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Essay  |  Dermatol Pract Concept 2012;2(4):12 47

Introduction 

The process of making a diagnosis is a process of problem-

solving during which the diagnostician notices various attri-

butes of a patient, recognizes associations between the attri-

butes, and applies a classification—the disease process that 

can then be treated, hopefully to good result for the patient. 

But at the heart of it, “making a diagnosis” is a very human 

endeavor; “to classify is human.” Our training helps us to be 

systematic in applying the label that we call a diagnosis to a 

patient, but the process of classification is something that we 

are born to do. Problem-solving necessarily involves a sense 

of uncertainty. If one knew the answer ahead of time, one 

would not have “a problem” to begin with.

As we all know, there is a very large amount of poten-

tial data out there and it can be difficult, especially with the 

time constraints we face, to figure out which attributes of 

the patient we should look for (what data we will need to 

consider). Also, although we do not like to admit it, we know 

our cognitive abilities are somehow limited, throwing yet 

another chink into the process.

Basically, this essay is an inquiry into how a finite mind 

can work efficiently and with purpose in what is for practical 

purposes a world with infinite data. We will examine various 

types of uncertainty and consider the implications of each 

type with respect to the task at hand. We will look at the 

various elements that make up the problem-solving process 

of arriving at a diagnosis and see how those elements can 

interact with each other, sometimes with surprising results. 

Inherent in the concept of diagnosis is that illness is involved. 

Somehow, whatever state we are in, if we can be better, we 

must be ill now. So to consider the problem of making a 

diagnosis, we should consider the concept of health—can 

we identify readily “health” and “illness” and differentiate 

between the two?

Recognizing health and illness

All of us physicians likely think we have a pretty good idea 

of “health.” Webster defines “health” as “the condition of an 

organism or one of its parts in which it performs its vital 

functions normally or properly”; also “flourishing condi-

tion.” Before I go too much further, please recall the dis-

cussions in earlier essays in this series about the nature of 

definition, both ostensive (definition by showing) and verbal, 

(Interpretation) [1] and the (unavoidable) ambiguity of lan-

guage (Language) [2].

Most attributes, especially attributes of disease, are not 

readily observed by us. Parenthetically, there is a discussion 

about problems of taxonomy later in this essay. Imagine 

you are in the waiting room of a clinic. You notice men and 

women, girls and boys, some sit alone, others seem to be in 

small groups. You notice an older gentleman sitting and talk-

ing to a middle-aged gentleman. You imagine a (healthy) son 

has accompanied his father to the appointment. Surely the 

older man is ill, since the likelihood of illness increases with 

age. Then a name is called. The younger man stands and fol-

lows the office staff in to see the doctor. In fact, the younger 

On the nature of thought processes and their 
relationship to the accumulation of knowledge, 

Part XVI—The process of making a diagnosis
Cris Anderson, M.D.1

1 Southern Illinois University School of Medicine, Springfield, IL, USA

Citation: Anderson C. On the nature of thought processes and their relationship to the accumulation of knowledge, Part XVI—The process 
of making a diagnosis. Dermatol Pract Conc. 2012;2(4):12. http://dx.doi.org/10.5826/dpc.0204a12.

Copyright: ©2012 Anderson This is an open-access article distributed under the terms of the Creative Commons Attribution License, which 
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Corresponding author: Cris Anderson, M.D. Email: crisj@me.com.



48 Essay  |  Dermatol Pract Concept 2012;2(4):12

man is ill and his father has accompanied him, providing 

moral support.

Now imagine you are downtown shopping. Many people 

are on the street, going into and coming out of stores. Who 

has a kidney transplant? Who takes medication to treat high 

blood pressure? Who has high cholesterol?

Most of the attributes we use to identify illness are not 

directly visible. Even people who have diseases of the skin 

generally wear clothing that prevents others from noticing 

a lesion.

Basically, we must decide which attribute is important to 

notice and how we might best notice the attribute. In medicine, 

the vast majority of attributes are noticed by indirect means. 

We order a blood test or we do a biopsy and stain the fixed 

tissue to highlight a potential attribute to best advantage.

In addition to the fact that attributes associated with dis-

ease may not be readily visible, members of the population, 

including providers of health care, do not agree on whether 

an attribute identified is actually within the spectrum of 

“health” or “disease.” Consider the following scenarios. 

First, when I was in medical school on a rheumatology rota-

tion my mentor described a situation in which a patient of 

his, a young man, presented complaining of back pain when 

he lifted heavy weights as part of his exercise program. My 

mentor worked him up and found he had a spondylolisthesis 

of L4-L5. My mentor explained the situation to him, that it 

was a congenital condition that would require back surgery 

to “fix,” but that if the patient would not lift such heavy 

weights he would not have the back pain to begin with and 

would not require surgery. In fact, if the patient had not been 

lifting such heavy weights, he may have gotten through his 

life without ever knowing about his condition. The patient, 

however, insisted that the surgery be performed because 

he wanted to pursue a career in government service that 

required an extraordinary degree of physical conditioning. 

The surgery was performed and, as far as my mentor knew, 

the patient did well and gained employment in the job of 

his dreams. But was the patient ill? How many of us can 

perform extraordinary physical feats, say on a par with an 

olympic athlete? Are we unhealthy?

And what about Latisse, the glaucoma drug bimatoprost 

marketed as promotion for its side effect of causing longer 

thicker eyelashes? Are people with puny eyelashes necessar-

ily ill?

Should every human being on the face of Planet Earth 

take a statin drug to lower cholesterol—this seems to be 

what “Big Pharma,” large pharmaceutical companies, would 

like us to believe.

Consider the other end of the spectrum. Ross Upshur, in 

“Looking for Rules in a World of Exceptions” [3], writes the 

following about one of his patients, “Consider the following 

patient. Mrs G. is 82 years old. When I assumed her care six 

years ago, she was given a prognosis of six months to live 

from severe congestive heart failure. Mrs. G has lived beyond 

her original six-month prognosis. Would one consider her in 

good health? I don’t know. To consider her healthy is not in 

any way correct. To call her unhealthy is also seemingly inap-

propriate. I believe she is in equilibrium. [Upshur catalogs 

Mrs. G’s experiences with (both the diseases themselves and 

the reaction to her by healthcare providers) endometrial ade-

nocarcinoma, flash pulmonary edema, aortic valve replace-

ment, and type 2 diabetes mellitus] . . . she now requires a 

complex regimen of medications [including] diuretics, anti-

hypertensives, cardiac medications, cholesterol-lowering 

medications, and diabetic medications . . . the critical deter-

minant of why Mrs. G is still alive today . . . [has less to do 

with evidence-based therapy and more] to do with the fact 

that she has an adult son who is developmentally delayed 

and is absolutely dependent on her for emotional and psy-

chological survival.”

Is a person who has never had a complaint of ill health 

and who feels well an hour before s/he drops dead from a 

heart attack healthy? The point I wish to make is that the 

same definition of health does not apply in every situation. A 

determination of health depends very much on context and 

perspective. The context and perspective of the patient, prac-

titioner, and society all interweave to decide who is healthy 

and who is ill.

Before we proceed to the process of making a diagnosis, 

we will start with an ideal situation to which we can com-

pare our reality.

The ideal—by which we are able to 
make a correct diagnosis every time

Basically, we require three things to be able to make a diag-

nosis: 1) a knowledge base, 2) a set of reasoning skills, and 3) 

the ability to obtain specific data in the case of an individual 

patient.

Ideally, every disease would be fully understood and 

specific criteria would be defined to allow diagnosis of that, 

and only that disease. Additionally, no diseases would share 

identical criteria—there would always be at least one attri-

bute that would differentiate between similar diseases (that 

is to say, there would not be two or more names for the 

same disease). Second, the criteria used to define the disease 

would be unambiguous. That is to say, the same observer 

would make the same assessment of each attribute every 

time (intra-observer consistency) and different observers 

would all make the same assessment of the same attribute 

(inter-observer consistency).

Furthermore, every diagnostician would have access to 

the entire knowledge base and the knowledge base itself 



Essay  |  Dermatol Pract Concept 2012;2(4):12 49

would be internally consistent (all “facts” would be satisfi-

able with all other “facts”—there would be no contradic-

tions or paradoxes arising in head-to-head consideration of 

different “facts”).

Also, in conjunction with the two previous paragraphs, 

specific and parsimonious criteria would be defined for each 

disease and these criteria would be agreed upon by all to be 

sufficient to make a specific diagnosis such that disease A, 

and only disease A, is defined by the chosen subset of data 

selected from all of the data known about disease A. This 

item addresses the topic in information theory of “data com-

pression” and relates to the desirability of making a diagno-

sis with a minimum of ancillary testing (in order to save time 

and money within the health care system).

Additionally, the inner workings of each diagnostician 

would be logically consistent. Each diagnostician could 

assume confidently that, as each attribute of the patient is 

learned, all relevant data from memory would “pop” into 

mind and allow him/her to follow the algorithm of the pro-

cess of diagnosis to the diagnosis, which would be correct 
in every case.

