SDM, as opposed to more traditional authoritative and paternalistic models of the patient-practitioner relationship, requires information exchange between the clinician and the patient, who then deliberate together and decide on the optimal treatment option [11]. Researchers have recognized the need to temper the role of so-called “medical opinion” and enhance the role of patient preference in medical decision making [9, 11, 17]. Some investigators [6, 7] have noted that although some patients want complete control over their healthcare decision making and others want physicians to make all treatment decisions, most would prefer to share the decision making with their physicians. Hawker et al. [9] have shown professional opinion concerning a patient’s need for TJA can differ from the patient’s preferences.

Although physicians often have superior knowledge regarding the pathophysiology of a patient’s disease and the risks and benefits of specific treatment options, they lack information regarding individual patient preferences important in formulating a treatment plan for a particular patient. Expected-value decision analysis models can be used to supplement more traditional shared decision-making tools (eg, DVDs and booklets describing treatment alternatives) by incorporating the best available evidence-based outcome data with individual patient preferences, as measured from direct preference assessment. The purpose of an expected-value decision analysis model is to help patients and clinicians choose between two or more treatment alternatives, each of which can lead to several possible outcomes, with chance determining the outcome experienced by an individual patient (Fig. 1).

Health state utility, which is preference-based measure of health status ranging from 1.0 (perfect health) to 0.0 (death), refers to the desirability or preference that individuals or societies have for a given health outcome [16]. Utility scores also can be used to weigh time spent in each health state to estimate quality-adjusted life years (QALYs) gained, which then can be used as the denominator in cost-utility analyses and cost-effectiveness analyses. The three most commonly used approaches for direct assessment of utility are the time trade-off (TTO), standard-gamble, and visual analog scale (also referred to as rating scale) techniques [21]. The TTO technique (Fig. 2) derives values for various health states by asking patients how many years in their current state of health they would be willing to give up to live a fixed number of years in excellent health [8]. Another technique for direct preference assessment is the standard-gamble technique (Fig. 3), which involves offering the patient two treatment alternatives. Alternative 1 is a treatment with two possible outcomes: either the patient returns to normal health and lives for an additional finite number of years (probability p) or the patient dies immediately. Alternative 2 has the certain outcome of a chronic state of illness or disability (i) for a finite period of time. The probability (p) is varied until the subject is indifferent between the two alternatives, at which point the value for chronic state i is set equal to p [15]. Finally, the visual analog scale (or rating scale) technique involves asking patients to simply locate various health states on a linear scale from 0 to 1 [15]. Although it has been suggested this technique is the simplest to administer and easiest for patients to understand, one drawback of the technique is the tendency for most patients to rate their health as “average” versus the health of their peers and thus to center-bias the utility assessments [20]. However, the visual analog scale technique is valid and reproducible for measuring utility in patients with OA and TJA [3].