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\head{{\small Annual Progress Report -- 1 R24 RR 01320-01\hfill Peter
Szolovits -- 563-74-6748\hspace 2pc}}
\begin{document}

\chapter{Overview and Summary}

This is the Annual Progress Report for our research project funded under
the Biotechnology Resources Program's grant number
\mbox{1 R24 RR 01320-01} from the NIH Division of Research Resources, titled
``Clinical Decision Making and Artificial Intelligence.''  As a
Resource-Related Research Project affiliated with the national
``Artificial Intelligence in Medicine'' (AIM) effort, with SUMEX-AIM,
and with other AIM research at MIT, our overall objective is to develop
improved methods of computer-based medical reasoning, to better
understand the reasoning of human expert clinicians, to develop means
for better explaining the knowledge and methods of the computer to its
potential users, and to improve the application of decision-analytic
reasoning to clinical problem-solving tasks.  Each of these objectives
matches a deficiency in current AIM programs; thus, through this
research effort we seek to enhance the related capabilities of future
AIM programs.

This project consists of three related sub-projects, focusing on the
following goals:
\begin{enumerate}
\item To study and elucidate the knowledge representations and
problem-solving methods employed by physicians making difficult
management decisions, involving considerable risk and uncertainty.

\item To develop new methods that allow expert programs to explain and
justify their conclusions by arguing from fundamental medical facts and
principles and reconstructing the path by which those bases have led to
the program's recommendations.

\item To continue development and enhancement of a micro-computer-based
decision-analysis and sensitivity analysis system for clinical use by
physicians.

\end{enumerate}

A summary of the accomplishments of each of these sub-projects is
presented here, and a more thorough discussion of each sub-project and
its plans for the coming year follow in the subsequent three section.

\section{Critical Decision Node Project}

Throughout the past decade of significant progress in the creation of
artificial intelligence programs for medical applications, the study of
how human expert medical practitioners make clinical decisions has
played a major role in suggesting the methods and knowledge
representations that could be used by these programs.  Equally important
is the fact that such studies also help to make explicit just what the
knowledge of these expert clinicians is, thus helping to teach their
skills to new generations of doctors.

One of the most difficult areas of medical decision-making is that where
decisions rest on a complex interplay between competing risks and
benefits.  If all known options open to the decision-maker are
explicitly known, if all possible consequences of each decision can be
foreseen, and if the likelihoods and costs and benefits of each of these
outcomes can be assessed, then the normative methods of decision
analysis may be brought to bear.  (Indeed, making practical the
application of just this methodology is the focus of our Decision
Analysis Tools project.)  Often, however, critical decisions involving
uncertain outcomes and imprecisely-understood risks must be made, when
the knowledge required by our formal techniques is simply not available.

In this subproject, we have turned our attention to a formal analysis of
the decision-making strategies and knowledge of human experts faced with
just such problems.  Our goal is to understand what these experts do in
the face of a serious lack of hard data, and to develop a sufficient
understanding of their successful strategies that we may develop
computational methods that will enable future AIM programs to use
similar methods.

Dr.\ J. P. Kassirer and Dr.\ A. J. Moskowitz of the Tufts/New England
Medical Center and Prof.\ B. J. Kuipers of Tufts University have
collaborated in the extensive analysis of the patient management
decisions of one expert physician facing a very seriously ill patient.
To do this, they have developed a formal six-step methodology for the
analysis of verbal protocols taken from their subject, and they have
begun to apply the methods developed therein to improve the teaching of
medical expertise to clinicians at all levels.

\section{Explanation and Justification Project}

As we stated in our proposal, ``an innate part of the consultation
process is the user's ability to explore the reasoning on which the
expert bases his advice and to argue the appropriateness of the data and
assumptions on which that advice is based.''  We then outlined five
research goals that would move the AIM field toward the ability to build
programs that meet this requirement:
\begin{enumerate}
\item Develop and implement new strategies of explanation by improving
the user's access to the program's knowledge of
	\begin{enumerate}
	\item the causal and temporal relations among disease states,
	pathophysiology, observable signs and symptoms, and drug
	pharmacokinetics, and

	\item internal decisions made by the program in the course of
	its deliberations.

	\end{enumerate}

\item Test the utility and acceptability of explanation strategies.

\item Develop models of the user's present knowledge and what he or she
wants to know.

\item Give the user increased control over the system's internal
decisions and conclusions.

\item Generalize the models of therapy developed in our Digitalis
Therapy Program to other applications areas.
\end{enumerate}

Work during the past year in this subproject has had two foci:
First, during the three years intervening between our original proposal
and the beginning of funding for this project,
we have concluded that goals 1, 4 and 5 of the above list require
not only the creation of new techniques of explanation but also
fundamentally-improved representations of the knowledge that is to be
explained.  Therefore,
Dr.\ W. J. Long has, with the assistance of Mr.\ T. A. Russ and Mr.\ I.
Kohane, developed a set of new knowledge representations and reasoning
strategies that will underlie the new explanation capabilities.  This
work has been carried out in the context of two new larger projects that
derive from the Digitalis Therapy program (the context in which our
ideas were proposed):  a program to assist physicians with reasoning
about congestive heart failure, and a program for management of
ventricular arrhythmias.

Second, Mr.\ R. A. Granville has created much improved methods of
generating English output from the explanation program, exhibiting
conciseness and coherence of 
text.  A good explanation depends, however, not only on stylistically
acceptable English.  One particular area we have identified as crucial
to further improvements is to tailor what the program says to what it
believes the user already understands (part of goal 3).  During the past
year, Mr.\ Granville has begun to develop user models to help decide what
needs to be stated and what can be left implicit.


\section{Decision Analysis Tools Project}

Our group has had many years' experience in developing and applying the
formal methods of decision analysis to a large variety of clinical
decision tasks.  Within the past five years, the widespread availability
of small, inexpensive, but powerful computer workstations has made
possible the widespread dissemination of these methods and the provision
of necessary computer support for their effective application.  The
further problems facing the use of this method are partly
pragmatic---the cost and difficulty of use of the hardware and software
support environment---and partly fundamental---the difficulty that
potential users without extensive formal decision-analytic training have
in building new decision-models for novel clinical problems.

