“Sapere eum aude” – Multiple-criteria decision analysis as one way to the empowerment of patients

“Sapere Aude” - dare to use your mind was one of the core ideas of the famous philosopher Immanuel Kant. Born in the time of “enlightenment” more than 280 years ago, he supported radical changes in the political system towards more freedom for citizens to think, talk and act. Whereas changes in the relationship between state and citizen were revolutionary, relationships in another area remained almost unchanged in the last centuries: the relationship between physician and patient. Many patients still treat physicians as “gods in white”. The sick and help-seeking often see themselves as depending sub-ordinates ready to receive their sovereign’s decision. Many patients are unwilling or unable to reflect about the doctors’ recommendation and decisions. However, especially in the psychiatric area this relationship begins to change. The so-called empowerment movement aims to help patients regaining influence on decisions affecting their lives (Knuf and Seibert, 2000). Ideally, subjective values of the patient and expertise of the physician are incorporated in the decision-making process.

Many authors agree on the existence of t hree different decision relationships between physician and patient : The paternalistic approach, the informed (expert) approach and the shared decision-making approach (Zaumseil, 2000; Charles, Gafni and Whelan, 1997; Charles, Gafni and Whelan, 1997).

The paternalistic approach

In this century-old, traditionalist style, the physician observes the symptoms of the patient, asks diagnostic questions and prescribes a therapy. The therapist selects one option, explains the reasons and the patient accepts or rejects the recommendation.

The expert approach

In this information-based model, the patient asks the physician for information about a specific health problem. The th erapist informs the patient about the different treatment options, who decides independently of the expert.

Shared decision-making approach

When physician and patient share information about the disease and possible therapeutic options and decide jointly, they pursue a share decision making approach. The physician provides relevant expertise regarding effects and risks of the therapies, the patient communicates his values and concerns about these options.

Opinions about the applicability of these approaches in the clinical context differ. Jungermann (1999) argues that due to the differences in expertise physicians should propose one single treatment option. This “advice giving and taking approach” has four stages:

1. The patient describes the problem and provides relevant information about symptoms and about his/her needs.

2. The physician selects one best option, suitable to solve the client’s problem. The physician thereby categorizes the client and recommends the option, which is according to his expertise best for this class of patients (categorization-and-matching strategy).

3. As next step the therapist offers the identified option as advice, justified by arguments.

4. Finally, the patient evaluates this advice based on his values and the trust in the therapist. Patients either accept or reject the advice.

Contrary to this paternalistic “advice giving and taking” approach, Zaumseil (2000) suggests to “empower” patients by sharing the decision making process. This co-operative and participative relationship expresses a respectful way of dealing with the interests and preferences of patients. Zaumseil (2000) as well describes four stages.

1. The patient and the physician share information about the disease and possible treatment options.

2. Both discuss advantages and disadvantages of different treatments and the relevance of these consequences for the patient.

3. Patient and physician decide jointly for one therapy option.

4. After having executed the treatment patient and physician may meet again to share information about the effectiveness of the therapy.

Especially in decision contexts, when a commitment of the patient to the recommended therapy is of high importance (as in medication decisions or in psychiatric contexts) this shared decision -making approach can be beneficial.

3. Decision Analysis as a method to reach informed consent In medical decision situations often considerable uncertainty about the consequences of the different treatments occurs. In addition, the patient often has multiple, conflicting objectives regarding the consequences of the therapy. Decision-analytic tools were developed to aid decision makers in these kinds of decision situations (Keeney, 1992). Decision Analysis (DA) provides methods how to deal with decision problems involving uncertainty and/or conflicting multiple objectives. If a patient needs to decide whether to agree to a surgery of an eye with blur vision, the situation involves uncertainty. With a certain probability his sight can be restored, with a certain probability his vision gets lost completely. If a woman needs to decide which pregnancy prevention method she will apply, the decision situation might involve multiple objectives. She may want to minimize the probability of getting pregnant, to minimize the price and to maximise the ease of handling.

To help decision makers in these situations involving uncertainty and/or multiple objectives, decision-analytic researchers develop a variety of decision aiding tools (Goodwin and Wright, 1998). Decision analysts thereby offer decision makers a structured framework to think and to decide. The tools serve the decision maker. Core benefit is the possibility to create new insights in complex situations and foster commitment to solutions. The proposed solution is therefore a piece of ad vice, based on the values of the decision maker, it is not a deterministic, to-be-executed

duty. Most of these methods are based on expected utility theory. The next example illustrates this theory.

Decision for an eye surgery - Creating a decision tree If a clinical decision is mainly dominated by the uncertainty of the consequences of potential therapies, a decision tree can be used to solve the problem (Clement, 1996). This approach is recommendable only if the number of therapy options is limited, the physician can provide probability assessments of therapy success and the patient is able to quantify his preferences about possible “states of health”. Consider the following example: A patient has a blur vision on both eyes. The physician diagnoses a cataract. An operation is possible, but after the surgery the patient’s visual capacity is restored or he is completely blind. In this simplified example there are only two options: “surgery” or “no surgery”. Three states of health can occur: full visual capacity, blur vision and blindness. If the patient and the physician would analyze the problem, using a decision tree, they pursue a three-step approach. Firstly, they draw a graphical representation of the situation. Secondly, the doctor assesses the relevant probabilities of success for the surgery. Finally the patient quantifies his preferences about the different states of health.

STEP 1: DRAWING A GRAPHICAL R EPRESENTATION OF THE SITUATION The following tree represents the decision situation:

Figure 1- Eye surgery decision problem

STEP 2: ASSESSING THE RELEVANT PROBABILITIES

The patient can decide not to operate and his vision will remain blur. If he decides for the surgery, his vision will either be restored or he will be completely blind. According to the past experience the physician could now determine the probabilities of the success of the operation. Let’s assume the physician estimates, a 50/50 chance for the patient to get blind or to restore his visual capacity.

p(full visual capacity/surgery) = 0.50

p(blindness/surgery) = 0.50

When the eye is not operated now, the physician is sure, that the vision will not recover nor get worse in future.

p(blur/no surgery) = 1.00

Figure 2 - Eye surgery decision problem with probabilities

STEP 3: QUANTIFYING PREFERENC ES ABOUT STATES OF HEALTH With the betting technique (Clement, 1996) the physician could ask the patient to quantify his preferences. The patient is asked to answer the following question.

Which bet would you prefer?

Option 1: Remaining blur sighted for sure or Option 2: Being blind with a probability of 0% and having fully restored vision with a probability of 100%.

The patient would surely vote for Option 2 as restored vision is certainno risk is involved. The bet is now altered in:

Option 1: Remaining blur sighted for sure or

Option 2b: Being blind with a probability of 5% and having fully restored vision with a probability of 95%.

The probabilities of the Option 2 are altered subsequently until the patient is indifferent between the two bets. Let’s assume the patient is indifferent between these options: