Privileged Calta C.A.Gorry a Page Sl

witnesses, people or their actions, which would confirm the acute
rheumatic fever.

Did the attending Physician tell the patient he had acute rheumatic
fever? Did he treat the patient as though he had ARF? Did he ask the
patient the questions on would expect a doctor to ask if that doctor
thought the patient had ARF? Can the mother be found, and will her
recollections of the time in question prove more decisive?

A central question to ask is whether this behavior is typical of
experts in similar situations. -Perhaps this kidney expert reverts to
this behavior because the problem of acute rheumatic fever is out of his
domain of expertise. Will he use the same approach to a problem of
acute glomerulonephritis that occurred five years ago?

A cardiologist with whom we discussed this specific protocol, said
that he did not believe that he would have followed this line of
investigation. He felt he would have questioned the patient more
carefully about his remembrance of the symptoms. The cardiologist
conjectured that he would pursue this |ine because he was very familiar
with the symptoms of acute rheumatic fever. ,

If this were the case, then the difference in style would really
reflect a difference in knowledge. In other cases we have studied,
however, real style differences seem to arise. Some clinicians work
backward in time in that they move in arather strict line froma
problem to its antecedents. Others seem to move across all the problems
which occured at a particular time before moving back in time with any
one of them. Still other Clinicians seem to “jump around" quite a bit.

This study will proceed with these experiments, attempting to identi fy
differences in style, and to devise measures of the efficiency and
effectiveness of these style variations. We do not feel that important
new cognitive processes will be uncovered here that have been over looked
in the present i! iness project (although certain aspect of the process
may receive attention sooner). What will be different here wil! be the
characterization of the various Ways in which different clinicians
assemble and apply the building blocks of the present iliness. —

To bolster our ability to maximize what we learn from this study, we are

planning to include a cognitive psychologist in our group’ for
consultation on issues of cognitive style.
Privileged iim G.A.Gorry qu» Page S2

The Formalization of Clinical Knowledge
Principals
Or. Jerome P. Kassirer
Ann 0. Rubin
Professor Gerald J. Sussman

Introduction

One of the obvious problems facing researchers in computer-aided
clinical decision-making is how to identify and codify the knowledge
which is relevant to a given clinical area. In the present illness
project, we face this problem, but we have chosen to skirt same of the
major (and difficult) problems of codification and representation in
order to rapidiy push forward into the process of the present illness.
In this project, we are taking a more careful fook at the problem of
identifying and coding expert knowledge in an orderly way. This problem
is difficult for several reasons:

1) It is often unclear, even to the expert, exactly what knowledge he
uses in a given situation.

2) For many clinical problems, there seems to be a very large amount
of knowledge which is relevant (at last potentially) .

3) Much of the knowledge seems to be very diverse, consisting of
pieces of knowledge which are quite diverse in form.

These problems make the development of a concise, orderly way for
representing clinical knowledge very important.

Above we commented on the limitations of previous formalisms for
representing clinical knowledge. Basically, each has its virtues, and
each can be fruitfully applied in certain circumstances; but none is
sufficiently flexible- and powerful to cope with the diversity and
complexity of clinical knowledge.

The most obvious example of an attempt to deal with this problem of
organization and .presentation is a book abut a particular clinical.
problem. Although the book serves certain purposes weil, it is
inadequate in many respects. First, a book is an intrinsicatiy linear:
form. That is, the author must choose a central theme around which his
facts or opinions must be organized. Consider the following passage
from a chapter about acute glomerulonephritis. {13}

"Typically the illness with pharyngitis or tonsillitis
accompanied by fever and malaise. Whether or not specific
antibiotic therapy is given, respiratory symptoms and fever
disappear after a few days, and the patient feels entirely
well. One or tuo weeks after the onset of the illness,
weakness and anorexia return, and the patient notices that
his urine is scanty in amount and smoky in appearance. Upon
Privileged Gt 0.4. Corry Page S3
4.
ds

awakening the next morning, he notes swelling around the
eyes and complains of shortness of breath and headache.”

The text continues in this vein with a discussion of the remainder of
the scenario for the "classic" patient with acute post-streptococcal
glomerulonephritis. Later in the chapter, in a discussion of clinical
features of the disease, we find:

"Gross hematuria, one of the most common initial symptoms,
occurs in more than one-third of the patients. The urine is
often described as reddish-brown, smoky, rusty, tea-colored,
or cloudy. In most cases, gross hematuria disappears. after
@ few days, but it may continue for one or two weeks.
Microscopic hematuria can, of course, be found for a much
longer period, and often persists even after significant
proteinuria is no longer present."

In the first quotation, it is clear that the authors have chosen to
organize the information they are presenting around the time course of
the evolution of the disease in the "classic" patient. The discussion
mentions a number of signs and symptoms, but only in passing. The
objective is to provide a coherent picture of the course of the disease,
and too much attention to details will obscure that picture. There can
be only one major line to the discussion at one time.

In the second quotation, the focus of attention has been shifted to
hematuria, one of the ‘details’ of the earlier discussion. Now much
about hematuria that was passed over in the first discussion is
presented. In this discussion, proteinuria is treated as a detail, but
later in the chapter, it, too, becomes a main theme around which other

facts are organized. In fact, in that discussion, hematuria is treated
as a detail.

The point is arather obvious one, but it is very important. The
conventional presentation of information in a book places a real
cognitive burden on the reader. The reader must organize the
information in his memory, and he must create the associative links
implicit in the text. For example, he should associate the ‘smoky
urine’ of the first discussion with the ‘smoky urine’ in the hematuria
discussion. Links must be formed from the details of the first
discussion to more extensive knowledge structures about these details.

For knowledge such as this to be clinically useful, it must be digested
by the clinician. The demands of the clinical environment are such that
the linear organization (as in the book) is inadequate. At a minimum,
the clinician must be able to access this knowledge from the "entry
point’ of the patient’s presenting problems (e.g. smoky urine) and from

the entry point of particular disease hypotheses (e.g. Does the patient
match the picture of AGN?),
Privi aaa C.A.Corry > Page S4

A second cognitive demand which information presentation such as this
places on a reader is the need for re-coding. Clearly. the clinician
does not remember such text verbatim. His memory of it is coded in
terms of a (perhaps very ltarge) number of symbolic structures. Part of
this re-coding probably is essential if he is to remember the material;
another part probably is idiosyncratic and helpful in efficiently
retrieving the facts contained in the material.