Obviously, in the system as a whole, we are nowhere near 

to our ideal and, in fact, we never can be since the universe, as 

discussed in earlier essays in this series, is non-deterministic.

Types of uncertainty

There are multiple types of uncertainty and each type has 

different implications for us in our task of making a diagno-

sis. Two types of uncertainty are trivial. First, we might have 

known some fact and forgotten it. Second, perhaps we have 

not learned a fact yet, but some one else knows. For these two 

types of uncertainty, all we must do is look up the answer.

A third type of uncertainty is real and unresolvable. It 

arises from the necessity of learning about populations by 

sampling. As we all know, when a new ancillary test comes 

out to aid in the diagnosis of a disease, it is assigned a “sensi-

tivity” and “specificity” based on the performance of the test 

in the original study population. As we will discuss later 
in this essay, the attributes of the patient sitting before us 

do not match exactly the attributes of the study population. 

In fact, no two people share identical attributes (not even 

identical twins).

To confound the issue even further, the new test is com-

pared to a “gold standard” test—ideally a test the results of 

which differentiate perfectly all people who have the disease 

in question from all people who do not have the disease. Of 

course, we realize that no existing “gold standard” is ideal.

Our entire system of making diagnoses and utilizing 

ancillary testing is rooted in the concept that someone 
knows, somehow, who really has a disease and who does 
not. This is simply not true. All we have is a group of people; 

each person of the group has numerous attributes (some of 

which they share in common with other members of the 

group and some of which they do not share). We hope we 

have understood causally the disease in question well enough 

that a core subset of attributes is shared by group members 

with the disease and that a similar, but not identical, core 

subset of attributes is shared by the group members without 

the disease. We then ask the question, “does this new test 

differentiate reliably between the two groups?” Can we use 

this new test to diagnose reliably a new patient who is not a 

member of the original study group?

Since, by definition, we will use the new test on a patient 

outside the original test group, uncertainty related to the 

incomplete overlap of attributes necessarily introduces 

uncertainty into the diagnosis of every patient we see.

This type of uncertainty can be lessened somewhat by 

ensuring that relevant attributes of the study population are 

well-known and that the patient on whom we are using the 

test actually shares the relevant attributes.

A fourth type of uncertainty is related to the philosophi-

cal concept of “vagueness.” Just where do we draw the line? 

In an earlier essay in this series we discussed the work of Bart 

Kosko, in Fuzzy Thinking [4], about assigning in a dichoto-
mous manner attributes that actually occur on a continuum. 

In his example of apples, if we have a hundred apples and 

try to put them into two groups, one of red apples and one 

of green apples, the color of some apples will be clearly over-

whelmingly red or green, but many apples will have both 

colors and will be more difficult to assign.

Examples of this “vagueness” type of uncertainty are 

encountered in the practice of medicine on a daily basis. For 

patients with shortness of breath, one must consider whether 

the problem is more likely to be cardiac or pulmonary in 

origin. We have available in our diagnostic armamentarium 

B-type natriuretic peptide (BNP), which is released when the 

heart muscle is stretched during heart failure. Using BNP as 

a diagnostic aid works great, doesn’t it? If the patient is short 

of breath and his BNP is less than 100 units, the patient can 

be safely classified as a pulmonary patient. If the patient is 

short of breath and her BNP is more than 500 units, the 

patient can be safely classified as a cardiac patient. Pretty 

nifty! But what about the patient who is short of breath and 

has a BNP of 300 units (half way between 100 and 500)? 

In this case we simply cannot use this test to make our deci-

sion—we must search for other attributes that will help us. 

And similar examples abound in our daily practice.

The uncertainty of “vagueness,” however, can be tamed 

somewhat by defining more carefully the context of the 

patient. All we must do is identify other attributes that 

alter the context of the patient and make some of the attri-

butes we have identified more helpful—more dichotomous 

towards making a decision. In the new context, attribute A 



50 Essay  |  Dermatol Pract Concept 2012;2(4):12

now argues unequivocally either for or against a diagnostic 

possibility. More about context later.

Another type of uncertainty is that which arises in the 

context of experience (or lack thereof) and is the result of 

making decisions based on “explicit” or “implicit” knowl-

edge. Luchins [5] avers that “explicit” knowledge is analo-

gous to reading written directions to perform some action, 

while “implicit” knowledge is analogous to how the experi-

ence of actually performing the written instructions changes 

how one performs the action over time (with practice). 

An expert (one who has performed many times the action 

described in the written directions), for example, executes 

the written directions differently from someone who is fol-

lowing the instructions for the first time. The expert, via feed-

back gleaned while watching interim events during his/her 

multiple attempts performing the task, alters slightly his/her 

interpretation of the instructions and performs the task dif-

ferently, paying particular attention to one facet or another 

along the way. Importantly, the results of this feedback are 

not usually written into a new version of the instructions. In 

fact, because of the ambiguity of language (as discussed in 

the essay in this series on Language), it is probably not even 

possible to write reliably implicit knowledge into written 

instructions. Each performer of the task learns nuances from 

repeated performance and over time, his/her performance 

improves with continued iterations (the so-called “learning 

curve”). This fifth type of uncertainty, then is the difference 

between the written instructions themselves and the unwrit-

ten “value added” to the performance resulting from experi-

ence. See below the discussion of heuristics.

Yet another source of uncertainty arises from the the fact 

that all testing is indirect. We often know what we want to 

know, but we cannot look for it directly. We perform a test 

and from the results of that test we make inferences about 

what we really want to know. For example, we often want 

to know how well the tissues of a patient are oxygenating. 

Tests that are used to assess this include a hemoglobin or 

hematocrit to assess oxygen carrying capacity and the partial 

pressure of oxygen in the blood. When a patient is relatively 

healthy, that is to say when most of the patient’s physiologic 
systems are working well, inferences made from indirect 

evidence work admirably. Suppose a patient comes to the 

doctor’s office complaining of shortness of breath on exer-

tion. If we find that the patient has a lower than normal 

hemoglobin/hematocrit, we likely assume that is the reason 

for the symptoms, prescribe an appropriate hematinic agent, 

and have the patient return in a few weeks to see if the symp-

toms have improved and the hemoglobin/hematocrit have 

returned to the normal range. But many of us have likely 

stood at the bedside of a gravely ill patient with advanced 

sepsis syndrome. The skin appears dusky or pasty or gray 

and the hemoglobin is a little low (not low enough to explain 

the clinical appearance) and the pressure of oxygen is likely 

adequate. We have obtained our tests to try and see how 

well the patient is oxygenating, but his appearance itself tells 

us—he is not doing well. In fact, there is no ancillary test 
that truly assesses directly how oxygen is being utilized at 

the cellular level.

And this problem is repeated throughout our practices, 

day in and day out. Radiology sees “shadows,” not tumors; 

levels of any analyte obtained from a blood sample test the 

level on a “well-mixed” sample (from a fairly large periph-

eral vein or artery), making it more difficult to assess a focal 

process. Additionally, a sample is analyzed in such a way 

that the analyte usually reacts in a “test system,” making the 

analyte more visible (perhaps with a monoclonal antibody 

and/or a chromogen). Even when we look histologically 

at structure, we see artifact induced by us—the process of 

biopsy wrenches the tissue from the rest of the body thereby 

severing its ability to receive messages that direct its func-

tion, furthermore we place it in fixative to ensure it does 

not deteriorate, and then we slice it thinly and stain it in a 

variety of ways, using chemical properties to view indirectly 

one facet or another. Eosinophils, for example are named 

after their staining properties, not after any sort of function 

they may have.

Even our own senses “process” raw data and present it 

to our brains in a different format than received at the recep-

tors of the energy we sense. Then we “recognize” the data 

after our brain has processed it and sent a conclusion to our 

consciousness somewhere.

We can “work around” the uncertainty arising as a result 

of vagaries associated with the “indirectness” of testing by 

performing an additional (indirect) test that examines a dif-

ferent aspect of the problem, thereby obtaining “convergent” 

evidence. Convergent evidence comes about when results of 

tests looking at a problem from different perspectives all 

support the same hypothesis.

Another type of uncertainty is a type we can only know 

in hindsight—the so-called “unknown unknowns.” For this 

type of uncertainty we may not even know what questions 

to ask or how to ask a question. An example of this type 

of uncertainty occurred relative to subclassifying types of 

leukemia. Circa 1970 or so, Acute Lymphoblastic Leukemia 

(ALL) was known and treatable; most children fared pretty 

well, but a small percentage did not respond as expected to 

therapy. At the time, the diagnosis was made by observing 

cells from bone marrow or peripheral blood smeared on a 

glass slide stained with Wright-Giemsa stain. Shortly there-

after histochemical staining techniques were developed that 

could differentiate B lymphocytes from T lymphocytes. It 

turned out that the patients with B cell ALL responded to 

therapy much better than the patients with T cell ALL. Pre 

1970, differences existed between B cells and T cells, but we 



Essay  |  Dermatol Pract Concept 2012;2(4):12 51

could not tell the difference. Today we have flow cytometry 

and Cluster Designator markers and many subsets of lym-

phocytes can be detected; therapies for each subtype of leu-

kemia or lymphoma have been developed.