This subproject has concentrated (during the past year) on both the
pragmatic and fundamental aspects of the problem.  Dr.\ S. G. Pauker and
his colleagues at Tufts/New England Medical Center has improved the
Decision Maker computer program, adding new capabilities for
cost-effectiveness calculations and Markov modeling, and making it
available in less expensive and more powerful configurations.  Dr.\ J.
Hollenberg has implemented a small prototype artificial intelligence
program that can help its user to define new decision trees.  That
program represents typical decisions, potential outcomes, and
probabilities and utilities in a narrow domain of medicine, and applies
a frame-instantiation algorithm to guide the user through the process of
decision-tree construction.  Mr.\ M. Wellman, working with Prof.\ P.
Szolovits and Dr.\ Pauker, has been studying the development of
multi-attribute utility models, and has begun the design and
implementation of an artificial intelligence program that will assist an
expert decision-analyst in the construction of new multi-attribute
utility models.

\section{Institutional Arrangements and Plans}

As in the past, we have been successful in integrating a complex set of
related but distinct research efforts at three separate institutions.
This has proven possible because Prof.\ Kuipers (from Tufts University)
is also a Visiting Scientist at the MIT Laboratory for Computer Science,
because Prof.\ Szolovits, Dr.\ Long and Mr.\ Wellman regularly visit
Drs.\ Pauker and Kassirer's laboratory and fellows, and Drs.\ Hollenberg and
Moskowitz have taken courses at MIT and interacted frequently with other
members of the Clinical Decision Making Group.

We plan to continue in the coming year with very similar arrangements.
However, Prof.\ Kuipers will join the MIT Laboratory for Computer Science
as a part-time Research Associate, leaving his position at Tufts
University.  He will also share an appointment at Tufts University
Medical School.  In addition, Dr.\ Long has begun to spend more time at
Tufts/New England Medical Center because we have obtained new funding
for a project in the management of Congestive Heart Failure, the
research on which is to be performed in collaboration with Dr.\ Pauker
and another group of physicians at Tufts/NEMCH.  Also, Dr.\ Robert
Kunstaetter has joined the Clinical Decision Making Group at MIT as a
Research Assistant, and has begun to develop a collaborative project with
Dr.\ G. Octo Barnett of the Massachusetts General Hospital, wherein we
plan to apply the explanation methods developed under this project in
teaching students at the Harvard Medical School.


\chapter{Critical Decision Node Project}

\section{Objectives}
     
     To   describe  the knowledge representations   and  problem-solving
methods  employed  by  physicians  making    difficult 
management    decisions,    involving   considerable   risk   and 
uncertainty.  To investigate the nature of clinical expertise and 
to  characterize the development of probabilistic  reasoning   by 
comparing  the knowledge and problem-solving methods used  across 
experimental  subjects differing substantially in  expertise. 

\section{Completed Work}
\subsection{Transcript Analysis}

     Our   work   to date has involved an extensive analysis   of  
the  behavior  of  one expert physician  (pulmonologist)   making 
management   decisions   for  a  desperately  ill  patient   with 
preleukeminia,    chest  pain,   fever  and  an  acute  pulmonary 
infiltrative process.   The  problem  involves  choosing  between  
empiric   treatment,   no  treatment  and  employing  potentially 
dangerous  testing   to   guide treatment.   The  uncertainty  in 
etiology  of the underlying disease process,    the   high   risk  
associated  with  gathering  further information  and  the urgent 
need for specific treatment  of  the immunocompromised    patient   
engenders   decision   making   with probabilistic  reasoning and 
employment of techniques  to  assess multi-attribute utilities.

Research in transcript analysis proceeded as described in the following
sub-sections:

\subsubsection{Segmenting The Transcript}
  
           The  verbatim  transcript was  first  segmented   into  
      lines  and paragraphs.    This process involved breaking up 
      sentences   so  that only one or two pieces of   a  complex 
      concept was contained on a line,  which frequently spread a 
      sentence over many lines.    Paragraphs were delineated  as 
      coherent  chunks  of   narrative.   Paragraph  breaks  were 
      inserted  with each change of topic or style of  reasoning. 
      This  particular  transcript was segmented into  over  1100 
      lines.   An example of the process can be found in appendix 
      A.  The  transcript  was  then  reviewed  to  identify  the 
      sections    that   contain   problem   solving    material.   
      Subsequent  steps  in  the analysis deal only  with   those 
      sections that contained problem solving material.


\subsubsection{Determining the conceptual framework of the reasoning process}
        
      Each line of the transcript was examined to  identify 
      the domain object being referenced.   These object  phrases 
      are distinct from the wording used to refer to  them.  (See 
      appendix B for an example.)
       
      Each object phrase was then  categorized,  resulting  
      in   a broad list of elements that the expert was reasoning 
      about.    This   list   of elements was  then  grouped  and 
      further   categorized  to  establish  a list of  conceptual  
      framework  elements.  (See appendix C for an example.)

\subsubsection{Determining the content of the knowledge being used}
       
               Each   line of the transcript  was  then  reviewed  
      to  identify the assertions made about each domain  object.  
      We assume that the content of these assertions  constitutes 
      at  least  some  of the knowledge employed  by  the  expert 
      physician.


\subsubsection{Characterizing The Reasoning Process}

             With  the  transcript analyzed in terms  of   domain 
      objects  and  relationships  among  domain  objects,    the 
      progress   of  the  decision  process  was  analyzed.   The 
      reasoning  process employed  in  a specific problem solving 
      section  was then matched against problem solving   methods 
      derived  from  research in Artificial   Intelligence  (AI).   
      When  a  specific AI method  was seen to fit a   particular  
      section,  we then examined other sections of the transcript 
      to  determine  if  the same  AI  method  characterized  the 
      reasoning process in the new section.   In this manner,  we 
      reviewed   the transcript  and  were  able to  characterize 
      many of the  reasoning processes employed by the physician-subject.

\subsubsection{Validation of the model of the reasoning process}

            The model's completeness and consistency was assessed 
      by  two means,  its ability to account for each   referring 
      phrase in a selection of the transcript and its ability  to 
      specify a computer simulation of  the decision  process.

            The  first step accomplished  in the specification of 
      a  computer  simulation  of the reasoning  process  was  to 
      encode  the knowledge employed by the expert  physician  in 
      making his decision.  Sections of the transcript containing 
      decision  making  material were reviewed and the  knowledge 
      that  they contained was translated into a  rule  language.  
      The basic structure of the language is the rule, which acts 
      to   link  antecedent  conditions to actions based  on  the 
      truth of those antecedent conditions.   The rules take  the 
      form  of
\begin{verse}
\begin{verbatim}
IF <ANTECEDENT CONDITIONS>  THEN  <ACTION>.
\end{verbatim}
\end{verse}
An example  of this encoding process is documented in appendix 
      D.  The process was completed for the each of the decisions 
      focused on by the physician-subject.  The next step in  the 
      simulation process is described below.