Although our knowledge of these matters, particularly with respect to
details of the mechanisms involved, is limited, our interest in gaining
an understanding of these questions is very great. Few would argue
against our contention that knowledge such as that presented in the
quotes from the chapter is an essential ingredient of clinical
exper tise. It is alse certain, that such knowledge is not organized in
the expert’s memory the way it is organized in a book.

We have undertaken a research project aimed at the identification of
the knowledge structure of an expert in a particular area of clinical
medicine, the differential diagnosis of hematuria. The advantage of
working with an expert is that he has already digested materia! such as
that cited above and he has organized it in a way which is clinically
useful (at least to him). By working primarily with him, and
supplementing this work with studies of books and papers such as the one

mentioned, we can proceed most efficiently and effectively. Our goais
are several:

1) First, we want to catalog what the specific knowledge is.

2) Second, we want to-‘understand how much knowledge is required for
expert performance in this problem.

3) Third, we want to develop a formalism for representing this
knowledge including the appropriate associations.

4) Fourth, we want to understand how this knowledge is employed by
the expert to solve clinical problems.

This project is closely related to the present itiness project
discussed above, and it is also closely tied to the efforts to develop
good computer representations of medica! knowledge which we will discuss

belouw. Further, we expect these projects to move in close concert in
the future, with a major activity of the Laboratory centering on the
merging of fruits of these efforts.

For the near future, however, we feel that by maintaining different
emphasis in these projects, we can best bring the research issues into
focus. Continuity and cooperation among the projects will be maintained
by the participation of key researchers in more than one project each.
Privileged Communication C.4.Corry a, Page SS

Preliminary Work

To gain a better understanding of the knowledge possessed by an
expert about the problem of hematuria, we undertook a series of
experiments in what we called "CPC mode". Each experiment consisted of
presenting a case from a Clinical Pathology Conference to a clinician.

The CPC was presented to him one fact at a time. After each fact was
given to him, he was asked to discuss the "meaning" of the fact. The
meaning of the fact to him included the immediate conclusions which he
could draw from it, its effects on hypotheses currently being
considered, its suggestions of new hypotheses, etc. He was questioned
in detail to make certain that the observers understood the reasons for
his interpretation of the fact. When a satisfactory understanding of

his reaction to the fact had been obtained, another fact was given to
him, and the process was repeated.

From the observations of several such sessions, a first representation
of the inferred knowledge base was constructed. This was discussed in
detail with the clinician, and he was able to make many alterations and

suggestions for additions. The knowledge structures discussed below
result from many iterations of this process.

There are certain problems which arise during this kind of observation
of behavior. Most are minor. One problems is that the clinician
generally finds this mode of information acquisition somewhat
uncomfortable and unnatural. Another problem is that it is sometimes
necessary to ask him questions to clarify the details of his response.
This raises the possibility that the clincian may alter his behavior in
response to the additional questioning.

In addition, there is a question as to the validity and completeness of
introspective statements concerning the knowledge emp | oyed. Even if we
acknowledge all these problems, however, we still can report that these
experiments were very successful. From them we gained new insights into
the. structure of clinical knowledge, and we gained some new ideas about
how to represent this knowledge and its structure.

Consider the diagrams in Figure 6 and Figure 7. These are slices of
clinical knowledge, the first organized about the central concept of
renal infarction; and the second, about pyelonephritis. These slices
are typical of the large mumber of such diagrams which have been
constructed during the course of this project. The purpose is to
identify and structure a sufficient amount of knowledge about a given
problem (here, hematuria) to form the basis for a. program to do
differential diagnosis.

As is apparent from these sample diagrams, the same problems of
organization of information remain. The construction of such slices
requires the selection of a central theme.
BILATERAL

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Privileged Communication G.A.Corry > Page S8

As in the textbook examples above, there are many ways to “slice” the
knowledge which is relevant to the problem of hematuria. We have
allowed the clinician to make these slices in what ever way seems most
natural to him. Our emphasis has been on encoding each slice in an
orderly and clear way. This is the reason for the graphical form we

have chosen - clinicians seem to be able to work with this form
comfortably.

We still face the problem of relating all these slices to one another.
We plan to do that in the computer. A program for accepting these
slices (in some form). and making all the proper associations to link the
slices together will be produced. This program will be based on the
GOBBLE system we have developed and which is discussed in a later
section. The network of concepts which results from the assimilation of
these slices by this program will serve as the knowledge base upon which
programs for differential diagnosis can be constructed.

We should note here that the construction of even rudimentary programs
for diagnosis is an important step in obtaining the clinical knowledge
in question. We have found, however, that only part of the knowledge
possessed by an expert can be elicited from him in a direct manner. An
additional component of this knowledge can be identified only through
interaction with a computer program which makes decisions based on the
knowledge which he has already cataloged. We found this to be true in
our work on decision analysis, and we are finding it true here. After a
certain point, the clinician must see someone (in this case a program)
do something with the knowledge in order to see whether it is complete,
has been understood, etc.

Because of this, we have started to build an interface through which
clinicians can interact with a knowledge base of these slices and some
rudimentary diagnostic programs. The purpose is to identify places
where there are gaps or errors in these slices, and in the process, to
learn something about diagnostic process. The interface will permit the
clinician to use a subset of English (see the discussion of this in the
section on computer science research) to ask questions and to get simple
explanations of knowledge in the slices. He will also get explanations
of the way in which the diagnostic programs used this knowledge in
making decisions. Further, the clinician will be provided with
facilities for recording complaints, suggestions, etc.

By making this interface simple and direct, we hope that we can get
clinicians other than those working in the project to help us build this
knowledge base.. Further, such an interaction may encourage some of

these clinicians to become more actively involved in the efforts of the
Laboratory.

In addition to thie work, we are currently analyzing protocols of
differential diagnoses of hematuria to see if the stices we have
identified are adequate representations of the knowledge employed by the
clinicians. This activity is useful, because we can “hand simulate" a
Privileged

 

G.A.Gorry > Page S9

diagnostic program which uses the slices, and thereby learn quickly
whether our basic concepts are sound. More detailed studies, using
computer programs, will, be required in the tong run, but. these
experiments should prove very valuable in the short run.

