Decision support and disease management:
a Logic Engineering approach
John Fox and Richard Thomson
Imperial Cancer Research Fund, PO Box 123, Lincoln’s Inn Fields, London WC2A 3PX
email for correspondence: jf@acl.icnet.uk
Abstract: This paper describes the development and application of PROforma, a unified technology for
clinical decision support and disease management. Work leading to the implementation of PROforma has
been carrried out in a series of projects funded by European agencies over the past thirteen years. The work
has been based on logic engineering, a distinct design and development methodology which combines
concepts from knowledge engineering, logic programming and software engineering. Several of the
projects have used the approach to demonstrate a wide range of applications in primary and specialist care
and in clinical research. Concurrent academic research projects have provided a sound theoretical basis for
the safety-critical elements of the methodology. The principle technical results of the work are the
PROforma logic language for defining clinical processes, and an associated suite of software tools for
delivering applications such as decision support and disease management procedures. The language
supports four standard objects (decisions, plans, actions and enquiries), each of which has an intuitive
meaning with well-understood logical semantics. The development toolset includes a powerful visual
programming environment for composing applications from these standard components, for verifying
consistency and completeness of the resulting specification, and for delivering stand-alone or embeddable
applications. Tools and applications that have resulted from the work are described and illustrated with
examples from specialist cancer care and primary care. The results of a number of evaluation activities are
reported to illustrate the utility of the technology.
1. Introduction
Quality and consistency in most healthcare systems are highly variable, and occasionally lamentable.
Traditional clinical practices are proving unsustainable (e.g. overuse of drugs and investigations, waiting
lists measured in years), and the costs of medical services continue to rise inexorably. In addition, highprofile media exposés of clinical errors, and increases in litigation following North American trends, are
forcing many European governments and healthcare agencies to acknowledge that their services have
structural problems.
It is not a sufficient response simply to blame doctors, as tends to happen in litigation. Most doctors are
doing their best, but have too much to do and too little time to ensure that all their decisions attain an
optimal balance between achieving the best care for their patients and the most efficient use of resources.
Many organizations are therefore looking for new solutions, and many are asking whether information
technology can help doctors carry out their job more effectively, without usurping their traditional
professional roles and responsibilities.
This paper reviews a line of work that aims to show that advanced techniques for supporting clinical
decision making and disease management at the point of care can make a significant contribution to the
process of patient care 1. While the objectives of the work are common to many other projects worldwide,
the solutions proposed exploit technological and other strengths that are particularly European.
The idea that computers can be used to provide various forms of assistance to clinicians, such as better
clinical records, timely prompts and reminders, and assistance in following care pathways, is far from new.
A number of well-known centres, particularly in North America, have pioneered such systems, in some
cases over decades. We are now beginning to see that their faith was justified, as objective benefits of
computer support on clinical effectiveness [1] and cost-effectiveness [2] are emerging.
Until quite recently, the funding available in Europe in this field was relatively modest, but about ten years
ago the European Union (EU) established a series of research and development programs in medical
informatics. Since then the level of activity and range of projects has expanded substantially.
Although the emphasis of European projects can be different from those in North America (e.g. a greater
concentration on primary care) common themes are also apparent. For example, a renewed interest in
established methods (e.g. Arden Syntax [3] ) and a desire to develop new methods for standardising
medical knowledge and disseminating best clinical practice (e.g. clinical coding systems, guideline
interchange formats [4] and technologies for delivering decision support [5, 6]).
In the field of decision support, European developments have paralleled North American trends. There has
been a radical shift in emphasis away from providing support for diagnosis decisions (as described in de
Dombal’s classic work on diagnosis of acute abdominal pain [7]) towards support for patient management
(e.g. the UK health service’s experimental prescribing system, Prodigy [8], and the Dutch trial of the
Bloedlink system for assisting GPs in ordering blood tests [9]. As elsewhere, current developments are
profoundly influenced by the emergence of the Internet, as indicated by the explosion of clinical guidelines
published on the World Wide Web.