\subsubsection{Comparison to decision analysis}

          We  have compared the decision process employed by  our 
      physician-subject  to a normative model of decision  making 
      under uncertainty,  namely decision analysis.  We found the 
      reasoning  process exhibited in the transcript  to  closely 
      resemble  the conceptual framework of decision analysis but 
      to  employ a significantly different processing  algorithm.  
      Based  on  this work,  we have submitted  an  abstract  for 
      presentation  to the  Society  for Medical  Decision  Making 
      (appendix E)  at its sixth annual meeting in November 1984.    
  

\subsection{Application Of Results To The Teaching Of Clinical Medicine}

      In  the grant proposal we pointed out that the research  we 
were undertaking as part of this effort might enhance our ability 
to  teach  expertise to clinicians at all levels.   In  a  recent 
publication  (appendix F) we reported just such an effort,  based 
on the principles identified in descriptive research on  clinical 
problem solving.  In this paper, we showed that clinical medicine 
can  be taught to junior and senior medical students and to house 
officers by a method that follows an iterative,  hypothesis-based 
approach.  This method is a direct derivative of earlier research 
of  ours  and  others on human problem solving  and  on  clinical 
problem solving in particular.   In recent years this prospective 
hypothesis-based  method  also has been applied  to  postgraduate 
education  at  the  annual meeting of  the  American  College  of 
Physicians.  

      We  expect that the efforts of the research  supported  in 
this  grant  will produce similar insights into  decision  making 
under  conditions of uncertainty,  in particular those  decisions 
requiring  tradeoffs between the risks and benefits of tests  and 
treatments.    We   also  expect  that  these  insights  can   be 
incorporated into the teaching of clinical medicine.  

\section{Plans For The Coming Year}

\subsection{Completing The Validation Of The Model  Of  The  Reasoning 
       Process And Additional Transcript Analysis}
  
          We   plan  to complete the evaluation of  the   model's 
      completeness  and  generality  utilizing an  existing  rule 
      system  to  simulate  the  decisions  being  made  in   the 
      transcript.    EMYCIN,\footnote{van Melle, W., 1979. IJCAI 6, pp. 923--925.}
a domain independent rule system will accept the set
      of   rules  that  we   derived  from  the  transcript   and 
      systematically   apply  them  until  a  goal  attribute  is 
      established,  the  system exhausts the rules,  or the rules 
      that  we  derived fail.   Our goal attribute is   the  same 
      choice  of  action that our expert  physician  chose.   The 
      adequacy  of the knowledge encoded by these rules  will  be 
      assessed  by  applying them to similar management  problems 
      but with a slightly different context.  The degree to which 
      we must append the set of rules to establish our goal  will 
      be a measure of their adequacy.

          As  a  means  of establishing empiric validity  of  our 
      model of an expert physician's reasoning processes, we will 
      be  analyzing transcripts of the  same  physician,  solving 
      problems on a case in a less familiar clinical domain.   We 
      suspect  that similar reasoning processes are employed  for 
      decisions  with clinical problems that can be structured in 
      a similar fashion.
      
   
          We  also have collected several other transcripts  from 
      other   expert  level  physicians  with   other  areas   of 
      expertise than the present physician-subject.   We will  be 
      evaluating these transcripts in the manner described above.  
      The  process will allow us to assess the  similarities  and 
      differences   in   the  reasoning  processes   employed  by 
      expert  physicians with different areas of  expertise,  for 
      problems both in and out of their domain of expertise.   We  
      will   be looking to establish whether the same theoretical 
      model    of   the   reasoning   process   found   for   the 
      pulmonologist is employed  by  other physicians facing  the 
      same decision.   

          As mentioned in the grant proposal,  we are  interested 
      in  characterizing  the development of clinical  cognition.  
      Specifically  we  would  like to  follow  the  ontogeny  of 
      probabilistic  reasoning  and the development of  reasoning 
      tools  for  making  tradeoffs  in  the  risks  imposed   by 
      diagnostic  tests  and therapy.   We will start to  collect 
      transcripts  of  physicians  at  various  stages  of  their 
      training  (3rd year and 4th year medical students, 2nd year 
      Internal  Medicine  residents  and  medical  sub-specialty 
      fellows),  as  they  solve the same problems posed  to  our 
      expert level physicians.  

\subsection{Developing A Computational Model Of The Decision Process}
  
            Understanding the structure of knowledge and problem-solving
methods that underlie clinical expertise will  have  
      a  significant  impact  on the  design  of  knowledge-based 
      artificial intelligence systems in medicine.  We have begun 
      to  investigate  putting together results of our  study  of 
      expert   clinical  reasoning  with  a   hypothesis-directed 
      diagnostic  system that is currently in working model  form 
      at  Tufts University,  Medford campus (B. J. Kuipers).   In 
      fact Dr.\ Kuipers will be supported part time by this grant 
      in  the coming year to enhance this aspect of the  research 
      effort.

         The diagnostic system at Tufts employs an Evoker  module 
      that  is  modeled  after  the categorical  aspects  of  the 
      Present Illness Program developed here.\footnote{Pauker, et al., {\it Am J 
      Med} 60:981,1976}  Its purpose is to evoke a collection of 
      elementary hypotheses,  which are then assembled by a build 
      module,  into patient-specific hypotheses.   Given a set of 
      active hypotheses the system must choose diagnostic testing 
      for  the  confirmation of these  hypotheses.   We  will  be 
      investigating  the implementation of a test planning module 
      that  will  employ  the  reasoning  processes  that  expert 
      physicians  use in their pursuit of medical diagnostic  and 
      therapeutic strategies.


\chapter{Explanation and Justification by Expert Programs}

This section of our proposal focused on the development of improved
explanation and justification methods for the Digitalis Therapy Program
and the extension of these methods to other problem domains.  Since the
time of that proposal (nearly four years ago), our views of both the
Digitalis Therapy Advisor and of how best to accomplish the objectives
outlined in the proposal have evolved.  With the increased use of a
large variety of drugs in addition to digitalis in many conditions for
which digitalis was used almost exclusively, it became obvious that a
successful program must deal with a larger, coherent management problem
rather than concentrate on the use of a single drug.  Therefore, the
context of this research has broadened to encompass two logical
outgrowths of our early work on digitalis.
The first of
these is the Heart Failure Project.  The objective of this project is to
help the user reason about the diagnosis and management of heart failure
by relating the findings to the possible pathophysiology that might be
responsible.  This project is an outgrowth of the observation in the
Digitalis Therapy Advisor that it is difficult to relate the changes in
the digitalis levels to the changes in the heart failure manifestations
without taking into account the other therapies that might be involved
and the particular nature of the heart failure in the patient.  The
second project is the Ventricular Arrhythmia Management Advisor.  This
project extends the idea of computer assisted therapy management to the
management of a class of disease situations where any of a number of
therapies might be applicable.

