TJA decreases pain and improves function and quality of life for patients with disabling osteoarthritis (OA) of the hip and knee [19
]. However, some investigators have documented substantial geographic variation in TJA procedure rates and use of premium TJA implants (eg, hard-on-hard bearings) that cannot be explained by differences in population characteristics alone [4
]. Researchers from the Dartmouth Center for Evaluative Clinical Sciences [13
] have suggested one potentially helpful tool to address geographic variation in practice patterns would be an increased emphasis on informing patients, eliciting their preferences, and involving them in the choice of treatment. Others have noted an important prerequisite for successful shared decision-making (SDM) is gathering and synthesizing evidence-based outcome data with which to educate patients so that their choices are as informed as possible [1
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
]. Some investigators [6
] 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. ).
Fig. 1 A simple decision tree for THA versus nonoperative care is shown. The square indicates a decision node representing a choice between two or more treatments being considered (eg, THA and nonoperative care). The circles are chance nodes, reflecting possible (more ...)
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. ) 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. ), 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
Fig. 2 A graph illustrates the TTO technique for direct preference assessment. This technique 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 (more ...)
Fig. 3 A flowchart illustrates the standard-gamble technique for direct preference assessment. This technique involves offering the patient two treatment alternatives. In this example, Alternative 1 is a treatment with two possible outcomes: either the patient (more ...)
In addition to considering individual patient preferences for various health states and downstream consequences associated with their care, decision aids should also incorporate patient preferences and values regarding the cost of care. As consumer-focused health plans that require patients to assume a larger burden of healthcare costs have increased in popularity in the United States, cost considerations have become increasingly important for individual patients when seeking the best value for their healthcare dollars. Thus, SDM tools will need to incorporate not only patient preferences regarding individual health states and health outcomes but also information regarding their willingness to pay for a particular treatment intervention or desired health state.