Work on advanced clinical information technologies is now developing strongly in Europe. Mainstream
R&D in new technologies (e.g. multimedia), communications (e.g. the Internet and intranet technologies),
1 We acknowledge the contributions of the following to the work described in this paper: Pedro Barahona,
Ahmed Benlamkadden, Andrew Coulson, Subrata Das, Paul Ferguson, Claude Gierl, David Glasspool,
Colin Gordon, Nicky Johns, Andrew Newbigging, Peter Hammond, Ian Herbert, Jun Huang, Andrew
Jackson-Smale, Peter Johnson, Philippe Lagouarde, Joanne Lau, Rob Martil, Mike Morris, Mees
Mosseveldt, David Pitty, Ali Rahmanzadeh, Phil Reeves, Jean-Louis Renaud-Salis, Pierre Robles, Derek
Shafer, Paul Taylor, Johan van der Lei, Robert Walton.
and object-oriented programming languages and tools (e.g. CORBA, JAVA) are strongly influenced by US
developments. However, Europe is also showing distinctive strengths in certain areas. For example, formal
methods for software engineering (e.g. Z [10], VDM [11]) have always been strong, and these are being
transferred into knowledge engineering (e.g. ML 2 [12], DESIRE [13]).
There is also a strong European tradition in the development of software technologies based on
mathematical logic (e.g. logic programming languages like Prolog, deductive databases, constraint solving
systems), and these are having considerable influence on new technologies for machine learning and
software agents. There is a growing interest in safety-critical applications using both conventional methods
[14] and logic-based and AI techniques [15, 16, 17]. Our own work on medical systems has been
particularly influenced by formal software engineering [18], and the PROforma technology described
below exploits a number of ideas from these areas.
The work described in this paper is characterised by an emphasis on rigorous design and systematic
development methodologies for clinical applications, based on an integration of methods from logic
programming and software engineering, or logic engineering [19].
2. Logic engineering in medicine
Figure 1 summarises a line of research and development that began in 1986, aimed at creating a unified
technology for clinical decision support and disease management based on the logic engineering
methodology [15]. The diagram provides a map of the projects referred to in the paper and their interdependencies.
CREW
RED
Oxford system
of medicine
LEMMA
DILEMMA
MACRO
InferMed
PROMPT
RAGS
PRESTIGE
CADMIUM
CADMIUM II
Figure 1: Map of projects covering period 1986-98. URLs for project WWW sites are as follows:
PROMPT: http://www.acl.icnet.uk/lab/rtp_home.html; MACRO: http://www.eortc.be/macro;
PRESTIGE: http://www.rbh.nthames.nhs.uk/rbh/itdept/r&d/projects/prestige.htm; CADMIUM:
http://www.chime.ucl.ac.uk/HealthI/CADMIUM/; DILEMMA: http://www.acl.icnet.uk.
RED: http://www.augusta.co.uk/safety/listing/red.htm. InferMed is at: http://www.infermed.co.uk/.
The origins of the research are in a decision support system called The Oxford System of Medicine (OSM),
sponsored by Oxford University Press and aimed at general practitioners (GPs) [20, 21]. The OSM set out
to demonstrate how retrieval and browsing functions for electronic versions of conventional published
material could be combined with decision support. The Oxford Textbook of Medicine, a comprehensive
medical reference widely used by GPs in the UK, was the target publication. The resulting demonstrator
system included a range of decision making functions (for differential diagnosis, selection of tests and
investigations, therapy selection and simple prescribing decisions), together with a flexible text browser.
The main innovation of the OSM was a general decision procedure expressed in first-order logic rather than
in traditional quantitative formalisms based on probability. This procedure used meta-level reasoning
techniques to automate a number of steps in decision making which are not addressed by conventional
methods. These include the generation of decision options, identification of relevant information sources,
and the construction of arguments for and against alternative options [22]. Although at the time, the
method did not have the formal strength of statistical decision theory, it proved to have great flexibility and
versatility. Later projects provided a secure theoretical foundation for the technique as well as many
applications.