Yet another type of uncertainty arises as a result of 

the ambiguity of language. This topic is addressed more 

fully in earlier essays in this series on “Interpretation” and 

“Language.” This type of uncertainty can be minimized by 

paying careful attention to the context of the situation in 

which words are used and by using standard definitions of 

the words, appropriate to the context. Context will be dis-

cussed in more detail later in this essay.

Can we draw any valid 
conclusions at all?

Good heavens! If everything we “know” is a derivation of 

something else, and an inexact datum to boot, how can we 

make any progress?

An important point to make here is that uncertainty 

is not the same as randomness. While we may not be able 

to pinpoint exactly, and while we therefore feel uncertain, 

about some aspect of our work, we can be confident that the 

“true” answer lies somewhere between a set of limits; thus, 

the result is not random and completely unpredictable.

The most important thing we can do is to make careful 

observations, check the validity of those observations with 

others (test inter-observer agreement), consider possible pat-

terns and/or develop hypotheses of “causation” about the 

set of observations, and then test the competing hypotheses. 

That is to say, to evaluate observations via the process we 

know as science.

Important factors in reasoning are context and perspec-

tive. A scientifically-minded human will construct care-

fully a context and consider different perspectives, reason-

ing through the data from each of the perspectives, trying 

to find a “truth.” It is also important that multiple people 

evaluate the same data. The importance of collective efforts 

is that different people will likely have different perspectives. 

Even if two people are considering what they think is the 

same perspective, the differing prior experiences of the two 

“thinkers” will likely lead them to a slightly different view 

of, and conclusion from, the data, and ensuing discussion 

will likely further expand the joint thinking process. If dif-

ferent people can confirm data or reaffirm proofs of conclu-

sions drawn, it is more likely that the data and conclusions 

are “true,” that is to say conform with principles of Universal 

Law. It is most important that we take pains to ensure that 

we are describing a consistent system. Bronowski [6] relates 
that Kurt Godel and Bertrand Russell have reminded us, 

in a consistent system there are true things that cannot be 

proved (Godel’s Incompleteness Theorem), but (Russell) in 
an inconsistent system one can “prove” anything!

The root of much of the uncertainty we face arises from 

the situation that we are faced, as a direct result of the nature 

of the universe in which we find ourselves, with infinite vari-

ation around a number of common themes. Human beings 

are very much alike (each of us shares a large number of the 

attributes of “humanness”), but additionally, each of us is 

different in some ways from all others of the set of human 

beings (the entire set of attributes that describes each of us is 

different from the entire set of attributes that describes each 

and every other member of the set “human beings”).

Psoriasis, for example, has multiple presentations and 

features, but all features and presentations share common-

alities that, when present, allow us to make the diagnosis 

and to have certain expectations about treatments that will 

be efficacious. The task we face is to recognize which subset 

of attributes represent the commonalities required to make 

a specific diagnosis among the entire set of attributes that 

comprise the “infinite variation.”

Human senses (sight, hearing, touch, etc) work by syn-

thesizing many bits of data almost instantaneously; in fact, 

we are not even sure as individuals how we accomplish this 

feat and are not even aware consciously of many of the bits 

of energy impinging on our senses. Consider the problem of 

recognition of human faces by computers. Computers have 

not done very well, especially in the earliest attempts at pro-

gramming computers to recognize faces, although progress 

toward recognition has been made. We humans, on the other 

hand, usually have little trouble recognizing faces that we 

have seen previously, even if the face belongs to someone we 

do not know well. Also, we usually have little trouble recog-

nizing a high school classmate 20 years later at a reunion, 

even thought many features have changed (such as sagging 

jowls, wrinkles, gray hair, and the like).

Parenthetically, Sacks [7] relates that a small percent-

age of people have prosopagnosia, the inability to recognize 

faces. The ability to recognize faces has been very impor-

tant to humans throughout our evolution because we need 

to remember who has treated us fairly or unfairly so we can 

behave appropriately at a future encounter. Good face recog-

nition skills serve a survival advantage.

When computers proved miserable in their first attempts, 

the human programmers began considering just which fea-

tures humans consider important. Programs started with 

photographs, but it turned out that computers could not rec-

ognize a recently photographed person who was now tired, 

for example, because eyelids were puffier or darker than the 

original photo for comparison. Over time, and with adjust-

ments to programming made possible by careful study by 

humans of which features are more important than others, 

computer face recognition has become better.



52 Essay  |  Dermatol Pract Concept 2012;2(4):12

For another example that “important” features can be 

discerned, forensic anthropologists can draw pictures of 

what people may have looked like from the bone structure 

of a skull. Drawings of proposed current appearance of a 

missing adult are made from photographs of those same peo-

ple as children who have been abducted. Using “important” 

features in the photograph of the child and enhancing those 

features using changes expected with growth and develop-

ment and aging, a drawing is created to see if anyone has 

seen recently a teenager or adult who may have been the 

abductee.

We seem to “know” inherently what features of human 

faces are important, but we may not be able to articulate 

what those features are. Our ability to recognize faces rep-

resents a heuristic—a “short cut” we use to make decisions.

Much study has been done about heuristics. Are they 

intuition that can never be defined? If someone says they 

have a “gut feeling” or instinct, should we trust them? I think 

it likely that what we call instincts or heuristics can be ulti-

mately defined, if we deem them important enough to study 

and solve. An elegant example is given by Gerd Gigerenzer 

in Gut Feelings [8]. He describes the “gaze heuristic.” A 
friend of his played baseball and was very good at catching 

fly balls. The player’s coach thought he was lazy because he 

would sometimes just trot slowly to catch the ball, and the 

coach thought he should run as fast as he could to where he 

had calculated the ball would land. When the player did this, 

he missed more balls than if he used his usual technique. An 

assumption, prior to discovering the heuristic, was that play-

ers made complex calculations about trajectories. Gigerenzer 

quotes Richard Dawkins from The Selfish Gene, “When 
a man throws a ball high in the air and catches it again, he 

behaves as if he had solved a set of differential equations in 

predicting the trajectory of the ball. He may neither know 

nor care what a differential equation is, but this does not 

affect his skill with the ball. At some subconscious level, 

something functionally equivalent to the mathematical cal-

culations is going on.” Gigerenzer then describes the difficult 

of computing a ball’s trajectory. One must consider a para-

bolic curve and consider air resistance and wind and initial 

velocity and projection angle . . . Parenthetically, I remember 

when taking physics courses it took me several minutes to do 

my calculations, even with a slide rule. Gigerenzer describes 

that studies were performed to see what ball players actu-

ally do when they position themselves to catch a ball and 

discovered a technique called the “gaze heuristic.” The “gaze 

heuristic” works when the ball is high in the air. The player 

looks at the moving ball and decides if the angle of gaze is 

constant or changing. The heuristic states, “Fix your gaze on 

the ball, start running, and adjust your running speed so that 

the angle of gaze remains constant.” If players do this, they 

do not need to consider wind speed or spin of the ball or any 

other variables. Gigerenzer mentions that most ball players 

are unaware of how they actually catch balls, and their lack 

of awareness does not matter if the player is successful. But 

importantly, Gigerenzer avers that once the mechanism of a 

heuristic is known, it can be taught to people less successful 

and improve their performance. He describes another and 

similar heuristic used by airplane pilots. “If another airplane 

approaches and you fear collision, look for a scratch on 

the windshield and observe whether the plane moves rela-

tive to the scratch. If it does not, dive away immediately.” 

Obviously, one does not want to “catch” an airplane, as one 

does want to catch a fly ball.

Gigerenzer further avers that “a simple rule is less prone 

to estimation and calculation error and is intuitively trans-

parent.” That is to say, it is preferable to use heuristics in 

many situations. However, one should recognize the heu-

ristic as a heuristic and know the mechanism by which it 

works. Daniel Kahneman, in Thinking Fast and Slow [9], 
quotes Herbert Simon, a long-time researcher in the psy-

chology of accurate intuition. Speaking of Simon’s studies 

of chess masters, Kahneman observes, “The psychology of 

accurate intuition involves no magic . . . You can feel Simon’s 

impatience with the mythologizing of expert intuition when 

he writes: ‘The situation has provided a cue; this cue has 

given the expert access to information stored in memory, 

and the information provides the answer. Intuition is nothing 

more and nothing less than recognition.’ . . . Valid intuitions 

develop when experts have learned to recognize familiar 

elements in a new situation and to act in a manner that is 

appropriate to it.”

Thus, expert and accurate intuition is the ability to 

recognize common themes in the new situation arising in 

an instance of (infinite) variation. And we must study the 

behaviors of experts to learn the mechanisms of their heu-

ristics so that they can be shared, thereby, in keeping with 

the topic of this essay, improving the overall performance of 

diagnosticians in the healthcare system.

If we want to understand how experts think, we must 

understand how the human mind works.

The workings of the human mind

Basically, we can only think about what pops into our minds. 

