Solving the Diagnostic Challenge: A Patient-Centered Approach

THE THRESHOLD APPROACH TO CLINICAL DECISION MAKING

In 1980, Pauker and Kassirer¹ proposed their threshold model of diagnosis. This model posits that when a physician is considering a specific disease, the action taken depends on 2 thresholds: a therapeutic (testing and treatment) threshold and a diagnostic (testing) threshold (Figure 1). Once the probability of disease rises above the therapeutic threshold, the physician stops the diagnostic process and takes action. This action may be therapy, but, depending on the context, could be other measures, such as referral.

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Figure 1

The threshold model of the diagnostic process.

Note: The figure depicts 2 examples of diagnostic decision making. The shaded area represents the state of diagnostic uncertainty. The top example shows eventual crossing of the upper boundary (therapeutic threshold), indicating ruling in of disease and start of treatment or other intervention. The bottom example shows eventual crossing of the lower boundary (diagnostic threshold), indicating ruling out of disease.

On the other hand, if the disease probability falls below the diagnostic threshold, the physician considers the disease to be absent and stops collecting data relevant for confirming or rejecting that diagnosis. As long as the disease probability lies between the diagnostic and therapeutic thresholds, further diagnostic testing is justified until the case is solved, either by crossing the therapeutic threshold upward (disease assumed to be present) or crossing the diagnostic threshold downward (disease assumed to be absent).

Factors influencing the probability levels at which the diagnostic and therapeutic thresholds are set include disease severity and the benefits and harms of tests and treatments. Other formal models to calculate thresholds have been proposed.^2–4 They have descriptive as well as normative values. The latter apply when the threshold model helps clinicians align their diagnostic practice with their own values or, better but harder, those of their patients.

Although the threshold model has an intuitive appeal, recent research from primary care shows that the explicit testing of specific hypotheses (deductive testing) may occur in less than 40% of diagnostic episodes.⁵ Strategies not directed at specific disease hypotheses are more common and capture more diagnostic cues than hypothesis testing.⁵ Not all clinical encounters end with disease probability crossing 1 of the 2 thresholds and resulting in decision. Once acute serious disease has been excluded, clinicians use expectant strategies (watchful waiting).⁶ Lastly, the threshold model is focused on the physician; the patient is a completely passive party in this view. This view contradicts empirical research showing that patients take an active part in clinical (diagnostic) decision making.^7,8

THE ECOLOGY OF THE CLINICAL PROBLEM SPACE

The threshold approach assumes that the clinical problem space is clearly bounded and mostly filled with identifiable specific diseases. This is not true in primary care, however. Here, the problem space is almost infinite and largely undifferentiated, and the prevalence of serious and specific diseases is low.⁹ For example, even among patients presenting with chest pain, only 1.5% to 3.5% have an acute coronary syndrome.¹⁰ Similarly, merely 1% of cases of abdominal pain are caused by neoplastic disease.¹¹ Pulmonary embolism, dissecting aortic aneurysm, and many other life-threatening conditions are too rare to be even quantifiable at the primary care level.¹² These probabilities are below any reasonable diagnostic threshold. In other words, if we take the threshold model seriously, we would consider most serious diseases to be excluded at the beginning of the consultation!

A common tenet of evidence-based medicine is that to rule out disease, sensitive tests are preferred (captured by the shorthand term sn-out).¹³ In low-prevalence settings, however, the likelihood of disease being present after testing, that is, the negative predictive value for disease, is invariably small. Even highly sensitive tests do not usefully modify this low probability. For instance, in a patient presenting with chest pain in primary care, the prevalence of acute coronary syndrome is approximately 2.5%.¹¹ If the patient is fairly young (younger than 65 years if female or younger than 55 years if male) and not feeling chest pressure or tightness, the likelihood drops to 0.26%. But with positive findings, such as known coronary heart disease or a request for an urgent home visit, it rises to a clinically relevant 42%, crossing the diagnostic threshold upward.¹⁴ In other words, with low prevalence, sensitive tests often are seldom informative.¹⁵

This situation conflicts with the threshold model’s implicit assumption that the probability of disease is above the diagnostic threshold and below the therapeutic threshold at the beginning of the diagnostic process. How do primary care physicians arrive at disease probabilities above the diagnostic threshold in the first place?

The challenge is even greater because the physician has to rule out a large number of potentially serious conditions. Moreover, most presentations in primary care are ambiguous, and several different explanations are possible even if only remotely. Finally, many clinically important health issues cannot be captured by conventional disease categories.

SEARCHING SPACES

What physicians actually do, especially early in the patient encounter, thus requires new descriptions. I propose that physicians search this expanded problem space first and that the patient has the leading role here.

Inductive Foraging and Triggered Routines

An analysis of 282 primary care consultations and 163 diagnostic episodes has identified a process called inductive foraging that has been shown to precede the formulation of specific hypotheses.^5,16 This process is the initial invitation to the patient to describe his orher problem. Usually, it goes far beyond the patient stating what is subsequently recorded as the presenting complaint. Patients spontaneously mention further symptoms, functional associations, and often also their own explanations or concerns. If they are allowed to do so without interference, they will lead their clinician to symptoms and problems as they perceive them, and will thus provide an exploration of the problem space.