3. EU funding of Advanced Informatics in Medicine
In 1987, the EU funded the LEMMA2 project whose main objective was to carry out technical refinements
of the logic engineering approach, and to establish whether the decision making techniques developed for
general practice could be applied more widely, notably in cancer care [e.g. 23]. The project was
significantly influenced by the well-known work on ONCOCIN at Stanford University, but made use of the
OSM’s logical decision procedure for cancer diagnosis, staging and protocol eligibility decisions. In
addition, as cancer protocols typically require a number of tasks to be carried out over time, the OSM
model had to be extended to manage control of task execution. Standard logic programming techniques
were used to implement rule-based scheduling for protocol execution, based on a task life-cycle model and
pattern directed state-transition rules. These methods were also adopted in CREW 3, a prototype system for
supporting protocol-based clinical trials, and the MACRO clinical trials system, described below.
The LEMMA project laid the foundations for the DILEMMA project (1992-1994) whose objectives were
to:
 Develop a decision making and task management engine to permit applications to be embedded in
third-party and legacy systems;
 Integrate decision support and task management with electronic medical records;
 Transfer task management and decision support techniques to new domains, specifically cardiology,
and multidisciplinary shared care.
Dilemma's main results in the areas of tools and applications were as follows:
2
Logic Engineering in Medicine: Methods and Architectures. Collaboration with Institut Bergonié,
Bordeaux and the New University of Lisbon, Portugal.
3
Clinical REsearch Workstation
Generic tools


A 'knowledge based' electronic medical record (EMR) system [24], using a standard patient data model
and providing storage of care plans. The EMR provided a variety of views of the data, including
diagnosis, problem-, treatment- and time-oriented views.
An integrated decision support and protocol execution engine (DSPE) which was interoperable with
other applications, such as graphical user interfaces and patient record systems.
Applications
The DSPE was successfully used to implement the following applications:
1. A breast cancer protocol manager, integrated with the DILEMMA EMR system [25].
2. PARSEC4, which demonstrated decision support functions in a cardiology setting
[25].
3. CAPSULE5, a decision support application for routine prescribing in primary care
[25]. This used a knowledge base of drugs and their attributes (uses, side-effects
contraindications, costs etc.) to generate a short-list of potential medications
appropriate for a specific patient.
4. The SYNERGY system [25], which was linked to a commercial EMR to deliver
guidelines (for the management of acute asthma and otitis media) at the point of care.
The DSPE was responsible for data acquisition, task control and decision support.
Research work in DILEMMA also explored the use of qualitative reasoning in drug prescribing
and dosage decisions [26], and developed techniques from distributed AI and multi-agent systems
to explore problems in supporting multidisciplinary care [27]. In the latter activity, software
agents were designed to act as assistants for several clinical staff in a scenario of shared care of
breast cancer patients. Each agent contained a DSPE, extended to include a set of transaction
rules for implementing communication of patient data, delegation of tasks and management of
workflow.
Related projects
The RED6 project, which is discussed in detail later, aimed to develop a formal foundation for the
concepts and techniques of logic engineering. It was especially concerned with developing
techniques for ensuring that decision support systems are safe. A focal application for the work
was in the management of chemotherapy protocols. The OaSiS7 system [17] built on ONCOCIN
and earlier work in LEMMA and DILEMMA, introducing a logic-based decision support
function (figure 2).
4
PAtient Records for SharEd Care, developed at the Royal Brompton Hospital, London
Computer Aided Prescribing Study Using Logic Engineering
6
Rigorously Engineered Decisions, funded by the UK’s Engineering and Physical Sciences Research
Council within a national research programme on safety-critical software.
7
Oncology Support System using Artificial Intelligence
5
Figure 2: A view of the OaSiS chemotherapy protocol manager. This provided support for routine
collection of data and prescribing of cytotoxics, together with functions for hazard alerts and
decision support.
OaSiS initiated an important line of work: the systematic investigation of requirements for
managing therapeutic procedures with possible safety implications. At the start of the study,
Hammond reviewed in detail some fifty cancer therapy protocols to identify clinical
circumstances where safety issues arose (such as where the absence of treatment might be
hazardous, or where inappropriate or continued use of particular treatments might be dangerous).
For example, Hammond identified the following remarks in two unrelated protocols:
“Use of pre-hydration or anti-emetics [is recommended] to counteract the dehydration due to
vomiting, or the vomiting itself arising as a side-effect of many chemotherapeutic drugs”;
“Folinic acid rescue carried out in conjunction with administratio of high dose methotrexate
chemotherapy to try to balance the bone marrow side-effect”.
