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051 Can We Automatically Produce Generic Decision Aids for the Clinical Encounter Directly from GRADE Guideline Recommendations? Experience from the Share-It Project
  1. T Agoritsas1,
  2. L Brandt2,
  3. A Anja Heen2,
  4. A Kristiansen2,
  5. P Alonso-Coello3,
  6. E Akl4,
  7. I Neumann1,
  8. K Tikkinen1,
  9. V Montor5,
  10. G Guyatt1,
  11. P Vandvik2
  1. 1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada
  2. 2Norwegian Knowledge Centre for the Health Services, Oslo, Norway
  3. 3Iberoamerican Cochrane Center (CIBERESP), Hospital de Sant Pau, Barcelona, Spain
  4. 4Department of Medicine, State University of New York City, Buffalo, USA
  5. 5Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA

Abstract

Background Although decision aids (DA) can help to communicate evidence to patients, their production is time consuming, often not based on the best available evidence or rapidly outdated. Linking trustworthy guidelines and DA for shared-decision making could both overcome these limitations and enhance guideline dissemination.

Objectives To test the feasibility of automatically translating any recommendations from GRADE guidelines into generic and interactive DA accessible on tablet computers for clinicians and their patients in the clinical encounter.

Methods As part of the DECIDE project, we developed a framework for translating components of GRADE into DA, following the International Patient Decision Aid Standards. Using a recently published guideline, we implemented that framework in our MAGIC (Making Grade the Irresistible Choice) application – a prototype electronic guideline tool and publication platform that can automatically display recommendations in multilayered presentation formats.

Results Our prototype was able to automatically translate a large number of GRADE recommendations and their supporting evidence into electronic and interactive DA. Preliminary results of user-testing in real patient-clinician interactions suggest that these DA can be used at the point of care to discuss estimates of treatment effects for patient relevant outcomes, confidence in estimates, burden of treatment, and cost issues.

Discussion This study provides a proof-of-concept that components of GRADE recommendations can be interactively displayed in generic tools for interactive shared-decision making in a wide range of treatment alternatives.

Implications for Guideline Developers/Users Our electronic DA offer promising opportunities to disseminate guidelines at the point of care.

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