Article type
Year
Abstract
Background: Although decision aids help communicate evidence to patients, their production is time consuming and often not based on the best available evidence. Linking decision aids to credible, current recommendations, such as those using the GRADE framework (Grading of Recommendations Assessment, Development and Evaluation), could both overcome these limitations and enhance dissemination of the evidence at the point of care.
Objectives: To test the feasibility of automatically translating evidence summaries from systematic reviews into generic and interactive decision aids accessible on tablet computers for clinicians and patients in the clinical encounter.
Methods: As part of the DECIDE project (http://www.decide-collaboration.eu/), we developed a framework consistent with the International Patient Decision Aid Standards for translating evidence summaries from systematic reviews using the GRADE framework into decision aids. Using recently published evidence profiles, we implemented this framework in the MAGIC (MAking Grade the Irresistible Choice) application—a prototype electronic guideline authoring tool and publication platform, developed by our group, that can automatically display recommendations in multilayered formats. We are refining the presentation formats for the decision aids using an iterative process of brainstorming, stakeholder feedback, and user-testing in real clinician-patient encounters.
Results: Our prototype can automatically translate a large number of GRADE recommendations and their supporting evidence summaries into electronic and interactive decision aids. Preliminary results of user-testing in real patient-clinician interactions suggest that these tools can be used at the point of care to facilitate communication of estimates of treatment effects, confidence in those estimates, and burden of treatment, resulting in decisions consistent with patients’ values and preferences.
Conclusions: This study provides a proof-of-concept that evidence summaries using the GRADE framework can be automatically translated into interactive decision aids for the clinician encounter. These tools offer a potentially revolutionary method for enhancing shared decision-making using best current evidence from systematic reviews.
Objectives: To test the feasibility of automatically translating evidence summaries from systematic reviews into generic and interactive decision aids accessible on tablet computers for clinicians and patients in the clinical encounter.
Methods: As part of the DECIDE project (http://www.decide-collaboration.eu/), we developed a framework consistent with the International Patient Decision Aid Standards for translating evidence summaries from systematic reviews using the GRADE framework into decision aids. Using recently published evidence profiles, we implemented this framework in the MAGIC (MAking Grade the Irresistible Choice) application—a prototype electronic guideline authoring tool and publication platform, developed by our group, that can automatically display recommendations in multilayered formats. We are refining the presentation formats for the decision aids using an iterative process of brainstorming, stakeholder feedback, and user-testing in real clinician-patient encounters.
Results: Our prototype can automatically translate a large number of GRADE recommendations and their supporting evidence summaries into electronic and interactive decision aids. Preliminary results of user-testing in real patient-clinician interactions suggest that these tools can be used at the point of care to facilitate communication of estimates of treatment effects, confidence in those estimates, and burden of treatment, resulting in decisions consistent with patients’ values and preferences.
Conclusions: This study provides a proof-of-concept that evidence summaries using the GRADE framework can be automatically translated into interactive decision aids for the clinician encounter. These tools offer a potentially revolutionary method for enhancing shared decision-making using best current evidence from systematic reviews.