Furthermore, there is a limit to how many items we can pon-

der simultaneously. What pops into our minds depends on 

association—that is to say, when we try to recall something, 

we try to draw an association with something else that helps 

us remember that item. Items that pop into our minds are 

likely to arrive there by “similarity matching” (“it looks like 

. . .”), “frequency gambling” (“I have seen that often lately”), 

and “recency” (“I just saw that”). That item we are consider-



Essay  |  Dermatol Pract Concept 2012;2(4):12 53

ing reminds us of something else, so we consider whether the 

new item belongs to the same class that we are reminded of. 

We have seen a number of one class in particular, so if we 

see something that shares (reminds us of) an attribute with 

something we see often, surely this new item is also of the 

frequently noted class. Gary Marcus, in Kluge, [10] reminds 

us that we have evolved the processes of thought that we 

now possess, so those processes must be good enough to 

enable us to survive long enough to reproduce others of our 

kind. We can learn and memorize various facts, but we must 

be able to recall items when we need to. Whether we recall 

what we need often depends on the context of the situation 

in which we are trying to think and how closely that context 

relates to the one in which we learned the fact we now need 

to recall.

Kahneman describes the large amount of work he and 

many other students of human cognitive neuroscience have 

performed over the past few decades and relates the conclu-

sions they have drawn. We have covered some of the work in 

earlier essays in this series.

Kahneman uses the metaphor of two systems of thought, 

which he calls System One and System Two. Kahneman 

observes “System 1 operates automatically and quickly, with 

little or no effort and no sense of voluntary control [while] 

System 2 allocates attention to the effortful mental activities 

that demand it, including complex computations. The opera-

tions of System 2 are often associated with the subjective 

experience of agency, choice, and concentration.”

System One is actually the prime mover and is respon-

sible for letting thoughts arrive in our conscious awareness.

Kahneman points out that we, ourselves, identify with 

System 2, “the conscious reasoning self that has beliefs, 

makes choices, and decides what to think about and what to 

do. Although System 2 believes itself to be where the action 

is, the automatic System 1 is the hero . . . The automatic 

operations of System 1 generate surprisingly complex pat-

terns of ideas, but only the slower System 2 can construct 

thoughts in an orderly series of steps.”

Kahneman lists, in order of complexity, some examples 

of activities thought to performed by System 1:

•	 Detect that an object is more distant than another

•	 Orient to the source of a sudden sound

•	 Complete a common phrase, such as “bread and . . .”

•	 Respond to a horrible picture by making a “disgust face”

•	 Detect hostility in a voice

•	 Answer 2 + 2 =?

•	 Read words on large billboards

•	 Drive a car on an empty road

•	 Find a strong move in chess, if you are a chess master

•	 Understand simple sentences

•	 Recognize that a “meek and tidy soul with a passion for 

detail” [discussed earlier in Kahneman’s book as a stereo-

type for a librarian] resembles an occupational stereotype.

Says Kahneman “We are born to perceive the world 

around us, recognize objects, orient attention, avoid losses, 

and fear spiders. Other mental activities become fast and 

automatic through prolonged practice.”

Kahneman describes further System Two, “The highly 

diverse operations of System 2 have one feature in common: 

they require attention and are disrupted when attention is 

drawn away. Here are some examples:

•	 Brace for the starter gun in a race

•	 Focus attention on the clowns in the circus

•	 Focus on the voice of a particular person in a crowded 

and noisy room

•	 Look for a woman with white hair

•	 Search memory to identify a surprising sound

•	 Maintain a faster walking speed than is natural for you

•	 Monitor the appropriateness of your behavior in a social 

situation

•	 Count the occurrences of the letter a in a page of text
•	 Tell someone your phone number

•	 Park in a narrow space (for most people except the garage 

attendant [who can use System One for this])

•	 Compare two washing machines for overall value

•	 Fill out a tax form

•	 Check the validity of a complex logical argument

Kahneman observes that everyone has a degree of aware-

ness that his/her capacity to pay attention is limited. He 

describes the study from the book by Chabris and Simons, 

The Invisible Gorilla, in which students, directed to count 

passes on a basketball court were concentrating so hard that 

they failed to see a gorilla walk onto the court. Kahneman 

notes that “intense focussing on a task can make people effec-

tively blind, even to stimuli that normally attract attention.”

Kahneman concludes that System One generates sug-

gestions to System Two. These suggestions are impressions, 

intuitions, intentions, and feelings. But once endorsed by 

System Two, “impressions and intuitions turn into beliefs, 

and impulses turn into voluntary actions . . . [most of the 

time, and when life runs smoothly] System 2 adopts the sug-

gestions of System 1 with little or no modification . . . [but] 

when System 1 runs into difficulty [for example when asked 

to multiply 17 × 24], it calls on System 2 to support more 

detailed and specific processing that may solve the problem 

of the moment . . . System 2 is [also] activated when an event 

is detected that violates the model of the world that System 

1 maintains.”

Kahneman continues, “The division of labor between 

System 1 and System 2 is highly efficient: it minimizes effort 

and optimizes performance. The arrangement works well 

most of the time because System 1 is generally very good 



54 Essay  |  Dermatol Pract Concept 2012;2(4):12

at what it does: its models of familiar situations are often 

accurate, its short-term predictions are usually accurate as 

well, and its initial reactions to challenges are swift and gen-

erally appropriate. System 1 has biases, however, systematic 

errors that it is prone to make in specified circumstances . . . 

it sometimes answers easier questions than the one it was 

asked, and it has little understanding of logic and statistics 

. . . [also] it cannot be turned off.”

Another facet of our minds is that we see what we want 

or expect to see. In the essay in this series on Patterns, we 

discussed the work of Erich Harth [11]. He described what 

occurs in one’s brain as one is walking along a beach. One’s 

eye catches sight of a round, shiny object. One’s brain tries 

to make it into a coin, but one’s senses can save the day by 

imposing reality on the situation. If one concentrates one’s 

visual apparatus on the object and compares it to one’s 

expectation of the appearance of a coin, one can see if the 

edge of the coin is oriented at right angles to the circular 

surface (no), or if the surface is truly round (no), or whether 

there is some sort of etching—the head of a former presi-

dent, perhaps—on the surface of it (no); by using one’s senses 

to compare what one sees to one’s expectation of what one 

thinks or hopes it might be, one concludes that what one 

really sees is a piece of shell.

Of course, one must engage System Two in order to make 

the determination. A frequent occurrence of a failure in com-

paring what one sees with what one expects is when one tries 

to proofread one’s own work, especially shortly after one has 

written it. If one lays the work down for a day or two before 

proofreading, one is much more likely to detect the errors 

that are present.

Another thing that Kahneman points out is that humans 

have a strong desire for everything to fit together into a 

logical story. If fact, whenever we have available a few data 

items, we invent a story to make all the facts fit together. It is 

important to us that what ever occurs has a cause. When we 

learn a few data, even if those data prove later to be unre-

lated in a causal manner, our System One tries to relate them 

in a causal way. Kahneman gives an example “‘Fred’s par-

ents arrived late. The caterers were expected soon. Fred was 

angry.’ You know why Fred was angry, and it is not because 

the caterers were expected soon. In your network of associa-

tions, anger and lack of punctuality are linked as an effect 

and its possible cause, but there is no link between anger and 

the idea of expecting caterers. A coherent story was instantly 

constructed as you read; you immediately knew the cause 

of Fred’s anger. Finding such causal connections is part of 

understanding a story and is an automatic operation of Sys-

tem 1. System 2, your conscious self, was offered the causal 

interpretation and accepted it.”

Kahneman also points out that System 2 can only work 

on one problem at a time. Recall from above that maintain-

ing a higher than usual walking speed is a System 2 activity. 

Kahneman performed some studies in which, while walk-

ing at a fast pace with a test subject, the test subject would 

be asked to perform a complex multiplication task, such as 

multiply 17 by 24. Each time, the test subject would slow 

down to complete the multiplication task. The implication 

of this is that humans really cannot multitask two System 

2 activities at the same time. We might be able to perform a 

System 1 activity concurrently with a System 2 activity, but 

not two activities that each require our attention (a defining 

aspect of a System 2 activity)

Another thing about the way humans think—we tend 

to think automatically somebody must know the answer 

to any question that arises. We may admit, reluctantly, that 

perhaps we ourselves do not know the answer to some ques-

tion, but we assume that somebody knows. This is natural 

in a way. When we are children, our parents or guardians 

teach us about the world around us. Whenever we do not 

know something, we ask them and almost always an answer 

is forthcoming. If they do not know, we are referred to ref-

erences (dictionaries, encyclopedias and the like) and the 

answer is there. Even when we have difficulty finding an 

answer, we assume the answer must be out there somewhere. 

It takes a long time, but eventually, especially when we get 

to graduate school, we begin to learn that some questions 

do not have satisfactory answers. We detect inconsistencies 

between the “answer” to this question and the “answer” to 

another question. We recognize that both answers cannot be 

true at the same time, and we can find no ready resolution 

to the dilemma.

Thinking recursively

Another thing about how humans think—we think recur-

sively. When ever we have a thought, we tend to modify 

that thought by something else that pops into our minds. 

