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Computers, decision making and clinical effectiveness
Paper prepared for the Institute for Public Policy Research
John Fox1
Imperial Cancer Research Fund
Lincoln’s Inn Fields
London WC2A 3PX
The problem
Until recently, most citizens in countries like the UK felt sure that if they had medical problems they would be
dealt with promptly, and as effectively as the medical art would allow. Furthermore, most of us look forward to
continuing improvements in this art, as scientific understanding of diseases and their management increases.
Although standards in most developed countries remain high, public confidence is apparently falling. The public
frequently hears of service failures and high profile legal actions. We are all becoming increasingly aware that
standards of care are not consistent across society. The increasing medical needs of an ageing population; rising
costs of more and more sophisticated treatments and procedures, and a demand to provide an ever wider range of
services with a limited budget, are all creating challenges to traditional ways of organising and delivering care.
Furthermore, the unprecedented growth in our understanding of diseases and their management is not matched by
equivalent abilities to apply that knowledge in practice. The problem is that it is now impossible for even the
most dedicated medical professionals to stay up-to-date and know everything he or she may need to know about
new drugs, new procedures and even new diseases. It has long been impossible for individual doctors and nurses
to know everything there is to know about general medicine; it is now often impossible for them to keep abreast
of developments in their individual specialties.
Case: the treatment of cancer
Studies of cancer treatment have shown that although there seems to be an international consensus on “best
practice” for many patients who contract the disease, the expectation of a positive outcome can vary considerably
between treatment centres. In particular, specialist centres have been shown to produce significantly better
outcomes than general units for many cancers (Selby, 1995).
This is suggestive. Specialist centres have much more experience than general units of course, and will often have
more resources, better equipment and so on. But this is not the whole story. Another difficulty is the slow
process by which new scientific knowledge is disseminated throughout the clinical community, and there are our
individual limitations. Human beings are not all equally able to absorb new knowledge or equally open to
evidence that their established practices are not the best. Consequently it is difficult to avoid a “lottery”: some
patients get state-of-the-art care while others get something less.
Variations in quality of care have been recognised particularly in the cancer field because society concentrates
considerable resources on studying the causes, prevention, diagnosis and treatment of this disease. However, the
1
This paper was prepared at the invitation of the IPPR, but the views contained in it are those of the author
and should not be attributed to the Institute.
2
same organisational and human factors are operational in all fields of medicine and there is growing evidence of
similar variations in outcome in other specialties.
Case: general medicine in the community
As a contrast to specialist care, consider the GP who is responsible for providing front-line care for a couple of
thousand people. The GP requires a basic knowledge of a vast number of diseases and drugs. The GP is the
gatekeeper to hundreds of services for conditions that require specialist expertise, and therefore needs to have
enough knowledge of what these services offer to be able to decide when they should be called upon and how to
access them.
This adds up to a prodigious personal and educational challenge: we expect the doctor to possess, and keep up-todate, tens of thousands of pieces of information. This is an impossible demand. The reality faced by all doctors is
that they have too much to do, too much to know, and too little time.
The solution to this problem cannot be to blame the individuals or the organisations that provide our medical
services, punishing them through professional sanctions (or even the courts). What is needed are better ways of
ensuring that, so far as is possible, information and knowledge are disseminated more effectively and brought to
bear in a timely way.
Some possible remedies
The traditional solutions to such problems are to extend professional training and in-service “refresher” courses
for healthcare professionals. But these are palliatives at best; training is expensive, and medical knowledge is
increasing at a rate many times faster than formal training courses can accommodate.
An alternative is to encourage more specialisation, but the effect of this can only be that individual clinicians
know more and more about less and less. Most doctors have horror stories about patients who suffered
unnecessarily because their conditions required skills that crossed specialty boundaries.
More ambitious solutions are sometimes suggested, such as attempting to reduce the incidence of disease through
improved public health policy or education. However, such changes are painfully slow. The processes that create
the demand for more and better healthcare, such as the increasing age of the population, occur far faster than
changes in policy or dissemination of public health knowledge.
The political response to these circumstances can be to demand greater “efficiency” of healthcare professionals to insist upon improved performance or better management. But improving the performance of skilled people is
not generally achieved by merely exhorting them to work harder, faster or more cheaply. Healthcare professionals
probably make decisions, plan their time and remember what they need to remember about as well as they can. If
they are to perform significantly better then they are likely to require help in some form.
