Group, Department of Primary Care and Public Health, School of Medicine, Cardiff University, Heath Park, Cardiff, UK – sequence: 4 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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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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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 |
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