James Reason, in Human Error [12], posits that people solve 

problems in three basic ways: skill-based, rule-based, and 

knowledge-based. Skill-based is used most often and relies 

almost entirely on the automaticity of “System 1.” Examples 

include tying one’s shoes, answering a telephone, brushing 

one’s teeth, or riding a bicycle. Once we learn the activity, we 

hardly think about it. The action just seems to occur with-

out much conscious thought, once we decide to initiate the 

action. Rule-based refers to following specific rules to an 

end. Algorithms are a good example of rule-based actions, 

and we are encouraged often to use algorithms when we 

practice medicine. For both skill-based and rule-based activi-

ties we are familiar with the situation and we know what to 

expect. The only problems with execution of these activities 

arise when we are distracted during skill-based actions or 



Essay  |  Dermatol Pract Concept 2012;2(4):12 55

when we misidentify the problem and choose an inappropri-

ate rule to execute for a rule-based problem.

We use knowledge-based techniques when we face prob-

lems that are new. We have never seen anything quite like the 

situation we find ourselves faced with. As a result we have to 

figure out what to do as we go along. At each stage, as we are 

trying to solve the problem, we ask ourselves, “Are we any 

closer to the answer?” Interestingly, we solve these problems 

by imagining a desired result and then trying to get to that 

result. We say to ourselves, “It looks a little like that other 

problem I had, so I’ll try this maneuver that worked back 

then.” After that step, we reassess and decide whether we 

seem to be closer to our imagined goal. If so, we continue. If 

not, we take a step back and try another tack.

It is the process of assessment and reassessment, using 

the feedback we receive from observing the status of events 

at each step of our progress and then deciding on the next 

maneuver based on the information gleaned, that is the 

recursive process.

Interestingly, this process that we have evolved to use is 

modeled in manufacturing endeavors as “Good Manufactur-

ing Processes,” or GMP. By following GMP and checking the 

interim product after each step, the firm has an opportunity 

to make alterations and save a batch of product that, if not 

manufactured according to GMP, might otherwise be lost.

The point I am trying to make about thinking recursively 

is that even though thoughts occur to us in succession, we 

do not really think in a linear fashion. Algorithms, however, 

do tend to encourage us to think linearly. Consider driving 

through territory new to you in an area of town that has 

many signs and many potential turn offs. You have a set of 

directions, and you know the name of the place to which you 

are traveling. Assume you have a fairly good sense of direc-

tion and can tell North, South, East, and West. You know 

you are traveling in a northerly direction. While turning here 

at this street and driving through two lights, then turning at 

a service station, etc, you become aware that you are moving 

in a southeasterly direction. What do you do? You still have 

ten steps to follow on your set directions. Do you travel in a 

“linear fashion,” continuing with the set of directions until 

you are at the last one (analogous to linear thinking)? Or do 

you pull over and stop, reexamine your directions to see if 

you might have made an error, and possibly turn around and 

go back to the last site where you felt you were still traveling 

in a northerly direction—the sense that you were meeting 

your expectation (analogous to recursive thinking)?

Melanie Mitchell, in Complexity: A Guided Tour [13], 

describes her experience of writing a computer program that 

solves problems by making analogies. Mitchell quotes Mar-

vin Minsky, a founder of the field of Artificial Intelligence, 

who said, “Easy things are hard,” referring to attempts to 

understand some of a human’s most basic thought processes 

and to replicate those processes by computer programming. 

Mitchell says of analogy-making, “ . . . analogy-making is 

the ability to perceive abstract similarity between two things 

in the face of superficial differences. This ability pervades 

almost every aspect of what we call intelligence.” Mitchell 

quotes Henry David Thoreau, “All perception of truth is the 

detection of analogy.”

A basic premise underlying the project was that the strat-

egy used in solving new problems is one of “Explore and 

Exploit.” One thing Mitchell realized was that all possibili-

ties must be potentially available, but they cannot be equally 

available. For example, counterintuitive possibilities must 

be potentially available, but must require a cogent reason 

to be considered strongly enough to warrant committing 

significant resources for adequate exploration of that pos-

sibility. She also realized the importance of keeping a bal-

ance between exploration and exploitation. “When promis-

ing possibilities are identified, they should be exploited at a 

rate and intensity related to their estimated promise, which 

is continually being updated. [recursive evaluation] But at all 

times exploration for new possibilities should continue. The 

problem is how to allocate limited resources—. . . be they 

lymphocytes, enzymes, or thoughts—to different possibilities 

in a dynamic way that takes new information into account 

as it is obtained.”

Mitchell’s goal was to write a computer program called 

“copycat” (because a premise of the project was that “anal-

ogy-making is a subtle form of imitation”). The goal of the 

program was to start with the example of two given strings 

of letters, similar but with an alteration, and then to give the 

problem of a “test” string of letters for the computer to come 

up with an altered string that was analogous to the example. 

One given alteration was “abc morphs to abd.” The test was 

“mrrjjj morphs to ?” The goal was to use concepts possessed 

by the program (concepts thought to underlie human ability 

to form analogy) to build perceptual structures. “ . . . descrip-

tions of objects, links between objects in the same string, and 

correspondences between objects in different strings . . . The 

structures the program builds represent its understanding of 

the problem and allow it to formulate a solution . . . the con-

cepts [must] be adaptable to different situations . . .”

Mitchell continues, “ . . . a scheme [was proposed] for 

exploring uncertain environments: the ‘parallel terraced scan,’ 

. . . In this scheme many possibilities are explored in parallel, 

each being allocated resources according to feedback about 

its current promise, whose estimation is updated continually 

as new information is obtained. . . . all possibilities have the 

potential to be explored, but at any given time only some are 

actively explored, and not with equal resources. When a per-

son ( . . . or an immune system) has little information about 

the situation facing it, the exploration of possibilities starts 

out being very random, highly parallel (many possibilities 



56 Essay  |  Dermatol Pract Concept 2012;2(4):12

being considered at once) and unfocused: there is no pressure 

to explore any particular possibility more strongly than any 

other. As more and more information is obtained, explora-

tion gradually becomes more focused (increasing resources 

are concentrated on a smaller number of possibilities) and 

less random: possibilities that have already been identified as 

promising are exploited.”

Mitchell’s program has subroutines such as “Slipnet” that 

is a ‘network of concepts, each of which consists of a central 

node surrounded by potential associations and slippages’; 

“Workspace,” ‘in which letters composing the analogy prob-

lem reside and in which perceptual structures are built on 

top of letters’; “Codelets,” ‘agents that continually explore 

possibilities for perceptual structures to build in Workspace 

. . . [and working in teams] . . . [using a parallel terrace scan] 

. . . [teams of codelets] “via competition and cooperation, 

gradually build up a hierarchy of structures that defines the 

program’s ‘understanding’ of the situation with which it is 

faced’; and “Temperature,” “which measures the amount of 

perceptual organization in the system . . . high temperature 

corresponds to disorganization and low temperature corre-

sponds to a high degree of organization.”

Observes Mitchell, “Via the mechanisms [of the pro-

gram], Copycat avoids the Catch-22 of perception: you can’t 

explore everything, but you don’t know which possibilities 

are worth exploring without first exploring them. You have 

to be open-minded, but the territory is too vast to explore 

everything; you need to use probabilities in order for explo-

ration to be fair. In Copycat’s biologically inspired strategy, 

early on there is little information, resulting in high tempera-

ture and a high degree of randomness, with lots of parallel 

explorations. As more and more information is obtained and 

fitting concepts are found, the temperature falls, and explo-

ration becomes more deterministic and more serial as cer-

tain concepts come to dominate. The overall result is that 

the system gradually changes from a mostly random, paral-

lel, bottom-up mode of processing to a deterministic, serial, 

focused mode in which a coherent perception of the situation 

at hand is gradually discovered and gradually ‘frozen in.’”

It seems to me that Mitchell’s program serves as a good 

analogy for the process we use for making a diagnosis, or for 

that matter for any problem-solving activity. And because we 

face infinite variation around a number of common themes, 

we must use a “knowledge-based” approach to problem-

solving more often than we would like to, even if we narrow 

somewhat early in the process our exploration by recogniz-

ing an attribute, or group of attributes, that seem to suggest 

to us a specific common theme. However, as admonished by 

Mitchell and her program we must still “explore” to a small 

degree less likely probabilities. After all, if we do not con-

sider, however briefly, a diagnosis, we will never make that 

diagnosis.

Dealing with large amounts of data

How do we humans deal with large amounts of data? Is 

more data always better? We have already learned from 

Kahneman’s work that our minds are lazy. We know there 

is no way we can learn and use efficiently vast amounts of 

data. We need shortcuts of some sort. A common thing that 

we humans seem to want is some sort of “unifying theory.” 

We think in our heart of hearts that if we have the rule, or a 

small and easily remembered set of rules, we can figure out 

anything and we will not have to memorize so much and 

work so hard to make progress.