Model-Based Decision Making Project
Principals
Professor G. Anthony Gorry
Dr. Stephen G. Pauker
Howard Silverman .

Introduction

For a number of problems of clinical medicine, there exist formal
models upon which decisions can be based. In these cases, it is
sometimes true that the best decisions are made through a dependence on

the model. The reasons for the superiority of the model-based. decision
may be several.

First, the relevant physiology or pathophysiology underlying the
problem may have been modeled with precision surpassing that which the
clinician can maintain in his own, less formal model. In some cases,
the clinician’s model is inferior because it fails to account for
certain details of a process. In other cases, the clinician cannot (or
will not) do the computations required to achieve the accuracy of the
formal model. In still other cases, the clinician does not know the
parameters of the system with sufficient precision to make predictions
of system behavior which are as good as those of the formal model.

In any event, there are situations in which models (perhaps coupled
with automated decision making procedures) can outperform the average
physician, and in certain cases do better than even the best physician
in solving particular problems. Examples which come to mind are acid-
base chemistry and the administration of antibiotics. ,

In general, the problem domains in which models such as these have
been successful share an important characteristic. This is that the
clinical problem can be dealt with in isolation from the most of the
other problems which the patient might have. This does not mean that
the model (or computer program based on the model). does not consider
aspects of the patient’s condition other than the particular problem in
question, but rather that the number of such considerations is small,
and in toto these problems can be rather neatly circumseribed. Of
course, it is rather obvious that this property great!y increases the
likelihood that such a model can be developed.

There are other clinical areas where models exist, but a variety of
factors which are not (or perhaps cannot be) incorporated in the mode!
are relevant to the decisions required in the clinical area in question.
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G.A.Gorry a> Page 6@

Here the clinician wishing to use a program based on the model
encounters some difficulty. First he may know certain facts about the
current clinical situation which he would like to combine with the
program’s results. The program cannot accomodate this additional
information. This is to be expected; not all models can incorporate
all potentiatly relevant factors. The problem is, however, that the
physician is not sure how to combine his judgments with the results of
the program. For example, exact!y how did the program arrive at its
conclusion? What assumptions was it making? Oid it already include
consideration of some of the information he is consider ing?

In some circumstances, the program could produce packaged responses
to standard questions which would satisfy the clinician. If they do
not, then it is not clear what he should do.

Of course, an ideal solution from the clinician's point of view is
for him to have access to a consultant who understands the program and
the model on which it is based. Then when questions arise, or when the
clinician simply wants to learn some more about the model, he can go to
the consul! tant. The consultant will understand the language and the
background of the clinician, and he will know how to make his
explanations understandabie.

Now the reader may easily guess that we would propose that. the
program become the consultant. The program should know much more than
how to compute the model. It should know what the mode! is, how it was
developed, and what relatian it has to the problems facing the users
(clinicians). Such a program, of course would have to possess a great
deal of knowledge. It Would need the knowledge of the consultant
described above. Before we discuss this possibility and the research
problems involved further, let us offer another argument for trying to
build programs which are “knowledgable” about madels.

We noted above that various models have been developed which now
serve as the basis of decision-making programs. In several instances,
these programs are real clinical successes. If we look to the future,
we can see the need to bring a (potentially large) number of such
programs together in a common system. Such a system will need a great
amount of administrative knowledge as we discussed above. One aspect of
that knowledge will meed to be knowledge about these model-based
programs. In general, the administrator of the system will need answers
to ail the questions posed by the clinician above. (What assumptions
have been made in this program? Are its assumptions compatible with the
clinical situation? With the assumptions of a second program which wil!
be used?, etc.) If programs such as these are to be marshalled together
in some clinical situation, questions such as these become paramount.
The major research problem is how to insure that some supervisory system
can get answers to these Questions when it needs them.

For these reasons, we have undertaken the study of model-based
decision making. Specifically we are studying situations in which a
Privileged ris, G.A.Gorry a> Page 61
model is relevant, even central, but not all-inclusive. In these
situations, the best decisions are made by clinicians who are experts in
the area and well acquainted with the model in question. We want to
buitd a program which is really adn expert in the domain in question (and
generally this domain is very limited). With the model as a core, the
program would possess a knowledge base which encompassed al! the facts
and procedures use by the expert in his work with the model.

In addition, the representation of this knowledge would be such as to
support an inquiry and explanation facility which was natural and direct
for a clinician, and this representation would also facilitate the
supervision of the model by some higher level program monitoring the

overall clinical strategy. Finally, this representation scheme would be
suitable for a variety of different models.

These efforts directed at developing the technology for such programs

and models will be discussed below in our section on representation
research. ”

The specific problem we have chosen for our initial project in this
area is the administration of digitalis-digoxin. We now turn toa
discussion of this problem. ©

The Digitalis/Oigexin Therapy Advisor

The clinical use of digitalis preparations has been one of the
classical skills of the experienced clinician. Although this drug is
often life-saving, its proper administration is difficult and requires
careful clinical judgment.. Digitalis possesses arather tow toxic-

therapeutic ratio, and signs of under-digitalization are often very
similar to signs of toxicity.

There have been several recent advances in clinical biochemistry and
pharmokinetics which have significantly altered the use of this drug,
and much of this mew technology and knowledge is now available to
clinicians throughout the country. However, administration of this
class of drugs stil! remains a significant clinical problem, and we feel
that the availability of a knowledge-based system concerning the cardiac
glycosides may be of additional clinical use.

Background

Use of the foxglove began several hundred years ago, but until
recently techniques of administration have changed very § little.
Withering’s original advice was to administer the drug until signs of
improvement or signs of toxicity occurred, and that remains the
cornerstone of digitalis therapy today. Problems arise, however,
because the signs of toxicity can often be confused with signs of
insufficient drug dosage, and mistakes can be costly since the first
Sign of excess drug administration can be sudden death. The clinical
signs of digitalis excess are cardiac (disturbances of cardiac rhythm)
Privileged eae, C.A.Corry > Page 62

and extra-cardiac (nausea, vomiting, anorexia, visual changes), but the
dangers of excess drug are by and large cardiac. The extra-cardiac
signs are helpful if they occur before the dangerous cardiac

manifestations of toxicity and if they are predictive of those more
serious toxic problems.

