Literature DB >> 12078365

Development and description of a decision analysis based decision support tool for stroke prevention in atrial fibrillation.

Richard Thomson1, Angela Robinson, Jane Greenaway, Philip Lowe.   

Abstract

BACKGROUND: There is an increasing move towards clinical decision making that engages the patient, which has led to the development and use of decision aids to support better decisions. The treatment of patients in atrial fibrillation (AF) with warfarin to prevent stroke is a decision that is sensitive to patient preferences as shown by a previous decision analysis. AIM: To develop a computerised decision support tool, building upon a previous decision analysis, which would engage individual patient preferences in reaching a shared decision on whether to take warfarin to prevent stroke.
METHODS: The development process had two main phases: (1) the development phase which employed focus groups and repeated interviews with GPs/practice nurses and patients alongside an iterative development of a computerised tool; (2) the training and testing phase in which GPs and practice nurses underwent training in the use of the tool, including the use of simulated patients. The tool was then used in a feasibility study in a small number of patients with AF to inform the design of a subsequent randomised controlled trial.
RESULTS: The prototype tool had three components: (1) derivation of an individual patient's values for relevant health states using a standard gamble; (2) presentation/discussion of a patient's risks of stroke using the Framingham equation and the benefits/risks of warfarin from a systematic literature review; and (3) decision making component incorporating the outcome of a Markov decision analysis model. Older patients could be taken through the decision analysis based computerised tool, and patients and clinicians welcomed information on risks and benefits of treatments. The tool required time and training to use. Patients' decisions in the feasibility phase did not necessarily coincide with the output of the decision analysis model, but decision conflict appeared to be reduced and both patients and GPs were satisfied with the process.
CONCLUSIONS: It is feasible to develop a decision analysis based computer software package that is acceptable to elderly patients and clinicians, but it requires time and expertise to use. It is most likely that a tool of this type will best be used by a small number of clinicians who have developed experience of its use and can maintain their skills.

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Year:  2002        PMID: 12078365      PMCID: PMC1743557          DOI: 10.1136/qhc.11.1.25

Source DB:  PubMed          Journal:  Qual Saf Health Care        ISSN: 1475-3898


  27 in total

1.  Aiding clinical decisions with decision analysis.

Authors:  M Tavakoli; H T Davies; R Thomson
Journal:  Hosp Med       Date:  1999-06

2.  Decision analysis in the selection, design and application of clinical and health services research.

Authors:  R Lilford; G Royston
Journal:  J Health Serv Res Policy       Date:  1998-07

3.  Probability of stroke: a risk profile from the Framingham Study.

Authors:  P A Wolf; R B D'Agostino; A J Belanger; W B Kannel
Journal:  Stroke       Date:  1991-03       Impact factor: 7.914

4.  Sharing decisions with patients: is the information good enough?

Authors:  A Coulter; V Entwistle; D Gilbert
Journal:  BMJ       Date:  1999-01-30

5.  The potential use of decision analysis to support shared decision making in the face of uncertainty: the example of atrial fibrillation and warfarin anticoagulation.

Authors:  A Robinson; R G Thomson
Journal:  Qual Health Care       Date:  2000-12

6.  Validation of a decisional conflict scale.

Authors:  A M O'Connor
Journal:  Med Decis Making       Date:  1995 Jan-Mar       Impact factor: 2.583

7.  Patient satisfaction with health care decisions: the satisfaction with decision scale.

Authors:  M Holmes-Rovner; J Kroll; N Schmitt; D R Rovner; M L Breer; M L Rothert; G Padonu; G Talarczyk
Journal:  Med Decis Making       Date:  1996 Jan-Mar       Impact factor: 2.583

8.  A patient decision aid regarding antithrombotic therapy for stroke prevention in atrial fibrillation: a randomized controlled trial.