	These two projects have given us a clearer understanding of the
problems of giving the user useful explanations of the reasoning and
advice of such programs.  In particular we have been making significant
strides over the past year to design programs where the user has access
to the disease, physiological, and therapeutic relationships behind the
advice given by the program.  One important benefit of the explicit
representation of these relationships is that it promises to let us
build systems wherein the user has a much larger degree of control
over the reasoning process and the conclusions reached by the program.
The program thus becomes more a ``reactive blackboard'' on which the
user can try out various ideas, rather than the more traditional expert
decision maker.  Finally, the changes needed to allow our programs to
deal with larger disease classes, in which there are significant
therapy choices to be made, also simplify the
addition of new therapy modalities.

\section{The Heart Failure Project}

	The Heart Failure Project has been particularly fruitful for
developing a model of user access and control over the reasoning
process.  The central observation in the development of a design for
this program is that much of the reasoning that supports both diagnosis
and therapeutic decisions is based on the particular complex of disease
processes and physiological compensations exhibited by the patient.  The
most obvious way to produce similar kinds of reasoning in a computer
program is to do the reasoning from a physiological representation of
the understanding of the patient's state.  Thus we are developing a program
that has at the center a qualitative causal physiological model of the
factors related to heart failure, which can represent what is known
about the patient state.  This model is used for understanding the
significance of the findings, reasoning about the possible diagnoses,
reasoning about appropriate therapies, and providing explanations for
all of these kinds of reasoning.

	A physiological model centered approach to the patient
management process gives a user access not only to the reasoning of the
program but also to the justifications for the reasoning.  That is, it
is possible for the user to examine and explore the physiological
relationships concluded by the program and used to support the
recommendations.  For example, if the program suggests the use of an
arterial vasodilator to increase the cardiac output, the physiological
model would provide a consistent picture of why the recommendation is
appropriate.  This would include facts such as evidence of
vasoconstriction caused by a high sympathetic state and therefore the
likelihood that the cardiac output would increase rather than causing
the arterial pressure to decrease.  In addition the model provides the
user with the reasons for being cautious in giving such a therapy since
a possible alternative result is for the arterial pressure to drop.

	The most important consideration in developing a physiological
model for supporting this kind of reasoning is to provide the
relationships at the appropriate physiological level.  From our
exploration of the heart failure domain it appears that the level needed
for reasoning and for explanation are the same.  It also appears that a
single level which includes the hemodynamic relationships in the
cardiovascular system is adequate for this domain.  This is not so in
other domains, as pointed out by Patil in the acid-base domain, but the
single level in this domain simplifies the problem for exploring other
issues of diagnosis and management reasoning.

	The physiological model also makes it possible for the user to
exert control over the reasoning process.  We have observed that the
user many times is better able to assess physiological states of the
patient than a program that has a limited perspective on the problem and
only the ability to ask questions of the user.  Thus, our intention is
to collaborate with the user in the reasoning process and give the user
the ability to make hypotheses, change conclusions, and in general to
test the alternative relations that might exist in the patient.  This
kind of flexibility is possible because the model provides a consistent
physiological explanation of what is known about the patient.
Modifications to that representation are supported by a Truth
Maintenance System that propagates the implications throughout the
model.  Thus, if there are inconsistencies in the deduced state, these
will be pointed out by the system along with the alternatives for
correcting them.  These mechanism makes it possible for the user to
realistically consider changes in the conclusions of the system and to
tailor the reasoning to whatever the user knows about the patient.

	Over the past year we have made significant strides in the
development of the heart failure program.  To explore the important
representational issues, we are initially developing the model on a
subset of the domain---the problem of managing angina in the context of
possible heart failure.  We have put together a causal model for this
subdomain that includes about fifty nodes related as definite and
possible causal factors and worsening (predisposing) factors.  With this
model we are developing general strategies for the assessment of
evidence from the patient findings, strategies for pursuing a diagnosis,
and strategies for finding and assessing therapy choices.  A working
version of this program has given us new understanding of some of the
needs for explanation and the necessary ingredients for a useful
explanation over the last few months.

\section{The Ventricular Arrhythmia Management Advisor}

	The problem of generalizing the strategies for therapy
management are being addressed by the Ventricular Arrhythmia Management
Advisor.  The domain of the program is the control of ventricular
arrhythmias in the context of the intensive care unit.  Many of these
arrhythmias occur as a result of ischemia but there are also a number of
other causes as well as factors that may worsen the problem.  The
therapy for these arrhythmias is of two types.  Either it is directed at
the arrhythmia itself or it is directed at the cause.  The arrhythmia
therapies commonly employed include lidocaine, procainamide, quinidine,
bretylium, and others.  The determination of which drug is appropriate
in an individual is primarily done by trial and error.  Thus, several
may be tried before an acceptable patient response is achieved.  These
are all drugs with multiple compartment pharmacokinetic models, high
interpatient variability, narrow therapeutic windows, and high incidence
of toxicity.  Thus, it is an excellent domain in which to consider the
issues of generalization of patient management strategies.

	Our approach to generalization is to look for the underlying
unifying concepts that run through the patient management process.  For
example, in adjusting the dosage of a drug for anticipated
sensitivities, the physician is actually going through a particular
instance of risk-benefit analysis in which the risk is the likelihood of
toxicity and the benefit is the likelihood of control of the arrhythmia.
The determination of the appropriate recommendation depends on the
assessment of costs of the two conditions and the assessment of their
likelihoods.  The reasoning that leads to the actual therapy
recommendation can be made more general by including explicitly the
risk-benefit analysis that supports it.  This strategy simplifies
several problems.  Inclusion of new therapies involves changing the
assessment of costs to reflect the particular characteristics of the
therapy.  Changes in understanding about the properties of a therapy
either because of new medical knowledge or a different school of thought
are similarly incorporated as changes in the assessments or as changes
in the strategies for optimizing the achievement of therapeutic goals.

	Over the past year our work on this project has centered on the
gathering of case material to test both an initial trial program for
arrhythmia advice and for the development of the appropriate design for
the Advisor.  The case material includes a complete record of the
minute-by-minute arrhythmia data from an arrhythmia monitor as well as the
clinical information needed to follow the disease state and the record
of therapeutic interventions.  On a few cases we have also been able to
obtain drug level information for verifying pharmacokinetic models.  So
far we have collected data on 44 cases.  This case material is not as
detailed as we will ultimately need for verifying particular assessment
algorithms, but it is serving us well for developing the initial design.

\section{User Models in Discourse}

The problem of modeling what the user knows already and wants to know is
difficult.  During the past year we have begun to concentrate on one
aspect of this problem: the role of conversational context in generating
appropriate text.

