Shared Decision Making
Communicating evidence for shared-decision making is challenging. This process can be facilitated by the use of Decision Aids (DA), but their production is time consuming, often not based on the best available evidence or rapidly outdated. Linking the production of DA to current high-quality GRADE guidelines offers unique opportunities to overcome these limitations.
View a presentation here: Online presentation
The SHARE IT project (SHARing Evidence to Inform Treatment decisions) seeks to:
A. Develop a framework for the production of generic DA directly from recommendations in GRADE guidelines
B. To design a set of interactive and adaptable presentation formats for these DA to be used by clinicians and patients in the clinical encounter to facilitate Shared Decision Making.
C. To test the feasibility of an automated production of DA from electronically published guidelines using the MAGIC app, and display them on a wide range of devices: tablets, computer desktops, smartphones, as well as integrated in electronic medical records.
We design our DA to be used for the clinical encounter, between patients and clinicians, in contrast with DA meant to be used by patients alone, outside of the encounter. Our design is inspired by the work and model pioneered by Montori & colleagues.
Our DA prototype can display components of GRADE assessment of current evidence, including the list of patient important outcomes estimates of treatment effects, confidence in these estimates, burden of treatment, and cost . The prototype allows an interactive presentation of these elements, adapted to patients and clinicians needs, to promote a conversation about treatment alternatives in the clinical encounter.
Our framework for the development of generic DA builds on the International Patient Decision Aid Standards and on the methodology developed within the DECIDE collaboration (Developing and Evaluating Communication Strategies to Support Informed Decisions and Practice Based on Evidence).
Using an iterative process of brainstorming, stakeholder feedback, and user-testing in real clinician-patient encounters, we are developing and refining a prototype of an electronic guideline authoring tool that allows automated production of DA, linked to treatment recommendations.
Our generic DA linked to the guideline-authoring tool provide the opportunity to automatically produce and foster the uptake of DA for a wide range of diseases and thus enhance shared-decision making.