For these and other reasons the delivery of medical services is increasingly perceived as in crisis. Many people
now believe that the only practical hope of overcoming, or even containing, the threat to public health is to
introduce various forms of new technology. Among the most promising are “decision support” technologies that
can assist clinicians in their decision making and in the organisation and management of their work.
What is decision support?
A typical decision support system is designed to assist the clinical professional in making decisions about the care
of a patient in such a way that the outcome of care is likely to be improved. An obvious form of support system
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that is widely adopted is the paper guideline, such as a pamphlet that provides an aide memoire to clinical staff in
managing a particular category of patients. In the present context, however, the term decision support system
(DSS) is used to refer to computer systems that offer information to the clinician in a more flexible form than can
traditional paper media.
Most of the early research on computer-based decision support systems was aimed at providing assistance for
decisions about a patient’s diagnosis or the selection of treatment. However, the term is now more widely used to
include any kind of decision - including the choice of drug to prescribe, what format and dose to use, which tests
and investigations to order, what procedures to carry out, or whether to refer a patient to a specialist service.
The idea that computers can help in the management of disease is well established. It goes back to a seminal
paper by Ledley and Lusted (1959) who suggested that systematic, logical methods could play a role in medical
decision making. The practical potential of mathematical techniques in medical decisions, such as the diagnosis
of acute abdominal pain, was first convincingly demonstrated by Dr. Tim de Dombal at St. James’ Infirmary in
Leeds, as early as 1972.
For many years however it was widely felt that computers would never find a significant role in the process of
medical decision making, and that if they did it could only be to the detriment of the patient. This view was the
norm when doctors’ "clinical judgement" was seen as a human talent that was far beyond the capabilities of
computers; when the abilities and practices of every doctor were popularly assumed to be comparable to those of
the best in the profession; and before efficiency and the rational use of resources had acquired the importance
they have today.
In this paper I provide a brief overview of some of the kinds of computer system which are being developed to
help improve patient care and some evidence of their efficacy and value, as well as sounding one or two cautions
on their adoption. In this short piece we cannot acknowledge all the work that is being carried out in the field. For
those who are interested in learning more about DSS technology and its relation to other aspects of medical
information technology, I would recommend Coiera (1997) for a readable and informative review.
One point that that should be emphasised at the outset is that DSSs primarily “add value” to existing
organisations and activities, rather than being a competitor to them. In particular I shall argue that there is an
important synergy between the goals of DSS designers and those of the movement for evidence-based medicine.
Both communities are concerned with helping medical services manage the growing challenges to their
effectiveness from the avalanche of new knowledge and budget restrictions outlined above.
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Examples of medical decision support systems
The UK is in the vanguard of countries that are introducing clinical decision support systems2. For example, the
NHS has been evaluating a decision support system called Prodigy which is designed to help the general
practitioner in routine drug prescribing. When the GP has arrived at a diagnosis for a patient Prodigy can be
called up to recommend possible medications. Although Prodigy has attracted some controversy initial
evaluations are promising and its use is likely to be encouraged.
Another system which is also aimed at GP prescribing is CAPSULE, developed at the Imperial Cancer Research
Fund. The intended use of this system is illustrated by the following scenario:
A middle-aged man visits his GP with long-standing mild osteoarthritis, for which he has been previously
prescribed naproxen, but his symptoms are no longer fully controlled. After reviewing the history, current
medications etc. a British GP would normally consider the various medications he commonly uses, make a
decision whether or not to change the dose or drug, and write out a prescription order for the pharmacist. If
CAPSULE is used, however, the doctor presses a button requesting suggestions. The system examines the
patient record, consults its comprehensive knowledge base of drugs and their uses, and displays a short-list
of potentially suitable medications. The GP can ask for an explanation of the pros and cons of any of the
drugs on this list, an example of which is shown in the inset at bottom right, as shown in figure 1.
Figure 1: the CAPSULE system being used to suggest medication for osteoarthritis
Here the user has asked to see the arguments for and against naproxen, which the patient is currently taking but is
not at the top of the list of recommendations. CAPSULE reports that the drug is a generic treatment of choice in
2
The UK medical IT community has been very innovative and the research base is very strong by
international standards. This offers great potential opportunities for UK industry.
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the British National Formulary, and that it has been effective in the past as well as suiting the patient because of
lack of side-effects. However, the patient has a history of asthma, and naproxen is contraindicated in this
situation. Rather than simply increase the dose of naproxen, therefore, the GP accepts CAPSULE’s suggestion
that paracetamol be substituted.