Surely, it is preferable to have as much data as possible—

or is it? If we consider the example of Sudoku, one strategy 

to solving a puzzle is to start by writing all the possible can-

didate numbers in the top of each empty square, then look 

at the puzzle, including the possibilities, and try to figure 

out which number goes in each square. As an avid fan of 

Sudoku, I actually used this technique when I first started 

working the puzzles. But it ended up being very confusing—

there was simply too much data to consider at one time and 

I had more difficulty solving puzzles than I now do. I learned 

a few strategic “tricks” and now I only write possibilities at 

the top of the square when I have the square down to two or 

three candidates.

When we make a diagnosis, I posit that it is also pos-

sible to have too much data. It is much easier to figure out a 

diagnosis by performing a little strategic “legwork” first (by 

considering the clinical definitions of the diseases on our list 

of differential diagnoses for the patient) and to then order 

judiciously a few ancillary studies to further “flesh out” the 

data (determined by the clinical definitions) missing from 

our earlier “legwork.”

Is all data “information”? It seems that data can only 

be considered “information” in the context of the problem 

as a whole. Data that distracts us does not help us solve the 

problem. That extraneous data only takes up space in our 

Working Memory and we waste time trying, as Kahneman 

warns us, to make a coherent story of all the data residing 

in Working Memory. As Reason would say, extraneous and 

distracting data serve as “Nonsigns” (as opposed to “Signs,” 

which argue in favor of an hypothesis, or “Countersigns,” 

which argue against an hypothesis) and, therefore, Nonsigns 

do not serve as “information.”

The importance of context

I mentioned earlier that context is vitally important to mak-

ing correctly inferences about data. I recall an incident that 

occurred early in my pathology residency; I had been on my 

first surgical pathology rotation for only a couple of weeks. 

One case I had was a keratotic skin lesion from a middle-aged 



Essay  |  Dermatol Pract Concept 2012;2(4):12 57

person. I was still at the stage of looking through pathology 

textbooks and matching pictures to make a diagnosis. Pag-

ing through Lever’s Histopathology of the Skin, I happened 

upon a picture that looked very like the material on my glass 

slide. The caption of the picture read “Acrokeratosis verru-

ciformis of Hopf.” I carefully made a note of that and went 

on to the next case. When I went to sign out my cases with 

my attending and told him my diagnosis, he said, “Have 

you ever made a diagnosis of acrokeratosis verruciformis of 

Hopf before?” I looked quizzically at him—surely he knew 

this was my second week ever of surgical pathology, and on 

autopsy rotations we never paid much attention (rightly or 

wrongly) to the deceased’s skin. I said, “No.” He responded, 

“Well you better make the diagnosis now because you will 

likely never make the diagnosis again in you entire career.” I 

had read that the disease was rare, but the picture did look 

“exactly” like what was on the glass slide representing the 

patient’s skin lesion. At that point in my career I had very 

little understanding of what sorts of information could be 

learned from looking at tissue through a microscope. I had 

no concept of how much context was necessary to make a 

correct diagnosis. I still burn with shame whenever I think of 

my early diagnostic faux pas. But at least I learned a lesson 

from that experience.

Yair Newman, in “Meaning-Making in Language and 

Biology” [14], points out that both language and biological 

systems operate in recursive-hierarchical and semantically 

open systems. For example, he points out that a word, by 

itself, can have any number of meanings. But when a word 

is in a sentence, the meaning of that word becomes more 

restricted. The meaning is further restricted when the sen-

tence is in the additional context of a paragraph. Meaning 

can be further restricted by the chapter and book in which 

the word finds itself. In this way, a finite number of symbols 

(for example an alphabet) can be used to make an infinite 

number of messages. Additionally, while one cannot under-

stand a word without seeing the sentence it is in, neither 

can one understand the sentence without understanding the 

words. This serves as “interaction-in-context” and exhibits 

hermeneutic circularity (which means that there is recursive 

feedback between the levels of interaction).

Newman discusses further the example of protein con-

formation. In the context of the cellular machinery pro-

teins fold to a certain conformation even though some of 

the intermediate steps might have a higher, rather than 

lower, energy state (which would be unexpected to occur 

under the hypothesis that the protein will conform only 

to a lower state of energy). Of course, in the context of 

enzymes and catalysts, the protein is able to be temporarily 

in less stable intermediate conformations on the way to the 

final conformation.

Using the example of the immune system, Newman 

mentions that an agent may only serve as an antigen if the 

immune agents (macrophages, T cells, B cells, and cytokines) 

act in concert to recognize the agent as an antigen. Some 

people are allergic to ragweed or peanuts and other people 

are not, for example.

Furthermore, even second messengers such as cyclic ade-

nosine monophosphate (cAMP), although technically freely 

diffusible within a cell and potentially able to interact with 

different subsystems (having potentially different meanings), 

are generated and utilized focally, near the membrane-bound 

proteins with which they form a cellular subsystem of action 

and regulation.

In making a diagnosis, context is created by looking for 

multiple attributes that make up the set of a specific disease. 

A chief complaint can mean many conditions, but by adding 

attributes, gradually other conditions on the list of differen-

tial diagnoses are eliminated. A context is created such that 

the chief complaint comes to mean only one disease. For the 

example of substernal chest pain, if we add the attributes of 

“pressure,” radiation to the left arm, no change with breath-

ing cycle, lasts about 20 minutes and goes away, relieved 

by sublingual nitroglycerin, and aggravated by exercise and 

exposure to cold, we can exclude the possibilities of mus-

culoskeletal pain, pleurisy, and dissecting aortic aneurysm, 

but we are still left with the possibilities of cardiac angina, 

hypertrophic cardiomyopathy, aortic stenosis, and esopha-

geal spasm. If we then add to the context the item of systolic 

ejection murmur, we narrow the problem down to aortic 

stenosis.

Data and information; context 
and perspective

I have used frequently the terms “data,” “information,” 

“context,” and “perspective.” How are the meanings of these 

terms related?

A “datum” is a fact that has not yet registered in a human 

brain. Once a datum registers in a human brain (once a per-

son is actually paying attention to a Datum), it becomes 

“information.”

James Gleich, in The Information [15], mentions the 

lamentation of Heinz von Foerster during an early cyber-

netics conference, who complained “ . . . that information 

theory was merely about ‘beep beeps,’ saying that only when 

understanding begins, in the human brain, ‘then informa-

tion is born—it’s not the beeps.’” Thus, a datum becomes 

information when the human mind, using System 1, begins 

to associate the datum with other information/data stored in 

the human brain.

“Context” is the “system” of interacting data/informa-

tion, being considered recursively by the thinking human.



58 Essay  |  Dermatol Pract Concept 2012;2(4):12

“Perspective” is a sort of “lens” through which a thinking 

human considers the data/information and context. Perspec-

tive can be purposely altered to a certain extent, although as 

mentioned in the earlier essay in this series on Interpretation 

some of our bedrock beliefs are so ingrained in our world-

view that we are not consciously aware of how we came 

to believe them and we may not be able to consider some 

perspectives that would require considering those beliefs to 

be false.

An example of a datum not registering in the brain of a 

diagnostician, and thus not becoming information, might be 

examining the results of a complete blood count. The diag-

nostician glances at the entire sheet of data, but perhaps only 

registers the hemoglobin, hematocrit, total white blood cell 

count, and platelet count, paying no attention to the mean 

corpuscular volume, mean corpuscular hemoglobin, red cell 

distribution width, or mean platelet volume. All those data 

values are reported by the lab on the report, but diagnosti-

cians may not pay attention to them in a specific patient case.

Making a diagnosis

So, if we cannot make a diagnosis under ideal conditions, 

how do we do it? The ground rules still apply. That is to say 

we still require a knowledge base, a set of reasoning skills, 

and the ability to acquire necessary data in the case of an 

individual patient.

Considering how the human mind works, we must wait 

until some possible disease state pops into our minds. We 

know from the work of Kahneman and Reason, that com-

mon diseases (frequency-matching) are likely to pop up, 

recently thought-about (recency-matching) diseases are 

likely to pop up, and diseases that we are familiar with that 

have similar features (similarity-matching) are likely to pop 

up. Since the human mind works by association, as soon as 

the mind of a diagnostician is stimulated by hearing a chief 

complaint (the usual stimulus in the healthcare setting) or 

by noticing something amiss (seeing what could only be a 

melanoma on the neck of the person standing in front of us 

in a check-out line at the grocery store or seeing an unre-

sponsive person, having witnessed his recent collapse in the 

park) or even a combination of the two (a suspected malin-

gerer with a complaint that seems improbable in the context 

of additional data), that diagnostician begins using System 1 

and starts to make associations and draw data from stored 

memory into working memory.

In addition to the fact that we must wait for some idea 

to pop into our mind based on what we see is that “we 

notice what we notice” and nothing more. Ian Stewart, in 

The Mathematics of Life [16], discusses the work of tax-

onomists. States Stewart, “Taxonomists quickly learned that 

the most important features for classification were seldom 

those that immediately attracted the attention of the human 

observer. . . . Which characters are best suited for classify-

ing organisms? Tigers and Zebras are both striped, but that 

doesn’t imply that they are closely related. In fact, tigers and 

zebras do not belong to the same genus, to the same family 

or even to the same order. Tigers are in the order Carnivora 

(carnivores), but zebras are in the order Perissodactyla (odd-

toed hoofed animals). The two species come together only 

on the level of their class: both are mammals. So charac-

ters that strike the eye, like the tiger’s stripes, are often less 

significant than subtler ones, such as how many toes the 

creature possesses.”