When engaging in communication, it is important to know the context in
which statements are being made. This is equally true for the speaker
and the hearer. (Or the writer and the reader. We will not distinguish
between oral and written communication here unless we do so
specifically by exception.) It is necessary for the hearer to know the
context in which statements are being made in order to fully
understand them. For example, consider the sentence. ``The test result
was positive.'' In the context of a qualifying exam, this statement
made to the candidate would be cause for celebration. On the other
hand, this statement made to a patient being tested for leukemia has a
completely different meaning. And the statement made to two brothers
who are trying to see if one can donate a kidney to the other has yet
a third meaning. It is easy to come up with many contexts for which
this statement has a new meaning. It is obviously important for the
hearer to know the context in which an utterance is made.

It is equally important for the speaker to be aware of the context in
which his utterances will be understood. Telling a patient suspected
of having leukemia that the test results were positive, meaning that
the results were positively conclusive or that they were favorable,
would be incorrect because in this context positive means that the
illness being tested for is present. 

But context is more important than avoiding the misinterpretation of
statements. If somehow the speaker and hearer can agree on the context
for the discourse, both people's jobs are made easier. Whatever is in
the agreed context can be taken for granted in that both participants
know of its existence and understand it. Then conversation can be
restricted to those statements that somehow provide information that
is not yet in the agreed context. This allows the speaker to follow
the tenets of conversation observed by Grice.\footnote{H.P. Grice, ``Logic
and Conversation,'' in {\it Syntax and
Semantics: Speech Acts, Volume 3}, P. Cole and J.L. Morgan, ed.,
Academic Press, New York 1975.} And if we
needed to describe every concept each time we mentioned it, we would
literally never be able to finish a thought, since every concept used
to describe the initial idea would itself have to be described, and
each of the items used in those descriptions would have to be
described, and so forth.

Accepting that context is essential for discourse, we
might ask exactly what do we require to be in the context? As a
speaker, we need not only every piece of information we know that
might affect what we want to say, but also how each piece
will affect the interpretations of our statements for the hearer. And
as a hearer, we need to know what effects the speaker meant to have by
his statements.  In other words,
in addition to knowing what we know and believe concerning the topic
of conversation, we have to know (or least have an idea of) what the 
other person knows and believes. Since we cannot infallibly know what
is in another's head, at best we can have a model of the knowledge and
beliefs of our partner in conversation.

We use models of others' knowledge and beliefs at all stages when we
engage in communication. It is used when we are determining what is
appropriate to say. For example, if you were stopped in the streets by
someone in a car with out of state plates and the driver asked you
directions to a local famous site, you would probably not give
directions based on the location of other sites that would be well
known to residents, but not to tourists. This is because in this
situation you believe the driver generally doesn't know her way around
town. On the other hand, if you were giving directions to a friend
whom you know to have lived in this city for years, you would use
local landmarks because you believe she is familiar with them.

Models are also used in determining an appropriate way to make a
statement. To use the same example, one might say to a resident of
Boston, ``Go to the Hancock and turn left,'' whereas to a tourist a more
appropriate statement would be ``Go to that tall glass building and
turn left.''

If a computer system is ever going to engage in a discussion of any
significant depth, it is going to have to use context in both
understanding the user and in order to provide appropriate responses.
Then it follows that the system is going to need a model of the user's
knowledge and beliefs in order to provide the required context.\footnote{William C. Mann, Madeline Bates, Barbara J. Grosz, David D.
McDonald, Kathleen R. McKeown, William R. Swartout, ``Text Generation:
The State of the Art and the Literature,'' Technical Report
ISI/RR-81-101, Information Sciences Institute, Marina del Rey,
California, 1981.}
Two currently open problems with user models are how must the
system's model of the user change as the discourse progresses, and
where does the information in the model come from in the first place?

A good deal of active research is currently attacking the first of
these open questions, especially from the end of understanding user
statements. Examples of ongoing work include that of 
Pollack, Carberry and Granger.\footnote{Sandra Carberry, ``Tracking User
Goals in an Information-Seeking Environment,'' in the Proceedings of the
AAAI, 1983.
Richard H. Granger, Kurt P. Eiselt, and Jennifer K.
Holbrook, ``STRATEGIST: A Program that Models Strategy-Driven and
Content-Driven Inference Behavior,'' in the Proceedings of the AAAI,
1983.
Martha E. Pollack, Julia Hirschberg, and Bonnie Webber,
``User Participation in the Reasoning Processes of Expert Systems,'' in
the Proceedings of the AAAI, 1982.}
However, no one has proposed a theory with
which a system can initially create or derive a model of the user.
This is the problem that is being addressed in this project.

The solution proposed here is based on a theory of the way people
derive their models of others for discourse. In general, we know what
we know and believe, and more importantly, we have an idea how common
this knowledge and these beliefs are. When I meet an adult, I assume
he knows the difference between red and green, since I believe this
is common knowledge learned while very young, but I would not assume
he is necessarily familiar with Fillmore's case grammar theory,
because I believe only people with a special interests in linguistics
have studied case grammar, and that not everyone is that interested in
the subject. On the other hand, if I am introduced to someone and am
told as part of the introduction that the person is a linguist, I
would expect him to be at least aware of Fillmore's work. If I believe
that there is a little green frog in the bottom drawer of my desk that
created the universe, I might tenaciously hold this belief, but I
would also recognize that the vast majority of people, for whatever
reason, do not share this view. 

In other words, it seems that the initial model we form of a person
for discourse purposes is the first impression (for the present
meeting) we have of that person. Obviously, the more information we
have, the more accurate the impression. Upon meeting an intimate
friend, our model would be fairly elaborate and accurate. Meeting a
stranger for the first time would allow us to form only a vague model
based on what we believe is common to the average person. 

Computationally, this suggests a hierarchical default mechanism for
generating a user model. Each specific piece of information known
about the user, either from previous encounters or from being 
told, can be entered directly into the model. Other pieces can receive
default values from what the system believes to be common knowledge or
average values. Obviously, the system would need to keep track of what
is actually known and what is being assumed about the user. As more
information is learned about the user through discourse, default
values can be replaced with actual facts.

In conclusion, we have seen that context is essential for meaningful
communication. People use models of their conversational partners,
models that contain ideas of the other's knowledge and beliefs, in
order to determine the correct context of the conversation. Any
computer system that engages in intelligent discourse will have to
have a similar model of the user, and it will have to be able to
derive this model. Our plan is to build a prototype system that forms this
model much in the same way that people do, through past experiences
with the user, making assumptions about common knowledge where
specific knowledge fails.