He was able to translate these into a clear general principle that it is necessary to:
counteract any expected, serious, toxic effect of a planned chemotherapeutic action.
Hammond identified nine domain-independent principles of this kind [17] and expressed them in
the form of first-order logic rules, such as:
IF
component(Plan,Action1)
AND
AND
AND
AND
THEN
produces(Action1, Effect)
unsafe(Effect) OR undesirable(Effect))
can_ameriorate(Action2,Effect)
not incompatible(Plan,Action2)
add_to_plan(Plan,Action2).
Such rules can be executed in a logic programming language, such as Prolog, for automatic
modification of the design of therapy protocols, and dynamic repair of care plans.
The DILEMMA project also led to an effort to address important issues in medical image processing and
interpretation. Although many patient data are symbolic, much clinical information comes in analog
form (e.g. signals from monitoring devices and imaging systems). The integration of signalanalysis and clinical decision making in a general way remains an unsolved problem.
Furthermore, the fields of imaging and signal processing and those of knowledge based systems,
decision support and protocol based care are sharply separated, in terms of the functionality
which is required and the technologies which are used [28].
The CADMIUM8 project produced a prototype “radiology workstation” (figure 3) which
introduced a number of innovations [29], notably a demonstration of the use of logic
programming techniques to link processes of reasoning over qualitative symbolic data (e.g.
patient history data) with problem-directed measurements and descriptions of image features.
CADMIUM has been shown to improve decision making by radiographers looking for abnormal
micro-calcifications in mammograms (see below). The work has also resulted in a knowledge model of
several important decision tasks in radiology - detection, measurement and description.
The CADMIUM II project is now starting, funded by the UK’s Engineering and Physical
Sciences Research Council and the Imperial Cancer Research Fund. The major objective is to
reengineer the prototype as a practical clinical system and to assess systematically its value in
routine radiology.
8
Computer Aided Decision-Making and Image Understanding in Medicine.
Figure 3: A view of the CADMIUM system being used in breast cancer screening. The background shows
a mammogram, together with a patch of a breast which contains micro-calcifications. These have been
automatically located by the software; an enhanced image appears top right. The inset panel at bottom left
shows the state of execution of the screening protocol (the tasks which have been complete or are intended
for execution), and the panel at bottom right presents an assessment of the abnormal features in terms of
their support for malignant or benign conditions. The execution of the image processing is carried out
automatically as part of CADMIUM’s decision procedure.
A more recent application of logic programming and symbolic decision processes has been developed for
assessing and communicating genetic risk information. As more and more genetic markers for serious
diseases become available, healthcare professionals may be expected to carry out a number of tasks in this
area: systematic collection of family history data; construction of an appropriate family tree; calculation of
the appropriate disease risk, and finally explanation of the results to the enquirer. The RAGs system,
illustrated in figure 4, has been designed to support all these activities, focusing initially on breast and
ovarian cancer.
Figure 4: A view of the RAGs risk assessment system. RAGs automatically generates a pedigree
from family data, assesses the arguments for and against elevated risk, and generates a text report.
The program generates appropriate forms for collecting information about the client and her
family, and uses this information to construct a family tree. All information gathered is then used
to appraise the person’s risk level (high, medium or low), and a report is generated which
summarises the arguments for and against this assessment. The application is currently
undergoing evaluation in a study with general practitioners.
4. Industrial strength technologies and commercialisation
The goal of the PROMPT9 project (supported by the EC’s 4th Framework Health Telematics
programme, 1996-8) is to develop the core technology of a clinical workstation that incorporates
the functionality developed in the earlier projects. The structure of the PROMPT workstation is
summarised by the cloverleaf motif in figure 5.
9
PROtocols for Medical Procedures and Therapies
PROforma
Reference
data and
knowledge
Patient
record
system
Comms &
shared care
Figure 5: Component parts of the PROMPT clinical workstation
The patient record system [24] is based on the patient data model and prototype EMR developed
during DILEMMA. The Reference data and knowledge component is a browser providing access
to a collection of information sources, held locally or remotely on a network. The shared care
component includes a set of conventional communication functions. Initially, this will permit
multiple users to share patient information and care plans. Later, we plan to introduce support for
delegating and managing clinical tasks between members of a care team, based on the technology
described by Huang et al [27] and the KQML agent transaction language which is widely used in
multi-agent systems research [30].