To give some examples, a 63-year-old man with tiredness and depressed mood mentions to me his recent difficulties buttoning his shirt, thus providing a hint for early Parkinson disease. A 67-year-old retired plumber reports frequent episodes of cough in the recent past. Pondering whether to order spirometric testing, I nearly miss him mentioning that he regularly plays the tuba in the local brass band, which reassures me about his lung function.

Against the background of an almost infinite problem space, an exploration by the physician asking direct and mostly closed-ended questions is hardly realistic in generalist settings. Once the patient has been interrupted, he or she usually switches into a passive mode and answers only those questions related to problems the physician can think of. It is obvious that important and unexpected points can be missed after this kind of premature closure. This outcome is clearly shown by the introductory example about drug-induced headache. It is unlikely that the young physician would have arrived at the hypothesis of a medication adverse effect herself, or she may have arrived there only after lengthy questioning and cognitive effort. Giving the patient sufficient time for his initial description and encouraging him by active listening are thus not only a matter of kindness, but also improve the diagnostic yield and efficiency of the consultation.

After the patient has helped define the problem space, physicians explore limited areas by direct questions but without guidance by specific hypotheses, a process called a triggered routine (Figure 2). For instance, a patient reporting vomiting is asked about abdominal pain and bowel movements. The young physician questioning her patient about the characteristics of his headache is another example. Inductive foraging and triggered routines do not require defined hypotheses. Testing hypotheses too early can even be harmful because important information might be missed. As our previous research has shown,⁵ these exploratory strategies provide sufficient information so that evaluation of specific diagnostic hypotheses is needed in less than one-half of consultations. Only for this remaining minority do primary care physicians need to gather additional data guided by specific diagnostic hypotheses as suggested by the hypothetico-deductive model emerging from a seminal study by Elstein et al.¹⁷

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Figure 2

Problem space of the diagnostic task.

Note: A problem space in which the outstanding geometric forms denote unusual and relevant findings (symptoms), typically identified by inductive foraging with patient input. Arrow points to a shaded limited problem area to be explored by a triggered routine.

ADAPTIVE STRATEGIES NEEDING PATIENT INVOLVEMENT

Relevant disease cannot satisfactorily be ruled out by absence of any particular sign or even several signs, but only by a thorough inductive and collaborative search of the problem space (Figure 2). Its cornerstone is a confident patient having sufficient time and encouragement to mention all things that are different, worrisome, or both. Clinicians working in intimidating settings or interrupting their patients’ too soon during the inductive foraging phase are at a disadvantage. As the introductory example of drug-induced headache shows, such physicians will have to think of every possible explanation themselves and seek out related data, thereby potentially missing important findings or explanations. The accuracy of the diagnostic process is thus highly dependent on the quality of the clinician-patient relationship. The approach described here for initial history taking and examination may be extended into shared decision making about tests such as imaging or invasive procedures.²⁷

Human beings adapt their cognitive strategies to the environment and to the task at hand.²⁸ The physicians taking part in the seminal study of Elstein et al¹⁷ must have assumed that the case stories portrayed by actors or described on paper had a defined solution. In real life, clinicians face the alternate challenge of a potentially infinite problem space (described above) and patients with diffuse, ambiguous findings often defying any medical explanation.¹⁶ They turn to hypothetico-deductive testing only if the problem space has been narrowed sufficiently and relevant information is still missing.

The phenomenology described above does not exclude other processes relevant for medical diagnosis, such as feelings of alarm or reassurance,²⁵ rules of thumb,²⁹ or pattern recognition.^30,31 The last, which is certainly common and relevant, works well when the clinician is aware of the full range of pertinent symptoms and signs. Inductive foraging can prevent premature arrival at unjustified conclusions.¹⁶

Patient and physician collaboratively searching the problem space best describes the phenomenology of the primary care diagnostic process. (Figure 3 shows related tactics and potential pitfalls.) This model of collaborative searching accommodates criticisms brought forward against previously formulated theories, especially the threshold and hypothetico-deductive models. Although relevant data have mostly been derived from primary care, I believe that this model may apply to any clinical setting where more than 1 disease is of interest.

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Figure 3

Cognitive strategies and related tactics used in the diagnostic task.

CONCLUSION: PATIENT-CENTEREDNESS PAYS!

Patient-centeredness has been persuasively argued to be an essential feature of good family practice, most notably by McWhinney and Stewart and their colleagues.^32,33 Yet patient-centeredness is far from generally adopted, and failure to listen is one of the most common criticisms of clinician behavior.^34–36 Building an effective relationship and making a diagnosis are often seen as separate skills, but I would like to stress that these tasks are synergistic. Diagnostic efficiency is very hard to achieve without the patient’s contribution. Every diagnostic inquiry is a new collaborative adaptation within the clinical problem space. Self-interest and ultimate time saving should lead all primary care generalists to involve their patients in early shared inquiry, which I and colleagues have called inductive foraging. This exploration of how clinicians arrive at diagnoses can be summarized in several key messages that can inform primary care practice (Table 1).