Glenn Philip Salkeld*, The University of Sydney, Sydney, Australia Sally Wortley, University of Sydney, Sydney, Australia David Hunter, University of Sydney, Sydney, Australia Hemalatha Umapathy, University of Sydney, Sydney, Australia Track: Practice Presentation Topic: Online decision technology Presentation Type: Poster presentation Submission Type: Single Presentation Last modified: 2014-06-04

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Background

Decision making for osteoarthritis is complex and this can impact on management decisions in a number of ways. For example there are marked variations in overweight/ obesity, tolerance for physical activity, risk for anti-inflammatory adverse events including both gastrointestinal and cardiovascular toxicity, and frequent concomitant comorbidities including depression, hypertension and/or diabetes. All of these factors can influence informed osteoarthritis management decisions. This led us to develop an online decision aid as a means of combining the best available evidence on the benefits and harms of osteoarthritis management and patient preferences to provide an opinion on which option may be best for them.

Decision app

The decision aid uses a generic web-based decision-support template grounded in multi-criteria decision analysis (MCDA). The app, known as Annalisa© (AL), uses a simple expected value algorithm to calculate a score for each option by taking into account the individual's preferences for different criteria (as importance weights) and the evidence of the performance of each option on each criterion.

The app allows for the dual personalisation of the decision in terms of both the clinical characteristics of the patient and their preferences in relation to the benefits and harms associated with the alternative treatment options. It incorporates evidence on both the benefits and the potential harms of a range of OA management options (the ‘attributes’) from published evidence based guideline, tailoring these as closely to the specific patient as possible by information elicited about the patient. By combining this evidence with the individual's importance weights for the various outcomes (elicited in a graphical way at the point of decision), the best course of action for each patient will be identified on the basis of quantified scores for each option. This poster will present a summary of the systematic review of the literature on both qualitative and quantitative studies reporting on treatment preferences of patients with osteoarthritis. It will also present graphically the work in progress design and content of the osteoarthritis decision app.