In a systematic study with 42 GP volunteers, Walton et al (1997) found that CAPSULE could help GPs make
decisions more quickly and improve their decisions quite dramatically - significantly increasing the quality of
prescribing without increasing cost.
A particularly compelling piece of evidence that computer-assisted prescribing can produce major benefits in
terms of quality and cost of care was recently reported in a US outcome study of the use of antibiotics and other
anti-infective agents for critically ill patients in hospital (Evans et al, 1998). Decision support led to highly
significant reductions in allergies to drug use and other adverse events, excess drug dosages and antibiotic
susceptibility mismatches. Highly significant reductions in drug costs, length of hospital stay, and total hospital
costs were also recorded.
Figure 2 shows an example of a more complex decision support system, for advising on the management of acute
asthma. This is designed to help in the management of an acute attack during the first hour or two after the patient
arrives at an accident and emergency department. A scenario for use of such a system is as follows.
A young man arrives at the hospital in the early hours of the morning suffering from a serious asthma
attack. Unfortunately no senior staff are immediately available, so the doctor on call asks the department
computer to locate a guideline for acute asthma management. It proposes one published by the British
Thoracic Society. This has been converted from the original text form in a way that permits the computer
to “enact” the guideline, and assist the staff through the first hour or two of the patient’s care.
Figure 2 shows a display of the guideline at a point early on in this process. The first panel shows the guideline
rendered as a set of tasks in a structured “task network” or workflow diagram. (Access to the original guideline
as a multimedia document and other published reference information is also available.) The second panel shows
the point at which the doctor has finished the first task, “initial assessment”, having provided various prompts,
reminders and data entry forms. The computer has suggested that the young man should provisionally be
classified as suffering from a moderate attack according to British Thoracic Society criteria, and the user has
accepted this suggestion.
The recommended procedure for managing a moderate asthmatic will now be initiated (the rounded box
at the top centre of panel 1). This procedure consists of reassessing the patient after 30 minutes, possibly
with further medication, and again after 60 minutes. The system prompts for relevant information when it
is needed. As the tasks are executed, the graphical interface shows which tasks are scheduled, overdue,
complete, and so on. The system also monitors the electronic patient record (not seen here) for any
information which might subsequently indicate a misclassification or other hazard and, if so, draw it to
the clinician’s attention.
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Figure 2: Computerised care guideline for the management of acute asthma in a hospital accident and
emergency unit. The first panel shows an overview of the network of tasks recommended in the
guideline. The second panel shows the system during the enactment of the first task of the guideline:
assessment of the severity of the patient’s asthma attack.
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Primary Technologies for Decision Support
Two main traditions have influenced theories of decision making and the design of decision support systems to
date:3 statistical decision theory and knowledge-based or “expert” systems.
Statistical decision theory and decision analysis
Decision analysis is strictly a family of techniques, though in practice statistical decision theory is the standard
paradigm of this tradition. On this viewpoint a decision process is viewed as one of calculating the relative merit
of different decision options, such as alternative diagnoses or treatments. For example, the likelihood of a
diagnosis is determined by calculating its probability, usually based on population statistics. Decision theory can
also be used to help make decisions about clinical actions. The expected value of a candidate treatment, for
instance, can be computed by combining quantitative utility measures (such as the desirability of the possible
therapy outcomes) with probabilities (of each of the possible consequences of the treatment). On this
mathematical approach the best treatment is the one which has the highest expected value.
Decision analysis is well established and well understood. If the assumptions of the theory are satisfied and the
input numbers (probabilities and utilities) are well estimated the method will reliably identify the best (or
“optimal”) decision. A clear and comprehensive presentation of the use of statistical decision theory in decision
making is Dennis Lindley’s classic text, Making Decisions (1985).
However, the statistical view is rather restrictive, and computer systems based on it can be difficult to set up. First
of all, the theory requires that the designer knows, or can estimate reasonably accurately, all probability and
utility parameters. This is frequently difficult in clinical practice. In addition, notwithstanding the undoubted
power and generality of the mathematical approach, it really only addresses a small part of the clinical decision
problem. The set of decision options and relevant information sources is frequently unknown when the need for a
clinical decision is first recognised, and may only emerge progressively as a care process unfolds. However, the
theory provides no guidance on such matters, as Lindley himself acknowledges:
“The first task in any decision problem is to draw up a list of the possible actions that are available.