Quite often, however, the first hint of excess drug desage is a
potentially serious disturbance or cardiac rhythm The: interpretation
of these arrhytmias is often jess than straightforward. The same
arrhythmia can often be a sign of either under- or over-digitalization.
For example, ventricular premature beats may be caused by digitalis
toxicity or by. congestive heart failure (by enlarging the heart and
stretching its conduction system). In the case of under-digitalization,
administration of more drug might suppress these extra beats by
decreasing heart size. However, if the ventricular premature beats were
indicators of early excess digitalis effect, then the: slight increase in
drug dosage. could easily lead to a fatal arrhythmia.

In addition to this complex problem of recognizing toxicity, there are
other complicating factors in using digitalis. A: variety of myocardial
' processes (varying from myocardopathy to acute myocardial infarction)
make the heart more sensitive to cardiac glycosides: and thus make
toxicity more likely to develop. In addition, there are non-cardiac
problems which alter sensitivity, including thyroid dysfunction,
electrolyte imbalance,. hypoxemia, acidosis and the like. The astute
clinician is continually. aware of these factors and tries to. adjust his
dosage to what he judges the patients clinical state. ta. be.

Recent Advances

Jelliffe {14}. and Doherty (15} have demonstrated a variety of kinetic
factors influencing the amount of active glycoside available to the
myocardium after a given dose. These factors include variation in
absorption, distribution and excretion of the drug.. Because: the drug is
usually given over a relatively short dosage cycle (once or twice daily
down to every other day or so) compared to its in vivo half life (for
digoxin 1.6 days: and up; for digitoxin and digitalis leaf 6.@ days and
up), there is an exponential accumulation of body stores. Therefore
changes’ in excretion and absorption can have a marked influence on body
stores. For example, administration of digoxin to aman with normal
renal function in a dose of @.25mg daily would give body stores of
roughly @.625 mg at equilibrium, whereas if the patient had moderate
renal functional impairment ( a stable creatinine of 2.Smg%) his body
stores would be approximately 1.25mg. With a drug of such a low toxic

therapeutic ratio, variations of this magnitude are potentially
dangerous.

Other studies{16}]} have shown variation in the bio-availability of the
drug from patient to patient and from brand to brand. This naturally

limits the usefulness of a model which only deals with distribution and
excretion.
Privileged yr G.A. Corry SD Page 63

Direct measurement of serum drug levels have recently become fairly
common. The assumption that these serum levels bear a reasonable
correlation to myocardial levels seems to have been borne out
clinically, in that these serum measurements can, on the average,
predict the occurrence of drug toxicity. However, we have already
mentioned that sensitivity and toxic threshold varies from patient to

patient in different clinical settings, so serum levels can only serve
as a rough guide.

The State of the Art

What, then, is the behavior of the cardiologist today with respect to
the administration of digitalis? He first tries to establish that the
drug is indicated, and depending on the indications, decides on how
rapidiy the patient must be digitalized (loaded with the drug to reach
equilibrium levels). . He then selects a preparation whose kinetics fit
these objectives. Most cardiologists next decide on what maintenance
dose they would tend to use in this setting (based on those factors
which influence sensitivity to the drug), although they might
equivalently select a serum or body store level to fit the situation.
The loading and maintenance schedules are then determined based on the
patient’s renal function and fat-free body mass.

This program is then begun, with careful, frequent examination of the
patient for signs of beneficial effect and toxicity. Depending on
patient response to his initial program, the cardiologist modifies his
plan. If the patient demonstrates either early, unexpected signs of
toxicity, or fails to demonstrate clinica! response at reasonable doses,
the physician may then obtain § serum drug levels to clarify the
situation. For the vast majority of patients on digitalis preparations,
serum levels are used either as a guide in confusing situations or as a
source of comfort to the physician. It is still ultimately the

patient’s clinical response to the drug that dictates changes in
therapy.

When faced with a patient who requires therapy with digoxin and who ia
undergoing changes in renal function, the physician uses both the
parmacokinetic models and serum drug level measurements, The model is
used to prospectively adjust dosage to reasonable ranges, and then this
is "fine-tuned" retrospectively by clinical observation and drug level
determinations. In this situation, the pharmocokinetic model assumes a
central importance. One might imagine the physician selecting arbitrary
dosage plans and tuning them by clinical response and serum drug levels.
Although this technique might arrive at the same end-point, it would
make it more likely that the patient would be exposed to toxic levels
for some brief period. Since toxicity can be fatal, a predictive
approach, using the model. is preferable.

Current Computer Approaches
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G.A.Gorry Ga» Page 64

Jelliffe and others have developed computer implementations of var ious
kinetic algorithms which modify suggested administration schedules for
renal function (stable or changing), body size and. route’ of
administration. -©These programs also allow for the smooth transition
from one preparation to another with differing pharmocokinetics.
Studies have shown {15} that availability of these programs can make a
significant difference in the incidence of digitalis intoxication.
Sheiner has added the feature of feedback data based on measured serum
level to further adjust dosage for the individual patient. However, a
recent study by Peck {17} failed to demonstrate a significant difference
in the performance of expert physicians given access to computer-
predicted schedules with serum level feedback, when compared.to similar
physicians not having access to the program. This suggests that the
expert physician already uses the gross prediction algorithm, and that a
significant part of his “expert” behavior centers about the tuning of
his predictions based on clinical observation of patient response.

 

Qur Approach

We propose to implement a knowledge-based digoxin dose advisor, which
uses the generaily available pharmacokinetic models for its initial
prediction phase, but which also has the ability to guide the non-expert

physician through the feedback loop of adjusting drug dosage based on
clinical response. We would hope that this program might better allow
the non-expert to model his behavior after that of the cardiologist, and
that interaction with such a program would both improve his treatment
for the individual patient and teach him the principles of sophisticated
drug use. We feel that this goal can be accomplished because the use of

this drug constrains us to a fairly circumscribed, well-defined group of
clinical settings.