The fourth component of the PROMPT workstation, the PROforma toolset, is the main current
focus of our logic engineering methodology. It is designed to add value to the other functions by
adding reasoning, decision making, task scheduling, plan management and other capabilities to
conventional patient data retrieval and analysis, medical knowledge and protocol libraries, intersystem and inter-agent messaging. We shall look at PROforma technology in more detail in
section 6, but briefly its main components are as follows:
 The PROforma formalism, an extended logic language oriented toward the specifications of
decisions, plans and other medical tasks. It is a knowledge representation language in the
artificial intelligence tradition, and a formal specification language as that term is used in
software engineering.
 Composer, a graphical design environment for developing and verifying PROforma
specifications.
 Performer, an encapsulated execution engine which can enact a PROforma specification and
can be embedded in point of care or legacy applications.
PROforma technology also provides a central software component for designing and managing
clinical trials, which are being developed in a further EU funded project, MACRO10. The
principle services that MACRO offers to researchers are a graphical study design tool which is
used to create data collection schedules, and electronic case report forms incorporating data
verification (figure 6). Each study is defined in an extended version of the PROforma11 language
such that a definition can be automatically exported to centres collaborating in a trial via the
World Wide Web. Each collaborator can access the study definition via an MS Windows client
application, or via a standard WWW browser. Whatever software is used, the client can run the
trial protocol as required, e.g. for displaying active forms for entering and validating data, or for
Figure 6: A view of the study design component of the MACRO multicentre clinical trials
software. A trial is modelled as a collection of information recording tasks carried out during
patient visits (such as completing electronic forms to report patient data or adverse events during
chemotherapy sessions in an evaluation of therapeutics in breast cancer). The 'visit by task' matrix
10
co-ordinated by the European Organisation for Research into the Treatment of Cancer, Brussels.
The PROforma language is an interchange language for tasks only and is not a formalism for specifying
visual layouts, such as forms or graphical user interfaces.
11
(shown here) and the data entry forms are created with "drag and drop" design tools. The
PROMPT DSPE is used for task management and for data validation at run time.
exporting valid data sets in encrypted form to the trial administration centre. At the time of
writing, MACRO Trial Manager has been adopted for use in cancer therapy trials by the British
Medical Research Council and the European Organisation for Research into the Treatment of
Cancer, though the software can be used for managing controlled trials in any medical field. An
interactive demonstration can be found at http://www.infermed.com/macro/demo.
5. Evaluation studies
Empirical evaluation studies of a number of the applications described earlier have been carried
out during a number of the projects described.
In 1990, a special issue of Medical Informatics on "Validation and testing of medical decision
aids” [31] published results from ten European projects, many of which are still of interest.
O’Neil and Glowinski [32], for example, considered the special methodological difficulties of
evaluating large applications, using the Oxford System of Medicine as an illustrative case. They
considered central issues like medical performance (accuracy, sensitivity and specificity of a
decision aid), and other important questions such as the coverage, robustness, flexibility and
relevance of the advice offered by a system.
Their paper also included an evaluation of the OSM decision procedure applied to the question of
admitting a patient into a coronary care unit on the basis of his/her classification into particular
diagnostic categories. Using ROC analysis, the performance of the OSM was compared on a
number of dimensions with a standard Bayesian method. While noting that a single comparison is
not sufficient to address all the relevant assessment criteria, they found that in terms of decision
making, the performance of the two methods was indistinguishable.
Many in the decision making community will find this result surprising, since the logical
procedure essentially ignores the strength of individual pieces of evidence, while the Bayesian
procedure uses all the available information. Nevertheless, similar results have now been reported
in a variety of studies [33, 34, 35]. On the basis of considerable experience, we would make the
following claim: in much practical medical decision making, the crucial thing to get right is the
logical structuring of the problem. If it is wrong, no amount of precise calculation will produce a
correct decision; if it is correct, in very many applications quite simple decision procedures will
produce the same recommendation as those based on precise calculations. In rare situations where
the procedures produce different results, the evidential basis for the different recommendations
will be marginal, and it may be unwise to take important decisions without further corroboration.