Considerable attention should be paid to the compilation of this list [but] … we can provide no scientific
advice as to how this should be done”
Finally, experience shows that statistically based decision support systems are often unattractive to clinicians
because, rightly or wrongly, they do not naturally think about their patients and their work in quantitative terms.
In my judgement, a fair conclusion is that mathematical decision theory is an important intellectual foundation for
work on clinical decision support, and will keep a significant place in the development of future decision support
systems. However, many additional techniques will be needed to achieve the practicality, flexibility and
compatibility with human understanding that are needed.
The knowledge based systems approach
Knowledge based systems, often called “expert systems”, were introduced as an alternative to classical
mathematical methods for helping with decision making and other types of activity.
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There are other approaches, such as work in operations research and multi-attribute decision theory, but they do not
introduce major new issues which are important for the present discussion.
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“An expert system is a computer program that (a) reasons with domain-specific knowledge that is symbolic
as well as [quantitative]; (b) uses domain-specific methods that are heuristic (plausible) as well as
algorithmic (certain); (c) performs as well as specialists in its problem area; (d) makes understandable both
what it knows and the reasons for its answers; and (e) retains flexibility” (Buchanan and Smith, 1988).
Expert systems introduced many new ideas, notably qualitative techniques for capturing the knowledge of human
experts and emulating their problem solving and reasoning skills. This kind of symbolic representation is to be
contrasted with the numerical representations of mathematical decision theory. For example, in many expert
systems the diagnostic skills of a doctor are represented by means of if…then… rules, a simple but very powerful
knowledge representation technique.
Rules can be used to capture a surprising range of clinical thinking. They have been used to model reasoning
about the physical structure of organs, and the function (both normal and abnormal) of physiological systems.
They can emulate intuitive patterns of thinking about time and causality, incorporating uncertainty in a number of
different ways.
An advantage of knowledge based approaches over mathematical methods seems to be that they are more easily
understood by clinicians. Furthermore, rules and other knowledge-based techniques can model more kinds of
expertise than just the assessment of alternative hypotheses or actions. The more sophisticated methods can also
include rules for scheduling and planning as well as decision making. They can capture relevance (when and how
to use particular forms of knowledge), and they can express subtle concepts such as the clinical intention behind a
decision or action.
The designs of early knowledge-based systems were criticised for being ad hoc - lacking a rigorous theory to
justify them. This is a matter of serious concern if such systems are to be used where lives are at stake. Clearly,
we need to be confident that such systems are as safe as possible. However, the situation has improved rapidly in
recent years and knowledge-based decision support systems now have a substantial body of mathematical theory
underpinning them, providing confidence that they will be both safe and sound (Fox and Das, 1998).
On the face of it, qualitative methods should also be inferior to statistical ones because they are less quantitatively
precise, so one might expect them to make mistakes when fine judgement is needed. It turns out, however, that
lack of precision often has no serious consequences in practice (e.g. Fox et al, 1980; O’Neil and Glowinski, 1990;
Chard, 1991). Nevertheless, we must assume that there will be circumstances where precision is necessary, and it
is now possible to combine knowledge based techniques with methods from mathematical decision theory where
this is appropriate.
The focus of research nowadays is not so much on competing theories, but on when each kind of technology may
be used most profitably: exploring different types of application and establishing those areas in which they are
likely to have most clinical value.
Current forms of decision support
Coiera (1997) lists about 20 clinical decision support systems that are reported to be in routine use. These cover a
variety of functions in acute care, laboratory systems, educational applications and quality assurance and
administration. Another way of organising decision support systems is in terms of the types of assistance that they
offer. In this section I present a very short overview of the main types of function which are to be found in
current DSSs, starting with decision support based on paper guidelines to provide a context for comparison.
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Published guidelines and research results
There are many different types of paper guideline. They include:




comprehensive reviews and recommendations published by authoritative bodies (e.g. the British Thoracic
Society’s guideline for the management of asthma);
advisory pamphlets (e.g. the material distributed by pharmaceutical companies alongside their products);
brief, problem-oriented management summaries (e.g. the flow diagrams and bullet point summaries often
published in medical periodicals);
descriptions of research studies, possibly including summary tables and graphs.