The development of a program to predict dosage based on age, body size
and renal function has already been accomplished in many centers, and we
have such an implementation currently available. This system will first
determine why the drug is being given (arrhythmia, congestive heart
failure, prophylactically) and also look for any factors that might
predict increased patient sensitivity. Based on these determinations,
it will establish a desired speed of approach to equilibrium. With this
factor and knowledge of patient size, age, sex andrenal function (as
estimated by whatever parameters are then available), it will suggest an
initial loading and maintenance schedule.

The physician will then be encouraged to interact with the program,
prior to administration of each dose at first, and later, at intervals
throughout the equilibrium phase. The program will guide his search for
cardiac and extra-cardiac signs of toxicity and will collect data about
clinical effect. We do not propose that the program will directly
interact with the patient’s electrocardiogram in search for
manifestations of effect or toxicity, but rather will ask the physician

about specific features of the EKG. For the marginally experienced
Physician a set of labeled examples wil! be provided. Based on this
Privileged aaa C.A.Corry > - Page 65

information concerning patient response, the program will suggest
modifications of drug schedule.

If the situation becomes confusing or if unexpected effects are
observed, the program will have the ability to ask for and use data
about serum drug levels. We would also envision this program to be
useful in dealing with a patient already receiving digoxin or digitoxin,
but whose response is either troublesome or requires confirmation.

Oesling with Discrepant Information

Principals

Prof. G. Anthony Gorry
Or. Jerome P. Kassirer
Or. Stephen G. Pauker
Or. William 8. Schwartz

Intraduction

In the above discussion, we have emphasized the rapidity of the
focusing which clinicians do during their interactions with patients. -
Our observation of clinicians at work has caused us to view them as

‘rather aqgressive with respect to hypothesis construction and testing.
Because they assume this aggressive posture in. their problem solving
activities, they frequently confront situations in which new facts are
in conflict with their working hypotheses. An important aspect of
expert performance is the facility with which the expert can respond to
these instance of discrepant information. .

In some cases, the problem is readily apparent: two pieces of
information are clearly contradictory. For example, he may be told that
the patient has no hematuria but he does have red blood cell casts.
Except in the rarest of circumstances, these two statements are
contradictory because hematuria is a prerequisite for the formation of
red blood cell casts. So the clinician has the obvious choice of

assuming that there really are red blood cell casts and the hematuria
was overlooked, or there in fact is no hematuria and the red blood cell
casts are illusory. In accepting either alternative, he must account

for the implied error.

In other, more complex situations, a fact may not directly contradict
other facts, but the acceptance of the new fact by the clinician may
cast serious doubt on one or more hypotheses he is maintaining. For
example, suppose that the findings support the hypothesis that the
patient has idiopathic nephrotic syndrome. Assume that the records from
the hospital to which the patient was admitted before being transferred
to this hospital show that his serum creatinine was 1.8 mg. per. cent.
tuo weeks ago. The same test run today in this hospital yields a value
of 7.6 mg. per. cent. Clearly the acceptance of these two values as
accurate measures of the patient’s renal function requires the
conclusion that the patient is suffering rapidly progressing renal
Privileged Sin C.A. Corr Page 66

failure. On the other hand, patients with idiopathic nephrotic syndrome
almost never suffer rapidly progressing renal failure, and so there is a
significant discrepancy between these values taken together and the
hypothesis concerning the underlying disease. Of course the hypothesis.
of idiopathic nephrotic syndrome can be rejected, or one or both of the
serum creatinine values can be dismissed, but either course wil! require
new hypotheses to be generated and melded into the overall picture the
clinician has of the patient.

The problem of dealing with discrepant information is a common and
important one for clinicians. The strategies which experts use to solve
these problems are not well understood at present. Nonetheless, a
number of observations can be made which can serve asa basis for
further research and discussion. The importance of this investigation
should be underscored, because without the capability to deal with
discrepant information, a computer program cannot success in the face of
the complexities of real clinical situations.

Recognizing Discrepancies

The recognition of a contradiction always is conditioned on some
assumed state of knowledge about the world. For example, the fact that
the hematuria-red blood cell casts situation mentioned above constitutes
a contradiction ts based on physiological knowledge about the formation
of these casts. In other cases, a contradiction is recognized as such
oniy on the assumption of a hypothesis about the disease state of the
patient. The only difference in these two situations is the degree of
certainty the clinician possesses about the state of the world. In the
first case, he is so certain of the physiological mechanisms involved
that he only considers the possibilities that the hematuria has been
missed or the red cel! casts are spurious. In the second case, he might
also consider the possibility that his hypothesis about the underlying
disease state is in error.

For convenience, we recognize three types of assumed states of
knowledge about the world:

1) physiologic knowledge,
2} hypotheses about the disease state of the patient, and
3) common sense knowledge.

These categories of assumed knowledge are not precisely defined, nor are

they exclusive, but they do provide a rough cut at the bases on which
contradictions are recognized.

For any of these states of knonledge, different situations can
produce contradictions. We have identified a@ number of. these
situations. For example, these five situations can occur conditioned on
the acceptance of knowledge of one of the three kinds suggested above.

1) More than one of a set of mutually exclusive alternatives are
asserted to he true. (for example, a patient is said to have
normal renal function, but the radiologist reports that KUB studies
Privileged aa G.A.Corry Page 67

show no kidneys.)

2) A state of the world is asserted, but one or more prerequisites for
that state are denied.

(The hematur ia-red blood cel! cast example above)

3) A "cause" is asserted, but one or more of its certain “effects” are
denied. (For example, it is believed that decreased renal function

is the cause of observed hyperkalemia, but the patient’s serum
creatinine is normal.)

4) A measurement exceeds absolute or experiential limits.

5) The rate of change of a physical state exceeds absolute or

experiential limits (For example, a patient claims to have gained
48 pounds in one day).

Contradictions are most easily recognized when they violate
principles or facte which are known to be always true. When the known
principles or facts are conditioned on the acceptance of a hypothesis,
the contradiction can be asserted only on the assumption of the
under lying hypothesis. For example, in the example of the patient with
apparent rapidly progressing renal failure, the discrepancy is not
absolute; there are many examples of situations in which such acute
renal failure can occur. It is the acceptance of the hypothesis of

tdiopathic nephrotic syndrome which produces the conditional
discrepancy.

