Literature DB >> 20698918

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

Glyn Elwyn1, Mareike Stiel, Marie-Anne Durand, Jacky Boivin.   

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.
© 2010 Blackwell Publishing Ltd.

Entities:  

Mesh:

Year:  2010        PMID: 20698918     DOI: 10.1111/j.1365-2753.2010.01517.x

Source DB:  PubMed          Journal:  J Eval Clin Pract        ISSN: 1356-1294            Impact factor:   2.431


  30 in total

1.  Affective forecasting and medication decision making in breast-cancer prevention.

Authors:  Michael Hoerger; Laura D Scherer; Angela Fagerlin
Journal:  Health Psychol       Date:  2016-02-11       Impact factor: 4.267

2.  Personalizing Second-Line Type 2 Diabetes Treatment Selection: Combining Network Meta-analysis, Individualized Risk, and Patient Preferences for Unified Decision Support.

Authors:  Sung Eun Choi; Seth A Berkowitz; John S Yudkin; Huseyin Naci; Sanjay Basu
Journal:  Med Decis Making       Date:  2019-02-15       Impact factor: 2.583

3.  Coping strategies and immune neglect in affective forecasting: Direct evidence and key moderators.

Authors:  Michael Hoerger
Journal:  Judgm Decis Mak       Date:  2012-01-01

4.  Decision influences and aftermath: parents, stillbirth and autopsy.

Authors:  Dell Horey; Vicki Flenady; Liz Conway; Emma McLeod; Teck Yee Khong
Journal:  Health Expect       Date:  2012-06-19       Impact factor: 3.377

5.  Shared mind: communication, decision making, and autonomy in serious illness.

Authors:  Ronald M Epstein; Richard L Street
Journal:  Ann Fam Med       Date:  2011 Sep-Oct       Impact factor: 5.166

Review 6.  Communicating Uncertainty in Benefits and Harms: A Review of Patient Decision Support Interventions.

Authors:  Nick Bansback; Madelaine Bell; Luke Spooner; Alysa Pompeo; Paul K J Han; Mark Harrison
Journal:  Patient       Date:  2017-06       Impact factor: 3.883

7.  Toward theoretical understanding of the fertility preservation decision-making process: examining information processing among young women with cancer.

Authors:  Patricia E Hershberger; Lorna Finnegan; Susan Altfeld; Sara Lake; Jennifer Hirshfeld-Cytron
Journal:  Res Theory Nurs Pract       Date:  2013       Impact factor: 0.688

8.  What is a good medical decision? A research agenda guided by perspectives from multiple stakeholders.

Authors:  Jada G Hamilton; Sarah E Lillie; Dana L Alden; Laura Scherer; Megan Oser; Christine Rini; Miho Tanaka; John Baleix; Mikki Brewster; Simon Craddock Lee; Mary K Goldstein; Robert M Jacobson; Ronald E Myers; Brian J Zikmund-Fisher; Erika A Waters
Journal:  J Behav Med       Date:  2016-08-26

9.  Use of an Online Breast Cancer Risk Assessment and Patient Decision Aid in Primary Care Practices.

Authors:  Karen B Eden; Ilya Ivlev; Katherine L Bensching; Gabriel Franta; Alyssa R Hersh; James Case; Rongwei Fu; Heidi D Nelson
Journal:  J Womens Health (Larchmt)       Date:  2020-03-10       Impact factor: 2.681

10.  Mammography Decision Aid Reduces Decisional Conflict for Women in Their Forties Considering Screening.

Authors:  Karen B Eden; Paula Scariati; Krystal Klein; Lindsey Watson; Mark Remiker; Michelle Hribar; Vanessa Forro; LeAnn Michaels; Heidi D Nelson
Journal:  J Womens Health (Larchmt)       Date:  2015-09-11       Impact factor: 2.681

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.