Still explaining taxonomy, Stewart also reminds us, “One 

of the first steps in the development of any branch of science 

is to find a way to organize the wealth of observations that 

nature presents to us, and this is especially necessary in biol-

ogy, because of the vast diversity of life.” Stewart describes 

the use by taxonomists of cladograms, diagrams that relate 

branch points and their timing and that describe shared 

attributes and the time during evolution that the attributes 

split or diverged (were no longer shared by subsequent [new] 

groups). Each “clade” represents an ancestral organism with 

all of its evolutionary descendants.

Stewart mentions that constructing a clade “involves 

three steps: collect data on the organisms concerned, think 

about suitable cladograms, and choose the best of these.” 

From the collected data, a set of characters are selected and 

the candidate organisms are assigned a value for having (1) 

or not having (0) the attribute. Then the data is assessed 

as to how many organisms have the highest percentage 

of attributes, how many a smaller percentage and so on. 

Organisms more closely related share more attributes and 

those sharing fewer attributes are less closely related. The 

data is then fed into a computer and the computer generates 

possible cladograms. The computer then analyzes statisti-

cally the data to see which cladogram is the best fit. Then 

starting with the values generated, the computer re-runs the 

data multiple times until there is no significant difference 

between the previously run cladogram and the subsequent 

one. The process is re-run, using different attributes. The 

goal says, Stewart, is to find convergent evidence, “We can 

be very confident if different data, analyzed by different 

methods, lead to similar results.”

I believe this is similar to the process of figuring out 

which attributes associate to define a disease. If we consider 

the example above of substernal chest pain, we can see that 

a certain number of attributes are shared by the different 

disease entities that make up our differential diagnosis, but 

as the problem is considered in the context of different data 

(new attributes added to the mix), some possibilities become 

less likely (less closely related to the definition of the dis-



Essay  |  Dermatol Pract Concept 2012;2(4):12 59

ease). Also, consider over time how the understanding has 

evolved of various disease processes and how new attributes 

are added to the armamentarium in order to better classify 

a disease. We have iterated the process of defining a disease 

over decades, each new study helping to find a better defini-

tion of disease, a definition that will hopefully differentiate 

that disease from all others.

Of course contrary to the ideal conditions for making 

a diagnosis, the knowledge base of each of us is limited, 

the knowledge base itself has items lacking because many 

diseases are not fully understood, and some of our current 

“knowledge” will prove incorrect, perhaps because data is 

missing or because we have made incorrect inferences about 

the data we have.

The best we can do, really, is to consider a differential 

diagnosis based on the presenting situation, either chief 

complaint or observation of some aspect of the patient. The 

practice of medicine is a “team sport” (not necessarily a team 

working at the same time and on the same patient, but col-

lectively and over time we physicians share knowledge about 

a population of patients), so we had best consider a differ-

ential diagnosis that is listed by an authority for the pre-

senting situation. An authority might be a text or consensus 

statement from a professional society, for example. Then, in 

order to figure out the most important additional data to 

obtain, we must understand the clinical definitions of the 

disease entities on our list of differential diagnoses. This is in 

a way analogous to using a stratagem in solving a Sudoku 

puzzle—instead of obtaining a large number of data, some 

of which might be merely distracting and not helpful, we 

obtain the more helpful data. The clinical definitions include 

the clinical features, including items of history, information 

obtained from physical exam maneuvers, and ancillary test-

ing necessary to make a diagnosis of disease “A.”

Parenthetically a clinical definition of a disease differs 

from a pathophysiologic definition of the disease. The clini-

cal definition depends on the pathophysiologic definition, 

but consists of items readily discernible in a clinical setting. 

For example, the pathophysiologic definition of myocardial 

infarction might be “death of myocardial fibers due to occlu-

sion of a coronary artery by a blood clot caused by the rup-

ture of an atherosclerotic plaque, leading to segmental loss 

of contractility of the heart muscle in the area supplied by 

the occluded artery and leading to decreased cardiac output 

and possibly arrhythmia, etc. Evidence of myocardial death 

includes features histologically of, first, contraction band 

necrosis and, subsequently infiltration of the necrotic area 

by neutrophils . . .” The clinical definition is as follows: at 

least two of 1) chest pain consistent with ischemic chest pain 

(substernal, squeezing, pressure, radiation to jaw or arm, 

may be accompanied by diaphoresis), 2) localized ischemic 

changes on electrocardiogram, consistent with blockage of 

a coronary artery, and 3) elevated cardiac marker (troponin 

and/or creatine kinase (CK)-MB.

The clinical definitions are developed over time, after 

many studies, including postmortem examinations on 

prior patients, have been performed elucidating the patho-

physiologic definitions. Clinical definitions emphasize sets 

composed of clinical features and routine diagnostic tests, 

each set ideally unique to the disease in question; patho-

physiologic definitions often rely on specialized testing that 

has not yet been approved for patient testing outside the 

research setting.

When considering the items on our list of differential 

diagnoses and when looking for data that answers the ques-

tion, “Does this patient have the features required to diag-

nose clinically Disease ‘A,’ Disease ‘B,’ or Disease ‘C’?” we 

must ask the question from the perspective of each disease 

on the list of differential diagnoses. We must say to ourselves, 

“Feature One is a sign (favors the diagnosis) for Disease ‘A,’ 

but a countersign (argues against the diagnosis) for Disease 

‘B,’” and so forth, going through each of the data from the 

perspective of each disease. Then the disease that has the 

fewest countersigns is the most likely diagnosis. Of course, 

any very important countersigns may cause us to broaden 

our list of differential diagnoses, considering a less common 

disease as the cause. With the data we have, we engage in 

an episode of recursive thinking, going back and forth in 

our minds between possibilities until we finally decide on 

one—the diagnosis.

There are two very important points to keep in the back 

of our minds when we are making a diagnosis. First, disease 

entities do not have differential diagnoses. Disease entities 

share attributes and groups of attributes. Only the attri-

butes and groups of attributes themselves have differential 

diagnoses.

This may seem like a nit-picking distinction, but it is 

crucial. Think about times you have read an article about 

one disease or another in the context of thinking about a 

particular patient. What happens when you get to the list of 

supposed differential diagnoses for that disease? You prob-

ably think to yourself, “I would never have considered that 

disease in this patient.” And then what? If you believe that 

diseases have differential diagnoses, just because that disease 

was on the list of differential diagnoses you might order 

another test or two to rule out the disease that you were not 

even considering for that patient. But if you look at it from 

the perspective of the previous paragraph and think about 

which attribute or attributes the patient has that led you to 

read the article to begin with, you can then decide rationally 

whether the disease on the list you were not considering 

initially should be considered (shares the attribute(s) with 

the disease described in the article and with your patient) or 

whether the disease described in the article shares other attri-



60 Essay  |  Dermatol Pract Concept 2012;2(4):12

butes with the disease listed as a differential diagnosis, but 

other attributes that your patient does not exhibit; that is to 

say, you should not consider that disease on the differential 

list as a possibility for your patient.

For example, if a child presents with limb pain, one pos-

sibility is sickle cell crisis. If one reads an article about sickle 

cell disease in all its manifestations, the list of differential 

diagnoses might include carotid-cavernous sinus fistula. If 

the patient does not have the attribute of a swollen proptotic 

eye, one need not consider carotid-cavernous sinus fistula as 

a diagnostic possibility. On the other hand, if acute leukemia 

is on the list of differential diagnoses, sickle cell crisis can 

share the attribute of limb pain with a presentation of acute 

leukemia and leukemia should be considered in the differen-

tial because both conditions share the attribute exhibited by 

the patient—limb pain.

Disease states can be considered sets of attributes. Each 

disease-set has many attributes and only a subset (of the 

disease-set as a whole) of the attributes serve to define the 

disease for diagnostic purposes. For example, fever is a com-

mon symptom and is an attribute shared by many diseases, 

but usually we recognize that fever means “the patient is ill” 

and we look for other attributes that define more closely 

the nature of the disease. Periodicity of the patient’s fever 

may suggest malaria, for example, or presence of a rash 

accompanying the fever may suggest measles as another 

example; and usually we order an ancillary test, perhaps a 

Wright-Giemsa stained smear of peripheral blood to look 

for malarial parasites.

When considering the concept of disease-sets when mak-

ing a diagnosis, it can be useful to consider Venn diagrams, 

whereby one looks for areas of overlap between sets. Look-

ing for attributes in the area of overlap, that is to say looking 

for shared attributes, does not help us differentiate between 

disease-sets. We must look for attributes outside the area of 

overlap to differentiate between two (or more) diseases.

The second very important point is that there is one invi-

olable rule that applies to the process of making a diagnosis. 