\section{Research Plan}

Both the Heart Failure Project and the Ventricular Arrhythmia Management
Advisor Project are clearly efforts too large to be adequately supported
by this grant.  Also, both have developed multiple foci that range far
afield from the problems of explanation with which they began.
Therefore, we have successfully sought separate funding for the Heart
Failure Project from NHLBI, which began in April of this year.  Funding
through this grant will be used, therefore, only for support of work
relating to the generation of explanations in this project.  We have
also sought separate funding (as a subcontractor for a grant application
made by our colleagues at Boston University Hospital) for support of the
Arrhythmia Management Project.  If this application is successful, then,
as with the Heart Failure Project, this grant will support only the
explanation portion of that research effort.

Our current work on user modeling will continue, and we hope to have a
more complete definition of the initial model and to begin a computer
implementation during the coming year.  In addition, we plan to begin
development of an experimental environment where we can test our
theories on actual student users.  We are currently trying to select the
appropriate application area for this; initially, it must be a program
whose medical expertise is more limited and easier to build than the two
major projects described above, so that we may have a testbed in the
relatively near future.


\chapter{Development of Tools for Clinical Decision Analysis}
  
\section{Executive Summary}
 
     In this subproject, the investigators have been developing and  
modifying a microcomputer program to perform clinical decision analysis.  
The program presumes the user is familiar with the basic principles of  
decision analysis (formulating problems as decision trees, assigning  
likelihoods [probabilities] and relative values [utilities], and  
interpreting the results of systematic variations in the values of single  
parameters or sets of parameters [sensitivity analyses]). The program  
allows the users to efficiently specify and analyze such decision problems.  
In the course of the first twelve months of the project, the program has  
been rewritten in several different environments to explore its  
extensibility and to improve it ease of use. We have directed our efforts  
entirely at the IBM-PC personal computer because it has the lion's share of  
the microcomputer market. The original program is written in UCSD Pascal.  
That system, with its compact P-code representation was necessary to ``fit''  
the program into a microcomputer. Two more recently developed programming  
environments that can use larger amounts of memory (IQLISP and Turbo Pascal)  
have been used to provide versions that do not require the user to purchase  
the expensive P-system and version that are compiled, producing a five to  
twenty-fold increase in processing speed. In the past year we have also  
enlarged our representation scheme to allow more convenient representation  
of time series processes and to allow the convenient performance of cost- 
effectiveness analyses. We have also begun to rewrite our manual and  
develop a library of sample analyses. The original versions of the program  
have been distributed to some 30 users and groups. 
     		 
\section{Progress Report}
 
     The work accomplished in the past year differs slightly from that  
described in the original application dated 30 September 1980. First our  
target machine is no longer the Apple III. Since that time the IBM-PC and  
IBM-XT have become the dominant force in the microcomputer marketplace. We  
have therefore chosen these machines as our target. Furthermore the  
microcomputer market has been sufficiently shaken out that we did not feel  
compelled  to maintain machine independence by using UCSD Pascal. Another  
major reason for exploring the feasibility of abandoning that environment  
is because its purchase cost (\$600) was deemed to be a significant obstacle  
to program distribution. We initially had difficulty moving to other Pascal  
environments because the size of the required longest path object code  
overlay exceed one machine segment (64K). We have recently identified two  
languages with allow use of extended RAM and were therefore feasible  
programming environments---IQLISP and Turbo Pascal, version 2.0. 

     The Turbo and UCSD Pascal versions are nearly identical. The advantage  
of Turbo, however, is its use of full RAM (up to 640K), allowing far larger  
trees structures and, more importantly, far deeper recursion depths. In  
this version, we have incorporated a form of cost-effectiveness analysis  
which allows the use of dual utility structures with an average of only  
15\% increase in processing time (compared to a 100\% increase under more  
standard schemes). The concept involves recognition that all features of a  
cost-effectiveness tree are duplicated, save for the second utility  
structure. Rather that fold the tree back twice, we developed a new  
expression processor that evaluates and expression and its shadow, or  
alternate structure. The sum of probabilities x utilities then becomes two  
sums: probability x utility1 and probability x utility 2. Thus all linking  
of tree structure and probability evaluation (70-80\% of processing) is  
performed only once. 

     We have also developed a representation for Markov processes (submitted  
for presentation at SMDM meeting, 1984). The concept is that a Markov node  
is an outcome descriptor (like a terminal node) but is a more complex  
process than simple expression (utility) evaluation. Each utility is now an  
incremental utility, in three flavors: initial, tail, and all others.  
Transition probabilities can be arbitrary time dependent expressions. In  
the UCSD and Turbo Pascal versions, the number of states can be very large
(over 100) but processing is quite slow. In the IQLISP version, all  
evaluations are compiled into BASIC generated object code, but the compiled  
expressions are very large. Compilation is lengthy (minutes to hours) but  
running time is very fast. The number of states possible in this version is  
limited to 20-25. 

     The IQLISP version of the program is basically a compiler that does  
symbolic tree evaluation. It generates large expressions that are
analyzed for common sub-expressions and are then passed to the BASIC  
compiler for object code generation. The object code is very rapid in  
execution and includes a variety of advanced graphic displays, but the  
lengthy compilation-and-test cycle makes it cumbersome for tree
development and debugging.

     We have also been revising our manual. Preliminary distribution of an  
earlier program version and manual has pointed out many inadequacies in  
that manual. Clearly the additional features and new machine environment also  
requires that a new document be created. 

     Because the grant award has not included funds for evaluation and  
testing of the program, we have not been developing formal protocols for  
data collection by collaborating users. Nevertheless, we have been  
developing broad experience from program utilization in our own division  
and at a limited array of other user sites. 
  
\section{Research Plan}
 
     In  the  next year,  we plan to complete the manual and to  develop  a  
further  array or worked examples and template analyses,  including  Markov  
and  cost-effectiveness  techniques.  We shall also continue to refine  the  
IBM-PC versions of the program.  At this point, it is not clear whether the  
IQLISP version will continue to be feasible.  That language environment  is  
moderately  buggy  and the time intensive issue of symbolic compilation  is  
difficult  to manage.  We shall continue to work in a compiled object  code  
environment,  either  in Turbo Pascal or in C (Lattice version).  We  shall  
support  processors with and without the 8087 coprocessor.  We will try  to  
develop  a  set  of graphics routines (for use  with  both  monochrome  and  
color graphics display boards) to allow more natural tree display.  We shall  
also  explore  the  utility  of using  color graphics  to  display  multiway  
sensitivity  analyses and shall try to support a standard four or six color  
plotter, such as the Hewlett-Packard or the SweatPea. We are also beginning  
to explored to feasibility of using windowing techniques and the ``mouse'' as  
an input device.  

