A prioritization model was described to aid with decision-making, ranging from Priority 1 (patients who will derive the most benefit from admission) to Priority 4 (those who will not benefit at all).
Our systematic review identified only 5 studies of which 4 evaluated decision coaching with or without a co-intervention aid (e.g., computer programs, workbook exercises, information packages), 1 evaluated an educational workshop, and none evaluated patient decision aids with decision coaching.
As the level of CAIX expression has been shown to correlate with patient's prognosis, survival, and response to IL-2 therapy [31], [32], anti-CAIX mAbs G250 and M75 also have great promise as diagnostic/prognostic tools that could aid with treatment decisions [31], [33] [36].
The decision scientist experts went through a similar process, and were asked questions relating to the design and structure of the decision aid with respect to patient decision-making.
The second study used the HK-DQI in a randomized controlled trial of a patient decision aid with patients currently facing a decision about total joint replacement.
As the audio-component adds considerable complexity to the development and cost of the Decision Aid we decided to have two intervention arms: a Decision Aid with an audio-component and a Decision Aid without an audio-component.
With the aid of decision trees, an optimal decision strategy can be developed.
Seven participants (15%) changed their screening intent after using the decision aid, with 5 deciding against screening after the decision aid and 2 changing in favor of screening.
The majority of the participants (61%) in this study reached the threshold for clear values before the decision aid with a 9% increase after using the decision aid.
Eighteen video recordings (12 with decision aid) were eligible for analyses.
These patient characteristics may interact with decision aid interventions on outcome measures.