Clinics and units often post published guidelines as reminders to clinical staff, and clinical audit groups write
local guidelines which are often bound together into reference manuals. Increasingly, people are experimenting
with similar material on computer, such as “multimedia documents”.
When printed and multimedia guidelines are kept short they are often perceived as useful (though brevity may
imply a compromise with quality). Lengthy documents, on the other hand, even when prepared to a high standard
by authoritative individuals or bodies, are often perceived as worthy but impractical. Busy clinicians may not
have time to read (even if they feel guilty for not doing so) and even when they do they may find it hard to absorb
the detail or to translate the general principles of best practice offered into specific advice for particular patients.
This material is an important first step towards systematising best practice but it is not the solution to the
problems described above. To deliver effective decision support the information needs to be provided in a form
that is relevant to the specific patient and at the point of care. Ordinary documents, whether on paper or on
computer, are not a good way of achieving this.
Clinical data analysis and interpretation
One of the simplest and least controversial forms of decision support is the provision of facilities to carry out
routine calculations, such as percentage peak flow or body mass index. A computer or calculator can be quicker
and more reliable than a tired head. Functions for detecting subtle patterns in clinical parameters, such as blood
sugar levels or blood pressures, and keying the results to recommended interventions, are also practical in many
settings. Systems which calculate treatment parameters, such as optimal drug dosages, can be more controversial,
though there is considerable research data to suggest that dosage calculations have considerable benefits in
clinical practice (Walton, 1998).
Electronic reminders
Doctors are people, and people forget things. Even under favourable conditions (and many doctors feel that they
rarely work under favourable conditions), slips occur, histories and other routines are not properly completed,
potentially important measurements are omitted or significant clinical features are missed. Techniques to help
remedy such common errors have been the subject of considerable research and the evidence that they can have
value is now strong. Computer prompts (e.g. to ensure that planned clinical actions are carried out on time),
reminders (e.g. for ensuring that full histories are taken) and alerts (e.g. for detecting adverse events) can lead to
better compliance with best practice, and improved outcomes (Johnstone et al 1994).
The introduction of computerised prompts and reminders may produce organisational challenges but requires
little technical innovation. The first successful attempt at this was the HELP system developed at the Hospital of
Latter Day Saints in Salt Lake City well over twenty years ago. The main technical difficulty is that a
comprehensive system implies the availability of some form of electronic patient record. Nowadays, commercial
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hospital information systems (mostly US products) and GP information systems (a UK strength) have built-in
support for providing prompts and reminders associated with some form of clinical data record.
Making decisions
In many respects the most controversial applications of decision technology lie in supporting the decision process
itself. The decision support functions summarised so far are not directly concerned with decision making, but
with the collection and processing of the information which will be needed for decisions about diagnosis, therapy
and so on, rather than making suggestions about a patient’s management, as illustrated in the examples above.
However, there are good reasons to believe that it is in this area that DSS technologies will sometimes have their
greatest benefits.
One reason for controversy is that the original focus of DSS research was diagnosis, deciding what is wrong with
the patient. With hindsight this was probably not the most promising place to start. Many clinicians say “our
problem is not diagnosis, it is management of the patient’s condition” and this observation appears to strongly
influence doctors’ behaviour. For example, doctors’ use of the Medline reference database was reviewed by Dr.
Jeremy Wyatt; he found that the great majority of clinicians’ queries were management-oriented rather than
diagnosis-oriented (pers. comm.).
Diagnosis is, of course, important but, as figure 3 illustrates, it is only one of many different types of decision that
clinicians take, and most of these are concerned with what to do rather than what is wrong with somebody.
Although there are reasons to believe that clinicians can be over-confident in their diagnostic ability there is
certainly one practical fact that supports their assessment of where the main decision-making problems lie: the
presentation of diseases is relatively constant over time but best practice in the management of disease is
continually changing. It is hardly surprising, therefore, if clinicians find it difficult to stay up to date.
This seems to be reflected in results of studies on the impact of clinical DSSs. Systems aimed at supporting
diagnosis decisions sometimes produce only modest successes (Johnstone et al, 1994). On the other hand several
studies have indicated great value in disease management decisions. We gave examples earlier in the area of
prescribing decisions. Similar results are becoming available for test selection. For example, Johan van der Lei
and colleagues at Erasmus University in Rotterdam have developed a system called Bloedlink, for assisting GPs
in making decisions about blood tests. In a one year study involving 60 GPs in Delft Bloedlink produced
significant improvements in compliance with guidelines (report in preparation).