The performance of the CAPSULE prescribing system has also been quantitatively assessed. In a
carefully designed crossover study [36], 42 general practitioners were presented with 36
simulated patient cases, constructed from actual consultations. Computer support in the control
condition consisted simply of an alphabetic list of all available medications. Two further
conditions were defined: in the first, the computer suggested a short list of drugs appropriate for
the specific patient; in the second, the arguments for and against each of these alternatives were
also made available. Computer support significantly improved the quality of prescribing in a
number of respects. With decision support, decisions made by the doctors agreed with the
recommendations of an expert panel 70% more often than without support. In the control
condition. the doctors ignored a cheaper, equally effective drug in 50% of cases; this figure
reduced to 35% when decision support was provided. In terms of acceptability, 88% of the GPs
found the system easy to use, and 59% said they would be likely to use it in practice. A
demonstration of CAPSULE can be found at http://www.infermed.com.
A further evaluation of the use of a decision procedure based on logical argumentation has been
carried out in decision support involving image interpretation. One application of the CADMIUM
radiology workstation is in breast cancer screening. A protocol has been developed which sets out
the routine tasks required, including patient registration, mammography, a review of
mammograms and an assessment of whether there is any reason to suspect the presence of a
malignancy. When CADMIUM gets to the point where this latter decision is to be made, the
decision procedure automatically launches the appropriate image processing and interpretation
programs (as described in [37]). The imaging functions segment out the breast, then search for
abnormalities, notably calcifications (illustrated in figure 3). Calcifications can result from both
malignant and benign conditions, but their density, form, clustering and other characteristics
provide information which can be used to distinguish their type.
Taylor et al [38] carried out a study of whether the decision support that CADMIUM provides
can improve the classification of micro-calcifications by radiographers. Twelve radiographers
with specialist training in mammography were presented with a number of standard reference
images from patients with known diagnoses. In the decision support condition, CADMIUM
carried out the image processing and provided the radiographers with a report on any
calcifications present (in the form of arguments for and against various malignant or benign
conditions) based on their features. Decision support produced a significant improvement in the
radiographers’ decisions, by increasing hits and correct rejections and by reducing misses and
false positives (table 1).
TABLE 1: EFFECT OF DECISION SUPPORT ON RADIOGRAPHERS'
CLASSIFICATIONS OF MAMMOGRAMS
Without
decision
support
With
decision
support
True
positives
False
positives
True
negatives
False
negatives
11
8
58
11
15
4
62
7
Taylor’s work demonstrates a principled way of integrating the diverse worlds of decision
support and imaging by using logical argumentation to achieve two goals simultaneously: to
interpret information extracted from the image, and to control the execution of the image
processing software which carried out the extraction. The results suggest that this technique may
also have practical diagnostic value.
Finally, a pilot study of the impact of a disease management guideline is being carried out in a
primary care setting within the PROMPT project12. This is a multi-task guideline for patients
presenting with dyspepsia. The guideline is broadly similar in structure to those implemented in
the Synergy system mentioned earlier. It provides the GP with prompts and reminders for
collecting relevant information in carrying out a number of decisions, including whether to refer a
patient for specialist investigation, whether to investigate with endoscopy, or whether to prescribe
h2-antagonists, proton-pump inhibitors etc. Table 2 summarises the results obtained for 80
patients seen by GPs in the researchers’ own practice. All the doctors in this practice have
computer systems on their desks, and they could access the support system by simply pressing a
button on the computer screen. Patients presented in the usual (unpredictable) way and when the
doctor realised that the patient was complaining of dyspeptic symptoms then s/he would either
manage the patient according to routine practice, or by using the computerised guideline,
according to a predetermined schedule.
TABLE 2
EFFECTS OF DYSPEPSIA GUIDELINES ON GP DECISIONS
Av. No. of
consultations
for this problem
Av. Cost of
medication
Av. No.
Blood tests
Av. No.