That rule is Baye’s Rule. For a population in which there are 

members of different classifications, but with each member 

of the population sharing a given attribute or group of attri-

butes, the likelihood that a specific classification should be 

applied to a member of that population is directly propor-

tional to the prevalence of the classifications in the popula-

tion. For example, if the population consists of 1110 items 

and if 1000 items are class A, 100 items are class B, and 10 

items are class C, if one pulls one item “out of the hat (the 

hat representing the attribute or group of attributes in ques-

tion)” at random, the likelihood is much higher that the item 

will be of class A than of any other class. Items of class A are 

ten times more likely than class B and 100 times more likely 

than class C.

As we add attributes to the set that represents the attri-

butes of our patient, some differential diagnostic possibilities 

drop out of contention because those deleted diseases have 

attributes that exclude them from consideration or because 

they do not have an attribute or attributes required to make 

a diagnosis, leaving fewer and fewer possibilities. For a 

disease entity defined precisely, when the attributes of the 

patient match the set of attributes that make up that disease, 

the prevalence of the disease in a population of patients shar-

ing all the attributes of our patient approaches 100 percent 

(of course there will always be some disease not yet discov-

ered that cannot be excluded or some disease we failed to 

consider; thus the prevalence cannot reach 100%).

The problem, of course, in using this inviolable rule 

arises from trying to assign the attribute to begin with, par-

ticularly if that attribute is “a matter of degrees,” like the red 

and green apples or patients with shortness of breath with a 

measurement of B-type Natriuretic Peptide level. Attributes 

that lie on a continuum can only be assigned as “yes/no” in 

context, and by a process of recursive analysis. We must con-

tinuously “shuffle” or “juggle” the data, considering slightly 

new perspectives with each additional datum thrown into 

the mix until any change in the “apparent answer” becomes 

insignificant. When the degree of change has become insig-

nificant, then we have “homed in” on a “common theme,” 

the insignificant change representing part of the infinite vari-

ation that is an integral part of the system in which we live 

and work.

Failure in making a diagnosis

Using the analogy of Explore and Exploit, we can fail at a 

number of places on the road to making a correct diagnosis. 

We can fail to recognize an attribute and not even realize a 

diagnosis should be made. We can “home in” on an attribute 

that is not important, or fail to follow up by looking for more 

important attributes (from the standpoint of accurate classi-

fication) and end up making a wrong diagnosis. We can look 

for “confirmatory” attributes only (from the perspective of 

only one potential diagnosis on our list of differential diag-

noses), failing to rule out other items on the list of differen-

tial diagnoses. We must remember that we diagnosticians are 

human beings first and diagnosticians second. We think just 

like all other human beings. We must remember to engage 

System 2 to ensure we are not making a careless mistake. 

In the end, every human being classifies items many times a 

day, but as diagnosticians, our classifications affect another 

human life and we must take care when we perform our task 

that we have taken reasonable precaution to avoid the errors 

common to the process of classification, errors that are most 

often due to “signing off” too soon on the work of System 1.



Essay  |  Dermatol Pract Concept 2012;2(4):12 61

Conclusion

The process of making a diagnosis is analogous to a com-

plex adaptive system and, therefore, principles that apply to 

complex adaptive systems apply to the process of making a 

diagnosis.

Complex Adaptive Systems are composed of interacting 

elements, each part doing a different job, but each part inte-

gral to the outcome. While we can observe and study ele-

ments, we can not observe and study interactions in the same 

way. We can only observe outcomes, that is to say changes to 

an element, that result from the interaction(s). Furthermore, 

no one element controls the system; however, any one ele-

ment can affect all other elements.

Some of the elements that make up the system that is the 

process of making a diagnosis include the subsystems of the 

human mind (perception, interpretation, imagination, clas-

sification, attempts to construct coherence between items of 

information, knowledge base, ability to recall stored items 

into working memory, and in general all the items attributed 

by Kahneman to System 1 and System 2) and the relation-

ship of each human’s mind to culture and society (including 

our ability collectively to study disease processes and our 

collective understanding of the concepts of health and dis-

ease). Any one of these elements can, and does, influence the 

ultimate diagnosis of a given patient at a given time.

Complex systems, by definition are not completely pre-

dictable. To review from Edelman and Tononi [17] “Only 

something that appears to be both orderly and disorderly, 

regular and irregular, variant and invariant, constant and 

changing, stable and unstable deserves to be called complex.” 

That interactions themselves are not observable directly 

explains in large part the unpredictability that occurs in 

complex systems.

Systematic errors occur in all systems and these errors 

are predictable but the timing of these errors is not predict-

able (although systematic errors do not necessarily occur fre-

quently—errors occur secondary to interactions that stress 

the system in some way). If the process of thinking is a sys-

tem, there are predictable systematic errors that occur dur-

ing processes of human thought, such as confirmation bias, 

failure to consider viable potential diagnoses (premature clo-

sure), and the like.

Life itself is a complex adaptive system of which each 

of us is a member, and as a result we must expect rules to 

change. Some rules, basic rules, do not change—the mecha-

nism of the hydrogen bond, for example. But strategic rules 

can change and we must be on our guard, continually look-

ing and assessing “outcomes” to see if we expect them or 

whether we ourselves should change a strategic rule.

It has been said we live in the Age of Information. We 

all think we know what that means—that there is “data, 

data everywhere.” We think that too much information is 

a relatively new occurrence. However, Gleich describes the 

observations of Oxford scholar Robert Burton who wrote in 

the year 1621 (that is to say about 390 years ago, and before 

Isaac Newton, David Hume, Pierre LaPlace, Bertrand Russell 

and the many others who have tried to make sense of our 

world and to understand it),

“I hear news every day, and those ordinary rumors 

of war, plagues . . . thefts . . . comets . . . of towns 

taken, cites besieged in France . . . Persia . . . daily 

musters and preparations . . . which these tempestu-

ous times afford . . . so many men slain . . . strata-

gems and fresh alarms. A vast confusion of vows . . . 

edicts . . . lawsuits . . . grievances are daily brought 

to our ears. New books every day . . . whole catalogs 

of volumes of all sorts . . . controversies in philoso-

phy, religion . . . Now come tidings of weddings . . . 

jubilees . . . sports . . . then again, as in a new shifted 

scene, treasons . . . enormous villanies of all kinds . . . 

new discoveries, expeditions; now comical then tragi-

cal matters . . . today . . . officers created, to-morrow 

of some great men deposed, and then again of fresh 

honors conferred . . . one purchaseth another brea-

keth: he thrives, his neighbor turns bankrupt; now 

plenty, then again dearth and famine . . . Thus I daily 

hear, and such like.”

Gleich continues, “Another way to speak of anxiety is 

in terms of the gap between information and knowledge. 

A barrage of data so often fails to tell us what we need to 

know. Knowledge, in turn, does not guarantee enlighten-

ment or wisdom . . . It is an ancient observation, but one that 

seemed to bear restating when information became plenti-

ful—particularly in a world where all bits are created equal 

and information is divorced from meaning.”

It seems that the only way left to us to make progress 

and to diminish the anxiety (felt by all diagnosticians) asso-

ciated with the glut of information is to leave the Age of 

Information behind us and to enter the Age of Context and 

Perspective. By entering the Age of Context and Perspective, 

“all bits” will no longer be equal and “information” will no 

longer be “divorced from meaning.”

Each of the Ages of Mankind have formed the foundation 

of the next age. Principles learned in the Stone Age persisted 

in the Iron Age and on up through the ages of Agriculture 

and Industry. Information will not disappear if we leave the 

Age of Information. On the contrary, “information” can only 

gain meaning and actually inform us if we use that infor-

mation “in context” and look at the information “from a 

variety of perspectives.” By doing so, we can make a decision 

that makes the most sense in the context of what proves to 

be the best perspective.



62 Essay  |  Dermatol Pract Concept 2012;2(4):12

As Minsky has reminded us, things that are easy for 

humans to do are sometimes difficult to study because they 

are so easy and it is not clear to us what we actually do, 

but by perseverance, we can make progress. A computer pro-

gram (Copycat), can make an analogy, one of the most basic 

cognitive tasks humans do. Scientists have figured out the 

mechanisms of some heuristics, reliable short cuts to making 

correct decisions quickly. Although we may not know how 

or why we know something, Simon avers that a mechanism 

exists, if only we look for it, and the technique can be taught. 

We can put our minds to the problem of using context and 

perspective more often and more appropriately, thereby 

improving our efforts at diagnosis. While Uncertainty can-

not be banished from our existence, we can ease the anxiety 

resulting from the gap between information and knowledge.

Summary

The process of making a diagnosis is a problem-solving 

activity carried out in the human brain by thinking recur-

sively using a strategy of “explore and exploit” under the 

condition of Uncertainty that arises from multiple sources. 

Required for success are a knowledge base, a set of reasoning 

skills, and the ability to obtain the appropriate data in the 

case of a specific patient. Context and the use judiciously of 

perspective are the keys to minimizing the anxiety that can 

overcome us in the cognitive gap between information and 

knowledge. Failures at diagnosis are due to systematic error 

and are both predictable in nature, if not in time, and avoid-

able potentially if one understands the process.  

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