A complicating factor in the identification of discrepancies is that
they need not be direct. Inferences drawn from one fact may contradict
those drawn from another. Here it is required that the contradiction
itself be recognized, but in addition the original facts which triggered
the contradictory deductions must be identified as discrepant. Further,
such indirect discrepancies may arise through chains of deductions
conditioned on various hypotheses.

As a small example of this kind of problem, consider a patient whose
presenting signs and symptoms suggest a cardiac problem. Fur ther
suppose that the patient tells the doctor that when he was a young boy
he was treated for a "heart murmur" by his family physician. This
latter fact strengthens the physician’s belief that the patient’s
problems are the result of heart disease, in particular heart disease of
long duration. Then in passing, the patient mentions that he served in
the army during the Korean war. This fact is discrepant with the
hypothesis that. the patient’s current heart disease is a progression of
his childhood problem. If he served in the army, then he passed an army
physical exam. Such an exam probably would have revealed his heart
murmur (especially if it was loud), and he would not have been accepted.
Further, it can be presumed that he had a reasonable exercise tolerance,

and this too argues against the assumption of long-standing heart
disease.
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C.A. Corr ui | Page 68

 

How Experts Oeal with Discrepancies

As might be expected, experts use a number of approaches in their
attempts to resolve discrepancies during the diagnostic process.
Basically these approaches can be divided into three categories: 1)
doubting or dismissing one or more of the stated facts; 2) constructing
alternative relationships or connections among the discrepant facts
which make the discrepancy only apparent, not real; and 3) revising or
dismissing an underlying hypothesis about the disease state of the
patient. The choice of a method for dealing with discrepancies in many
cases is dictated by specific real world knowledge. In other cases,
although there is a certain amount of specific knowledge concerning the
situation in question, the clinician must fall back on more general
problem solving strategies.

One point ts worth noting here, because it seems to be
characteristic of the approach used by experts. When confronted by a
situation in which several facts appear to be discrepant, the expert
makes a specific choice of explanations which resolve the discrepancy.
If tater facts cause him to discard this explanation, he will return to
select another explanation if possible. Further, if his explanation
appears to be confirmed, he will make at least a cursory check of the
alternative explanations to make certain he is correct. He does not,
however, attempt to process alternative world views fone in which one
fact is assumed to be in error, another in uhich a second fact is
assumed to be incorrect, etc.) itn parallel. When discrepancies arise,

they are almost atways dealt with directly, and a specific explanation
is constructed.

In order to indicate some of the richness of the information used to
resolve discrepancies, we offer two real medical problems, and we will
identify the Knowledge used by the clinician to construct an explanation
of the way in which the problem arose. The first ts relatively easity
resolved; the last is considerably more complex.

In many instances, a problem arises because of a simple factual
error. An example of such a problem is given above in which it is
asserted that there are red blood cel! casts but no hematuria. Here,
because of the physician's firm belief in his understanding of the
pathophysiological mechanisms involved, he must reject one of these
facts. The physician clearly would like to have the urine studies
repeated in order to resolve the problem; but in certain cases, the
facts are historical, and no further information can be gathered. In
this case, the clinical’s knowledge of the relative likelihoods of error
will determine his choice of explanation. Many more mistakes are made
in the detection of red blood cell casts than in the detection of
hematuria, and so he would proceed on the assumption that the patient
had neither hematuria nor red blood cell casts.
The more complex situation is the case of the patient cited above who
was thought to have idiopathic nephrotic syndrome. Recall that a
problem arose because two measurements of serum creatinine taken two
weeks apart indicated rapidly progressing renal failure. Here we have a
conditional contradiction, in that the development of renal failure in
patients with idiopathic nephrotic syndrome is insidious. Hence, the
clinician must resolve the situation, perhaps at the expense of the
hypothesis of idiopathic nephrotic syndrome.

If the other evidence favoring the hypothesis of idiopathic nephrotic
syndrome is quite strong, then the natural inclination of the clinician
will be to doubt the evidence’ for rapidly progressing renal failure.
The simplest way to do this is to attribute the problem to a- simple
factual error. Either the serum creatinine done at the other hospi ta!
or the one done here is in error.

Of course, it is a simple matter to repeat the test in this hospital,
and to make the situation interesting, let us assume that repeating the
test yields the same result. So the clinician. now knows that the
patient is in renal faiture. The question of the rapidity of its onset

remains, however, and the lab test result from the other hospital
becomes suspect. ,

Now in trying to ascertain the validity of a test result from the
past, the clinician faces a different problem. Obviously, the test
cannot be repeated; the only avenue open to him is to gather other
facts about the patient, and to consider whether they are consistent
with the result in question. For example, if an x-ray of the kidneys
was taken at the first hospital and the physician has access to it, it
may cast some light on the problem.

If the x-ray shows that the kidneys are small, then it is reasonable
to assume that the serum creatinine measurement from the first hospi tal
Was im error, because kidneys of reduced size indicate a renal problem
of relatively long duration and severity and atrophy of the kidneys
takes a year or more with chronic renal failure (except with renal
infarction). This in turn is inconsistent with normal renal function
(as indicated by the lab test).

If the x-ray shows normal-sized kidneys, then the validity of the lab
test cannot be determined in this Way, because although peopie with
kidneys of normal size usually have normal renal function, when disease
is present, impaired renal function will precede atrophy of the kidneys.
Therefore, the patient could have been in renal failure during his stay
in the first hospital (the tab test is in error) and the x-ray of the >
kidneys would show normal size.

For the purposes of our example, fet us assume that attempts such as
this to ascertain the validity of the first serum creatinine all fail,
‘and the clinician is left with the tuo values which are inconsistent
With his diagnosis of idiopathic nephrotic syndrome. There is another
Privi \eced @oreeau, GC.A. Corry Page 78

way he can try to resolve the conflict, namely by retaining the.
diagnosis, and trying to show that the presence of renal failure is not
a direct consequence of severe damage to the kidneys. This requires
some rather specialized, expert knowledge on his part.