     At  this  time,  we have not found efficient means of  compiling  dual  
utility  analyses  (such  as cost-effectiveness  analyses)  in  the  IQLISP  
version. We shall explore these possibilities. We shall also continue to  
distribute the developing program to interested collaborators so we can 
evaluate its utility.



\appendix

\chapter{Segmenting the Transcript}

The  following  is an example of the  segmenting  process.   Each 
sentence  is broken up into numbered lines containing one or  two 
pieces of a complex concept.  

\begin{small}
\begin{verse}
L644	I'm sitting here thinking about,\\
L645	well,\\
L646	am I going to recommend a bronchoscopy\\
L647	with a transbronchoscopic lung biopsy in this individual\\
L648	or am I going to recommend an open lung biopsy?\\
L649	There are a number of considerations\\
L650	that go into making that judgment.\\
L651	One is:\\
L652	what do you think the patient has?\\
L653	If you think the patient has a disease\\
L654	which the pathologist can properly diagnose on a fragment of tissue\\
L655	because the pathology is classical\\
L656	and readily recognizable.\\
L657	Or you think it will be diagnosable\\
L658	because a microorganism will be identifiable\\
L659	which will have readily identifiable morphological characteristics\\
L660	and one would recommend a transbronchoscopic lung biopsy\\
L661	as the first approach.\\
L662	If you think it unlikely that that's the case,\\
L663	then I think one would recommend going rather early and immediately\\
L664	to an open lung biopsy.
\end{verse}
\end{small}


\chapter{Identifying the Domain Objects}

The  following  is the same section of the  transcript  that  was 
shown  in  appendix  A.  We have highlighted  the  domain  object 
phrases by printing them along the left margin to separate  them 
from the phrases that refer to them, which are are indented.  

\begin{small}
\begin{verbatim}
I'm 
               sitting here thinking
               about,
               well,
               am
I    
               going to recommend 
a bronchoscopy with a transbronchoscopic lung biopsy 
               in this
individual
               or am I going to recommend
an open lung biopsy?
               There are a number of 
considerations
               that go into making 
that judgment.
               One is:
               what do you think 
the patient
               has?
               If you think 
the patient
               has 
a disease
               which 
the pathologist
               can properly diagnose on 
a fragment of tissue
               because 
the pathology
               is
classical
               and
readily recognizable.
               Or you think it will be
diagnosable
               because 
a microorganism 
               will be 
identifiable
               which will have 
readily identifiable
morphological characteristics
               and one would recommend
a transbronchoscopic lung biopsy
               as  
first approach.
               If you think it
unlikely
               that that's the case,
               then  I  think  one would recommend going
               rather early and immediately to 
an open lung biopsy.
\end{verbatim}
\end{small}

\chapter{Listing of Conceptual Framework Elements}

Each  of the domain elements from sections of the transcript  are 
listed  by  category  and  are followed by  the  transcript  line 
numbers where they are found.

\begin{small}
\begin{verse}
{\it DIAGNOSTIC PROCEDURE}\\*
BIOPSY L628\\
BRONCHOSCOPY L646\\
TRANSBRONCHOSCOPIC LUNG BIOPSY L647, 660, 672, 674, 697, 704, 705, 842, 845\\
OPEN LUNG BIOPSY L648, 664, 672, 671, 675, 701, 734, 739, 752, 746, 850, 843\\
CHEST ROENTGENOGRAM L669\\
PROCEDURE L680, 818\\
OPERATION L742, 856, 699\\
LOW RISK PROCEDURE \\
TUBERCULIN TEST L769\\[10pt]

{\it GENERAL AND FUNCTIONAL STATES OF HEALTH}\\*
SICK L678\\
EMOTIONAL STATE L679\\
REASONABLE STATE CLINICALLY L695\\
VERY SICK L6731\\
HIGHER RISK PATIENT L753\\
ACUTELY AND SEVERELY ILL L779\\
MENTAL STATE L791\\
SEVERELY DIMINISHED INDIVIDUAL L798\\
SICKEST PATIENTS L849\\
PATIENTS CLINICAL STATE L1037, 993\\[10pt]

{\it DISEASE ENTITY OR SYNDROME}\\*
MUCORMYCOSIS L632, L637, 1076, L1095\\
RHINO-OPHTHALMIC MUCORMYCOSIS L636\\
LEUKEMIC INFILTRATE IN HIS LUNG L765\\
NONSPECIFIC INTERSTITIAL PULMONARY FIBROSIS L773\\
PNEUMONIC ILLNESS L774\\
DISEASE STATE L782\\
ARDS L785, 768\\
TUBERCULOSIS\\[10pt]

{\it MICROORGANISMS}\\*
LEGIONELLA L709,  720, 725\\
PNEUMOCYCTIS L710, 715\\[10pt]

{\it DIAGNOSTIC FEATURES}\\*
DIAGNOSABLE ON A FRAGMENT OF TISSUE L653, 654\\
PATHOLOGY IS CLASSICAL AND READILY RECOGNIZABLE 655, 656\\
MICROORGANISM IDENTIFICATION L658, 658, 659\\
MORPHOLOGIC CHARACTERISTICS L658, 658, 659\\
PATCHY NATURE OF PROCESS IN CHEST ROENTGENOGRAM L669\\
PATTERNS OF INFLAMMATION IN HETEROGENEOUS DISEASE L666, 667\\[10pt]

{\it DIAGNOSTIC REQUIREMENTS}\\*
ENOUGH TISSUE L665\\
PATIENT COOPERATION  L679, 682\\
ADEQUATE PATIENT HEALTH L680, L678, 695\\[10pt]

{\it NONINVASIVE INDICATORS OF SYSTEMIC FUNCTION}\\*
BLEEDING AND CLOTTING FACTORS 686, 687, 688\\
PLATELET COUNT 689\\
PT 690\\
PTT 691\\
ROENTGEN CLEARING L991\\
BLOOD GASES L992\\
BUN L909\\
CREATININE L910\\[10pt]

{\it TREATMENT}\\*
SOMETHING ON THE SHELF THATS GOING TO WORK L755\\
AMPHOTERICIN B L761, 861, 917, 939, 1083, 929, 973\\
SPECIFIC CHEMOTHERAPY L766\\
CORTICOSTEROID THERAPY L775\\
HIGHLY TOXIC AGENTS L879\\
DIALYSIS L920\\
HEMODIALYSIS L921\\[10pt]