Endoscopy/XR
Av. No.
referrals
Without
Guideline (n=40)
With
guideline (n=40)
1.65
1.5
£26.6
£8.45
0.05
0.2
0.1
0.2
0.1
0.4
The results shown in table 2 suggest that decision support has a clear effect on practice by
reducing numbers of consultations and medication costs, and increasing investigations and
referrals. The participating doctors consider that the guideline is simple to use, and suggest that it
could help improve quality of care by enabling:


12
Earlier pick up of cancers;
A shorter period of maintenance medication with expensive proton pump inhibitors or H2
antagonists;
The study is being carried out by Drs. Colin Lyons, Peter Wilson and colleagues at North End Medical
Centre, London.


Earlier eradication and cure of proven ulcers;
Better targeting for prescribing.
These results demonstrate the versatility of the logic-oriented approach to the design of medical
decision support systems, and suggest that they have a useful contribution to make in practical
patient care.
6. Towards a sound and rigorous methodology for logic
engineering
As with any medical technology, the efficacy of individual applications cannot be considered in
isolation; in our view we also have an obligation to show, as far as possible, that the general
techniques are formally sound and that the technology is safe. This position is based on an
analogy with engineering in other safety-critical areas such as aerospace engineering. Just as
practical trials are important before introducing any new clinical technology, test flights are an
essential part of aircraft engineering. However, the high safety demands on aircraft builders have
led them to develop formal design theories to guide and verify the design and construction
processes. The value of such theories is that they can help to predict the properties and failure
modes of an aircraft design before it is built. This has major cost as well as safety benefits.
The RED13 project explored the use of formal theories of reasoning and decision making, rigorous
specification techniques, and systematic design and implementation of software in order to
optimise soundness and safety. The definition of a formal framework for describing clinical
decision making and process management was the main focus of the theoretical work in this
project. It is summarised by the “domino model” shown in figure 7.
Clinical
goals
Patient
data
Actions
Possible
solutions
Decisions
Care plans
Figure 7: The “domino” model of clinical decision making and process management
Each node of the domino represents information that is relevant to a particular clinical situation,
such as facts about a patient’s history, decisions and other tasks in progress, actions which are
13
Rigorously Engineered Decisions.
planned. Each arrow represents an inference procedure that uses information of the type shown at
the arrow's tail in conjunction with information from a patient record and/or general medical
knowledge base, in order to generate information of the type shown at its head.
This model is an abstraction of the various decision types explored in the applications projects
described earlier (in general practice, oncology, cardiology and radiology), and the task
management functions required for enacting practice guidelines, therapy protocols and other
clinical procedures (in oncology, radiology, clinical trials). The decision logic is subsumed by the
four nodes to the left of the diagram, and the processes required to schedule and enact tasks are
summarised by the four nodes to the right.
The RED project formalised the inference processes of the domino model in terms of nonclassical logics, and demonstrated the consistency and completeness of the logics [19, 39]. To
illustrate this work, we may consider a central concept in medical decision making, that of
uncertainty management (e.g. uncertainty about the diagnosis or prognosis of a patient, the
severity of a condition, which tests to do or medication to give). The intuitive method of
reasoning under uncertainty used in all the applications outlined above emphasises analysis of the
reasons for decisions, rather than the classical framework of abstracting uncertainty into a
number. This idea can be given a clear interpretation in terms of a logic of argument (LA [40,
41]), a kind of labelled deduction system [42]. Formal argumentation can be used in combination
with classical quantitative decision parameters (probabilities and utilities) or alone when
quantitative information is not available [41, 43].
A second important result of the RED project was the development of a systematic methodology
for developing applications based on the formal knowledge representation, decision making, and
task-scheduling theory developed during the project and summarised in the domino model. This
provides a strong set of constraints for checking consistency and completeness of an application
knowledge base. The primary methodology incorporates a conventional software life-cycle model
(figure 8) which is grounded in the conceptual framework summarised in the domino model. The
PROforma toolset supports steps 2-5 of the development methodology shown in the figure.
The toolset combines the logic-based representation developed in RED with an object-oriented
representation of clinical tasks in order to achieve a balance between the clarity and precision of a
formal specification language, and a more intuitive representation of clinical concepts and
procedures. The domino model provides a good framework for defining the formal semantics of
PROforma [39], but it is rather abstract. We have therefore developed a complementary view in
which clinical procedures are conceived in a way that emphasises the clinical activities that the
application is designed to support. These are captured naturally using the concepts of objectoriented programming.