Authors:  M Man-Son-Hing; A Laupacis; A M O'Connor; J Biggs; E Drake; E Yetisir; R G Hart
Journal:  JAMA       Date:  1999-08-25       Impact factor: 56.272

9.  Decision analysis and guidelines for anticoagulant therapy to prevent stroke in patients with atrial fibrillation.

Authors:  R Thomson; D Parkin; M Eccles; M Sudlow; A Robinson
Journal:  Lancet       Date:  2000-03-18       Impact factor: 79.321

10.  Development of a patient decision aid for choice of surgical treatment for breast cancer.

Authors:  Carol A. Sawka; Vivek Goel; Catherine A. Mahut; Glen A. Taylor; Elaine C. Thiel; Annette M. O'Connor; Ida Ackerman; Janet H. Burt; Elaine H. Gort
Journal:  Health Expect       Date:  1998-06       Impact factor: 3.377

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  10 in total

1.  Impact of a Multifaceted Intervention on Promoting Adherence to Screening Colonoscopy Among Persons in HIV Primary Care: A Pilot Study.

Authors:  Pansy Ferron; Shihab S Asfour; Lisa R Metsch; Michael H Antoni; Allan E Rodriguez; Robert Duncan; Sheila M Findlay
Journal:  Clin Transl Sci       Date:  2015-05-21       Impact factor: 4.689

2.  Developing an Atrial Fibrillation Guideline Support Tool (AFGuST) for shared decision making.

Authors:  Mark H Eckman; Ruth E Wise; Katherine Naylor; Lora Arduser; Gregory Y H Lip; Brett Kissela; Matthew Flaherty; Dawn Kleindorfer; Faisal Khan; Daniel P Schauer; John Kues; Alexandru Costea
Journal:  Curr Med Res Opin       Date:  2015-03-13       Impact factor: 2.580

3.  Communication and decision making in cancer care: setting research priorities for decision support/patients' decision aids.

Authors:  Amber E Barnato; Hilary A Llewellyn-Thomas; Ellen M Peters; Laura Siminoff; E Dale Collins; Michael J Barry
Journal:  Med Decis Making       Date:  2007-09-14       Impact factor: 2.583

4.  A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with atrial fibrillation: randomised controlled trial.

Authors:  Richard G Thomson; Martin P Eccles; I Nick Steen; Jane Greenaway; Lynne Stobbart; Madeleine J Murtagh; Carl R May
Journal:  Qual Saf Health Care       Date:  2007-06

5.  Qualitative methods in a randomised controlled trial: the role of an integrated qualitative process evaluation in providing evidence to discontinue the intervention in one arm of a trial of a decision support tool.

Authors:  M J Murtagh; R G Thomson; C R May; T Rapley; B R Heaven; R H Graham; E F Kaner; L Stobbart; M P Eccles
Journal:  Qual Saf Health Care       Date:  2007-06

6.  Medical communication and technology: a video-based process study of the use of decision aids in primary care consultations.

Authors:  Eileen Kaner; Ben Heaven; Tim Rapley; Madeleine Murtagh; Ruth Graham; Richard Thomson; Carl May
Journal:  BMC Med Inform Decis Mak       Date:  2007-01-10       Impact factor: 2.796

Review 7.  Health state descriptions to elicit stroke values: do they reflect patient experience of stroke?

Authors:  Joanne Gray; Mabel L S Lie; Madeleine J Murtagh; Gary A Ford; Peter McMeekin; Richard G Thomson
Journal:  BMC Health Serv Res       Date:  2014-11-21       Impact factor: 2.655

Review 8.  Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges.

Authors:  Ruth Masterson Creber; Meghan Reading Turchioe
Journal:  Card Electrophysiol Clin       Date:  2021-07-02

Review 9.  A review of decision support, risk communication and patient information tools for thrombolytic treatment in acute stroke: lessons for tool developers.

Authors:  Darren Flynn; Gary A Ford; Lynne Stobbart; Helen Rodgers; Madeleine J Murtagh; Richard G Thomson
Journal:  BMC Health Serv Res       Date:  2013-06-18       Impact factor: 2.655

Review 10.  Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses.

Authors:  Ania Syrowatka; Dörthe Krömker; Ari N Meguerditchian; Robyn Tamblyn
Journal:  J Med Internet Res       Date:  2016-01-26       Impact factor: 5.428

  10 in total

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