If the patient is losing enough protein in his urine, he can become
hypovolemic. The mechanism for this involves a severe reduction in his
serum albumin with an accompanying reduction in blood volume. This
reduced blood volume in turn can cause a reduction in the glomerular
filtration rate which is sufficient to produce a markedly elevated serum
creatinine concentration. Experience indicates that only under special
circumstances can this occur, but when it does, it produces elevations
of the serum creatinine which can be mistakenly interpreted as the
result of severe structural renal damage.

The expert knows the limits of proteinuria, hypoalbuminemia, and
serum creatinine which are consistent with this mechanism. He can match
the patient’s findings to these limits in order to test this hypothesis.
Further, he knows that if this mechanism is operative, the patient
should manifest low blood pressure (at teast posturally), and so he
would use blood pressure as evidence for or against this hypothesis.

Of course, the third possibility which the clinician should consider
is that his original hypothesis of idiopathic nephrotic syndrome’ is

incorrect. To follow this route, however, probably wil! require a major
reorganization of the facts in his mind in order to fit them into
another framework. Whether he is willing to make this reorganization

will depend on the success of the approaches described above, and the

strength of his belief in’his diagnosis based on the totality.of the
facts in hand.

Reasoning of this complexity is often required in difficult clinical
situations. We plan to undertake some studies of the way in which
clinicians deal with such complexity. At present, we see aspects of the
problem of discrepant information throughout afl our work with
clinicians, but our work has not produced a single, coherent project.
We have raised the problem of discrepant information here however,
despite our rather vague plans for dealing with it, because we realize

its importance, and we plan to initiate an effort focused on it as soon
as possible.

Research on Osaling with Discrepancy

In the absence of a specific research plan, we will suggest a number
of goals we hope to achieve with the work we will initiate in this area.

1) How Are Discrepancies Recognized?

A problem which we will face immediately is that of finding a good
characterization of discrepancies. What exactly constitutes a problem
of this type? How does a clinician recognize such a problem?
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G.A.Corry SD . Page 71

This problem is more difficult than it appears at first glance.
Consider, for example, the addition of a SINGLE fact to a knowledge
base. How should this fact be "tested" to see if it contradicts one or
more facts already accepted. Does a clinician test the incoming fact
with every fact he knows? With every fact he knows about the patient?

If he uses only some of the facts he knows, how are this subset
selected?

The “obvious” answer to this last question is that he tests the new
knowledge only against existing knowledge which "relates" to it. But of

course, this simply avoids the issue; how do we measure "relatedness"
in a meaningful way?

This problem of recognizing discrepant information is really a
difficult one. A great deal of effort will be required to solve it.
Our immediate goal is to first develop a theory of how potential
conflicts among facts and hypotheses are recognized. This work will
involve not only introspection and protocol analysis, but also it will
require some innovations with respect to the ways we have for

representing knowledge in a computer. Thus this work will interact with
the work on GOBBLE discussed belon.

Although we do not know now how this effort will develop, we think
that it most likely will involve. the detailed study of a number — of
clinical examples. These studies may be augmented by studies of the way
people recognize discrepancies in situations other than clinical ones.

2) How Are Discrepancies Dealt With?

Once a discrepancy has been recognized (at least tentatively), the
clinician must deal with it (if onty by ignoring it). We will study the
way in which clinicians deal with discrepancies using our basic approach
of protocol analysis and intervieu. The result of this effort will be
the description of a number of the strategies they use, and the

characteristics of the situations in which these strategies are
emp | oyed.

These strategies will be tested by simulation, and their efficacy
will be considered in various clinical situations. As soon as possibie,
we Will begin to integrate the work on conflict identification with this

work. It should be noted, however, that both these efforts can proceed
in parallel at the outset.
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Supporting Computer Science Research

Principals

Professor G. Anthony Gorry
Kenneth Kahn

Peter Miller

Dr. Stephen G. Pauker
Charles Safran

Howard Silverman

introduction

In the projects discussed above, the present illness project, the
formalization of medical knowledge project, and the model based
decision-making project, a number of computer science issues were raised
(at least implicitly). In some cases, a need for improved technology is
more or tess clear; further we see ways to produce the required
improvements. In other cases, we will need to do more fundamental
research to achieve the facilities required by the medical projects.

In this section, we will discuss some computer science problems which
arise in the context of the medical projects, and will review our
current work on these problems and our plans for the future. Much of
this work is in preliminary stages, and so the examples we give show our
first prototypical programs. Undoubtedly much will change as we

proceed, and so we offer these examples oniy as that, not for their
technical details.

We also want to emphasize the advantage which our close association
with the computer science community at M.I.T. offers us with respect to
these problems. A considerable amount of research is being pursued by
members of that community which is either directly in line with or
supportive of our efforts. We plan to draw heavily on the expertise of
these workers, and whenever possible, we wil! incorporate their ideas
into our work. On the other hand, we believe that our research wil!
produce ideas and technology which they will find equaliy interesting

and useful. In atl, we are anticipating a close and fruitful
collaboration.

Computer Representation of Clinical Knowledga

One of the needs of each of the above projects is a means for
representing knowledge in the computer. This representational scheme
must be capable of accomodating diverse forms of knowledge, and at the
same time, it must allow flexible retrieval of knowledge. We have
undertaken the development of a program, called GOBBLE (uritten in
LISP), for managing a data base of knowledge. It is our intention that
GOBBLE (or some descendant of it) will serve the needs of all or most of
the above projects. The advantage of this is that it would greatly
facilitate the merging of the efforts of these projects. For example,
if the formal representation of clinical knowledge could be expressed in
Privileged (Ce C.A.Corry GEES Page 73

GOBBLE, and the strategies produced by the studies of the present
tt lness were compatible with GOBBLE, the two efforts could be readily
combined. The results of this combination would be a program with both
good strategies for dealing with knowledge, and a detailed data
structure which it could use for problem solving.

Although such a “knowledge management" program would be very
important, our initial aims for GOBBLE were rather pragmatic. We wanted
a program for our immediate needs (writing experimenta! present iliness
programs and rudimentary simulations of clinical cognitive process), but
we did mot wish to undertake a major tanguage development effort,
especially when our understanding of the clinical decision-making
process was as yet unclear and poorly developed. Hence we opted for the
implementation of a flexible representation scheme with a small set of
primitives for accessing a knowledge base. This, then, is what GOBBLE
is, away of writing down facts, for ‘grouping’ facts together, and a
set of programs for retrieving facts which have been written in this way
and "digested’ by the GOBBLE program.