{\it COMPLICATION OF PROCEDURE OR THERAPY}\\*
BLEEDING L732\\
PULMONARY FUNCTION DEFICITS L741\\
MORTALITY \\
RENAL REACTION L764\\
TOXIC MANIFESTATION FROM THE DRUG L904\\
RENAL FAILURE L918, L980\\
LUNG TOXICITY L963\\
KNOCK OUT THE PATIENTS KIDNEY L944\\[10pt]

{\it NOTIONS OF LIKELIHOOD AND CHANCE}\\*
REASONABLE CHANCE OF SUCCESS L708\\
POSSIBLY EVEN RARER L720\\
THE POSSIBILITY L773\\
PROBABLY L780\\
50-60\% CHANCE OF GETTING NONDIAGNOSTIC TISSUE L846\\
ABSOLUTE CERTAINTY OF GETTING L851\\
65-70\% CHANCE OF GETTING AN ETIOLOGIC DIAGNOSIS L853, L854\\
PROBABLY 60-70\% RISK OF RENAL FAILURE\\
CHANCES OF SALVAGE ARE VERY VERY SLIM\\
5-10\% SALVAGE RATE IN ALL LIKELIHOOD L1047, 48\\
UNLIKELY L662\\[10pt]

{\it NOTION OF FREQUENCY}\\*
FATALITY RATE L956\\
FACED WITH 100 SUCH PATIENTS L890, 891\\
RARE L716, 717, 715, 763\\[10pt]

{\it NOTION OF RISK}\\*
RISK OF MORTALITY: 50\% RANGE L781; 30\% RANGE L783; FRACTION OF 1\% L839; 1 IN 1000 L845; 1 IN 400 L850\\
HIGH RISK OF BLEEDING L732, L733\\
LOW RISK PROCEDURE L856\\
HIGH RISK PATIENT L704\\
HIGHER RISK PATIENT L753
\end{verse}
\end{small}


\chapter{Encoding Knowledge in Rules}

We  show here the process of translating the knowledge  contained 
in this section of the transcript,  into a rule  language.   This 
section  of  the transcript deals with choosing the type of  lung 
biopsy to be performed.

\begin{small}
\begin{verse} \obeycr
L652	what do you think the patient has?
L653	If you think the patient has a disease
L654	which the pathologist can properly diagnose on a fragment of tissue
L655	because the pathology is classical
L656	and readily recognizable.
L657	Or you think it will be diagnosable
L658	because a microorganism will be identifiable
L659	which will have readily identifiable morphological characteristics
L660	and one would recommend a transbronchoscopic lung biopsy
L661	as the first approach.
L662	If you think it unlikely that that's the case,
L663	then I think one would recommend going rather early and immediately
L664	to an open lung biopsy.
\end{verse}
\end{small}

In  English,  the  rule  states that if a lung  biopsy  is  being 
considered  and if the diagnosis being sought can be diagnosed by 
the  pathologic architecture seen on a fragment of tissue  or  if 
the  diagnosis  being  sought  can be  diagnosed  by  seeing  the 
etiologic  organism  on  a fragment of tissue,  then  choose  the 
transbronchoscopic lung biopsy.  Otherwise an open lung biopsy is 
preferred.

Encoding  this knowledge produced these three  rules.

\begin{small}
\begin{verbatim}
1. IF CONTEXT = ACTION(INVASIVE, LUNG BIOPSY)

   AND LUNG BIOPSY(TARGET-Dx) =  DIAGNOSABLE(PATH-ARCHITECT, FRAGMENT)   

   THEN LUNG BIOPSY(TYPE) = TRANSBRONCHOSCOPIC


2. IF CONTEXT = ACTION(INVASIVE, LUNG BIOPSY)
 
   AND LUNG BIOPSY(TARGET-Dx) = DIAGNOSABLE(ORGANISM-MORPH, FRAGMENT)

   THEN LUNG BIOPSY(TYPE) = TRANSBRONCHOSCOPIC


3. IF CONTEXT = ACTION(INVASIVE, LUNG BIOPSY)

   THEN LUNG BIOPSY(TYPE) = OPEN-LUNG   
\end{verbatim}
\end{small}

  A hierarchical ordering of the rules exists such that a rule 
with true antecedent conditions that is highest on our list  will 
be activated first.   None of the rules that follow the activated 
rule will subsequently be activated.


\chapter{Abstract}

This  abstract  is a direct result of our  investigation  of  the 
clinical reasoning process using verbal protocol  analysis.    It 
was  submitted for presentation at the 6th Annual Meeting of  the 
Society for Medical Decision Making.

\mbox{ }

\begin{center}
CLINICAL REASONING VERSUS DECISION ANALYSIS.\\
{\it A  J Moskowitz,  J P Kassirer,  B J Kuipers}\\
New England  Medical Center, Boston, MA\\
and Tufts University, Medford, MA.
\end{center}

   Decision  analysis  provides a normative conceptual  framework 
for analyzing complex decision-making problems.   To what  extent 
does   this  framework  resemble  the  knowledge   representation 
actually  employed  by  expert physicians  in  clinical  decision 
making?   We are investigating this question by studying verbatim 
transcripts of the behavior of senior physicians,  not trained in 
decision  analysis,   as  they  solve  difficult  decision-making 
problems  aloud.   We report here on our analysis of the behavior 
of  a single expert on a case involving substantial  uncertainty, 
risk and urgency. 

   The  analysis  proceeds  in two  stages:  1)  classifying  the 
referring  phrases  in the transcript to  reveal  the  conceptual 
framework   of  the  reasoning  process  and  2)  analyzing   the 
assertional  content of the statements to determine the knowledge 
being used.   We assessed the analysis according to two criteria: 
1)  its  ability  to account for every referring  phrase  in  the 
selection  being  analyzed  and  2) its  adequacy  to  specify  a 
computer  simulation  of the decision process  exhibited  in  the 
transcript.

   The  conceptual framework identified closely resembles that of 
decision analysis,  consisting of choices,  chance  alternatives, 
relative  likelihoods,  and utilities.   However,  the evaluation 
process  itself  focused  independently  on  different   utility 
attributes  to make particular decisions,  rather than collapsing 
utilities  into  a single  scale.   The  decision-making  process 
followed a ``hypothesize and test'' method whereby some choices are 
evaluated  before the entire decision tree has been  constructed.  
The  evaluation process itself generates additional  alternatives 
or decision nodes.  

   Thus,  verbatim  transcript analysis proved a useful technique 
for  determining the conceptual framework and the knowledge  used 
by expert physicians.   Looking at clinical decision-making under 
uncertainty,  we  saw  a  close  resemblance  to  the  conceptual 
framework  of  decision analysis but  a  significantly  different 
processing method.


\chapter{Teaching Clinical Medicine by Iterative Hypothesis Testing}

\end{document}