1. Generalised
design theory
2. Application
task analysis
3. Detailed logic
specification
4. Analysis & verification
of PROforma
specification
5.
Execution testing
Figure 8: The RED logic engineering development lifecycle
In the object-oriented view of PROforma, an application such as a protocol or clinical guideline is
modeled as a plan made up of one or more tasks. PROforma supports four basic classes of task.
1. A decision is a task that involves a choice of some kind, such as a choice of investigation,
diagnosis or therapy. The PROforma specification of a decision task defines the decision
options, relevant information and rules by which the pros and cons of different options can be
derived.
2. An action is a clinical procedure that is to be enacted outside the computer system, such as
the administration of an injection.
3. An enquiry is an action that yields information. The specification of an enquiry includes a
description of the information required (e.g. the type, range and other properties of a clinical
parameter) and a method for getting it (e.g. by creating a data entry form, by querying a local patient
record or a remote laboratory database, or by processing an image).
4. A plan is a set of tasks, which are to be carried out to achieve a clinical (e.g. therapeutic) goal. Plans
are composed of any number of PROforma tasks, including sub-plans, and are usually ordered to
reflect logical, temporal or other constraints.
Generic
task
Figure 9: The task ontology supported by the PROforma guideline specification language
Tasks are defined with a class hierarchy or ontology, as shown in figure 9. They are modelled in
an object-oriented style in the usual sense that:
(a) Any specific clinical task is seen as an instance of some more general class of tasks. Each
class is eventually a specialisation of a root task, (at the top of the figure). Any class can be
further specialised into particular sub-types. For example, decisions can be specialised into
diagnosis decisions and therapy decisions; plans may be specialised into research protocols
and routine guidelines.
(b) Every task inherits part of its specification from the classes above it in the hierarchy,
expressed as a set of attributes. A task is distinguished from its parents by the possession of
additional attributes, and distinguished from its siblings by different values for their common
attributes.
(c) When tasks are enacted, the communication of information between tasks is achieved by
passing messages between encapsulated task objects, rather than by explicit procedure calls
or other mechanisms that require access to the internal definition of a task.
Detailed technical definitions of the PROforma task hierarchy are given in [19].
Figure 10: The PROforma Composer (composition tool). The centre panel is used to lay out the
basic structure of an application using icons for the four classes of task. Each recursive task (a
plan) adds another design area which can be accessed as required. Arrows are used to define
logical and temporal relationships between tasks, and details of each task are provided by means
of tools accessed via the task table on the right. A global overview of the whole task structure is
shown via the tree on the left. Automatic verification of any task, or the whole application, can be
invoked at any time. The inset panel (bottom left) shows the result of calling the task checker.
This uses the strong task model which underpins PROforma to identify incomplete and
inconsistent definitions in the task specification.
The main concepts of the domino model are embodied in PROforma. The model can be used to
specify the knowledge required in medical decision making and the management of clinical
procedures, and is sufficiently well defined to permit a high degree of automatic analysis and
verification of applications.
Figure 10 shows the guideline Composer, which supports the representation of clinical
procedures in terms of the four standard tasks. Here it is being used to integrate several tasks
concerning the management of breast cancer (which were discussed earlier as separate
applications). Further tools are provided to populate these outline task definitions with medical
content, and verify the consistency and completeness of the definitions. The process of building
an application consists of laying out a structure of this kind, and populating the details of each
task using a standard set of templates for each task class. The system then checks the application
definition, generating an error and/or warning report if necessary. When the application is
complete, a comprehensive set of test functions is provided to permit the designer to step through
the application to ensure that its behaviour is as intended.
The PROforma toolset has been successfully used to implement a wide variety of applications in
primary, secondary and tertiary care. It is available as a commercial product and for academic use
from InferMed [44].14
7. Conclusions
The emergence of knowledge based systems, and European strengths in formal software
engineering and logic programming, created an opportunity to develop the new approach to
clinical decision support and disease management described in this paper. There have been many
promising results from the work, of which a mature example is the PROforma language and
toolset. Like other researchers in medical informatics, we have found that there are many
pressures to produce quick results. But in a safety-critical field like medicine research must be
both broad and deep. The creation the PROforma technology was only possible because of longterm funding provided through national and European research programs.
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