It is fitting to note the strong similarity of GOBBLE to MAPL 2 {17},
a formalism developed by Professor William A. Martin at M.I.T. We have
found that many of the ideas Martin had for MAPL 2 were well suited for
our work in medicine, and so we incorporated them directiy into GOBBLE.
Because of our close association with Martin and his research project in
Automatic Programming, we expect that GOBBLE will continue to be
influenced by the work of that group. Another influence on our thinking
has been the CONNIVER language {18} developed by Professor Gerald.
Sussman and Drew McDermott, also of M.1.T. Our understanding of the
issues was considerably enhanced by our experiences with CONNIVER.

Our emphasis on the antecedents of GOBBLE is to underscore the close
involvement we have with fundamental computer science research at M.1.T.
Our initial design of GOBBLE is only one example of the benefit which
accrue to us from this association.

The GOBBLE Program

GOBBLE is a data base handling system which we have written in LISP.
The principal features of GOBBLE are: 1) the use of contexts to create
*clumps’ of associated facts, and 2) the threading of facts in such a
way as to permit the retrieval of expressions representing facts through
the specification of subexpressions of these expressions.

A context name is associated with a set of ordered doubles or triples
called "valid expressions" where the validity of an expression is
determined through checks in a user-built, system maintained dictionary.
A GOBBLE context has no inherent significance other than that all facts
in a context are marked with the same context name. The same fact (e.g.
"(STATUS EDEMA PRESENT)") can appear in many contexts, but in each it
will have a unique incarnation. Each incarnation, however, will be
recognized by the system as corresponding to the basic pattern. Thus
the user canrefer either to the generic pattern (e.g. "(STATUS EDEMA
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PRESENT)") or to a particular realization of the pattern ("the edema
Which is present in Acute Glomerulonephritis"). This latter reference

would be to "(STATUS EDEMA PRESENT)" in the context "Acute.
Glomerulonephritis".

It should be noted that the system imposes no overall structure on
contexts. By mentioning context names in “subcontext" expressions in
other contexts, however, the user can organize an explicit hierarchy of
contexts. By mentioning the name of a context in a fact expression in
another context, the user. creates a link in an implicit network | of
contexts. (We will give some examples of below.) Of course, it is
incumbent upon him to make such a network useful. :

A context may contain any number of facts, each one represented by a
an expression in GOBBLE. form. By creating a context, the user
represents a theme for the facts, much as the writer of a book selects
the theme around which his presentation is organized. For instance,
Acute Glomerulonephritis (AGN) might be the context name, and the
expressions associated with it could represent the clinical picture of
this disease. Thus it would be a simple matter for a diagnostic program
to find out what kinds of things (e.g. sodium-retention) complicated the
identification of this disease, and how tikely this was to happen.
There might also be contexts about edema, hematuria, proteinuria, etc.
in which AGN is mentioned, but in which the central theme is the finding
in question. Thus various points of view about AGN would be found in
individual contexts (representing "clumps" or frames). To this extent,
GOBBLE represents information much as do the writers of the chapter
cited above. There is a major difference, however, in that in GOBBLE,
all these clumps are linked’ by the through extensive cross-referencing.
GOBBLE stores information in.a complex association network, and provides
functions for the flexible retrieval of facts from this network.

The GOBBLE Formalism
The general form of expression for GOBBLE is:

(<function> <argument> <value>)

where the value is optional. In our formalism, facts are equivalent to
applications of functions to arguments to produce values. In our
current work, we use such “functions” as LOCATION, AMOUNT, CAUSE,
FINDING, SUGGESTS, ETC. Thus, for example, to represent the fact that

the patient has light proteinuria, we could GOBBLE into the "patient"
context an expression for this fact.

(GOBBLE PATIENT (AND (STATUS PROTEINURIA PRESENT)
(AMOUNT PROTEINURIA LIGHT) ))

Below, we will show how this new fact can be related to other facts
about light proteinuria already in the knowledge base.
Privileged Communication G.A.Gorry a> Page 75

As another example, consider the structure:

(PREREQUISITE (STATUS STREPTOCOCCAL-INFECTION PRESENT)
(AND (STATUS STREPTOCOECAL-EXPOSURE PRESENT)
{TIME-OF (STATUS STREPTOCOCCAL-EXPOSURE PRESENT)
(BEFORE (ONSET STREPTOCOCCAL -INFECTION)
(INTERVAL (WEEK 1.) (WEEK 3.))))))

This is an encoding of the fact that one must be exposed to the
streptococcal bacteria a few weeks before the disease develops.

More complex structures can be GOBBLE’d by the system, with the
context mechanism serving as the key to bind these structures together.
A fragment of a context for AGN is shown in the Figure 8. Here facts

about the time relationships of symptoms of the preceding streptococcal
infection and a few of the symptoms of AGN.

Pattern-Matching and Fact Retrieval

As noted above, our short term interest in GOBBLE is rather
pragmatic, and as aresuit, we have restricted the development of
pattern matching and. fact retrieval facilities to a few basic functions.
After we have gained experience with these functions and the GOBBLE data

structure in the medical projects, we will undertake a more extensive
development of these facilities. It seems, however, that our short term
needs in the other projects will be reasonably well met by the current

version of GOBBLE.

The facilities for pattern based retrieval of facts which we have
built into GOBBLE allows the specification of a "theme" for the
organization of facts at a time after the facts have been stored. Facts

can be retrieved either in a context or through all (or some set of)
contexts.

Suppose the piece of advice (suitably encoded in GOBBLE) "The presence
of tight proteinuria and gross hematuria together suggests either a
stone, or a tumor, or recent coagulopathy." were stored in the knowledge
base. If the program was given the fact “proteinuria is present", it
could find hypotheses about the cause of the proteinuria by using one of
the pattern matching programs. Among the suggestions returned would be

the one above. Then a dialogue could be initiated to "fill" the
pattern:

What is the amount of the proteinuria?
LIGHT

Does the patient have hematuria?
YES .

Is it gross?

YES

etc.