For other common decisions, such as referral decisions and risk assessment, there is little data as yet, though
many doctors see these as areas where they do not achieve the standards they would like. The need to improve
resource management in clinical practice is also generally accepted. However, clinicians are naturally concerned
that reducing costs should not take precedence over improving quality of care (the PRODIGY prescribing system
has attracted some suspicion, probably unfairly, for precisely this reason). Walton’s study of prescribing showed
a highly significant increase in prescribing quality together with a 50% reduction in the number of times the GPs
missed a cheaper, but equally effective, substitute. In van der Lei’s study of test ordering he showed a 20%
overall reduction in test ordering much of which was due to reduced ordering of inappropriate or outdated tests.
There is apparently no conflict between better resource use and improved quality of care.
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History
taking
Physical
examination
Tests and
investigations
Defining the clinical problem
“clinical picture”
Diagnosis
Risk
assessment
Test
selection
Therapy
selection
Referral
Planning
& plan
revisions
Figure 3: some of the principle tasks carried out in clinical medicine, including just a few of the
different types of decision that doctors have to take (bottom). If we are to provide medical
professionals with practical tools, these must reflect the variety of medical procedures.
Workflow
One cannot discuss the effectiveness of clinical decision making without talking about the implementation of
clinical decisions. The basic decision is some sort of choice, of a medication, say, or whether or not to refer a
patient for further investigation. Other management decisions, however, particularly where difficult clinical
problems are involved, require care plans that include a number of procedures that must be carried out over time,
and may require coordination of different specialists and services.
Even a simple procedure such as a GP’s request for blood tests (recall the Bloedlink example described earlier)
may involve several tasks. First there is the initial diagnosis, then the selection of tests, and the preparation of a
request to the testing laboratory (by letter or email). Once the report comes back to the GP the patient needs to be
recalled and a decision taken about any management that is needed.
The organisation of such tasks and associated “workflow” is not difficult, but in practice it is notorious for
failures of communication, lost information, and patients “falling down the gap” between services. The growing
emphasis on multidisciplinary shared care may be expected to exacerbate these problems.
The functions that are required to implement workflow support include scheduling and time management
facilities, process planning and resource control, communication and coordination. A wide range of computer
techniques is available for assisting with these functions.
Integrated methods
The various forms of decision support described above are usually considered in isolation but they can also be
used in combination.
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In the long-term management of chronic conditions, such as diabetes, hypertension or asthma, there is a need for
many different tasks to be carried out in a coordinated way. These include regular reassessment of the patient’s
condition, detailed patient examination, analysis of trends and reassessments of treatment, as well as
administrative tasks. Since these patients may require care over many years we also need to provide support for
major reviews, as new medications become available or recommended practices change.
We are also likely to see increasing use of complex therapy protocols, which are already widely used in cancer
care. A cancer protocol will normally specify careful eligibility assessments, detailed data collection and
coordination of surgical, radiotherapy and chemotherapy services. Chemotherapy typically requires several cycles
of treatment, with precise combinations of cytotoxic drugs, constant monitoring for adverse events, and long-term
follow-up of the patient.
The asthma management application illustrated earlier was only concerned with an hour or two of care but it
illustrates some of the basic concepts of integrated decision support. It shows how a guideline, protocol, or other
description of recommended care can be formalised in terms of precisely described actions, decisions and other
clinical tasks (Fox et al, 1997). This ability to proformalise medical procedures permits us to describe a
recommended process of care in a form that can be enacted by a computer. The computer can subsequently act on
behalf of service providers and professional organisations wishing to encourage compliance with guidelines, and
shoulder much of the administrative burden for the clinicians4. Proformalisation also offers a new form of
medical publishing by disseminating descriptions of recommended practice electronically, in a form that can be
used directly to bring prompts, reminders and other forms of assistance to the point of care.
Medical expertise, medical publishing and evidence-based medicine
Medical expertise is traditionally acquired by a combination of apprenticeship and private study of published
material. Unfortunately we now have to recognise that these traditional methods are insufficient to give the
clinician all the knowledge that is needed and keep him or her up-to-date.
Some areas of medical IT can be seen as offering new forms of apprenticeship, such as virtual reality training,
telemedicine and teleconsulting. Decision support can be viewed as a new form of medical publishing, which
permits us to “publish expertise” as well as reference information.
Decision support technology may therefore offer an important mechanism by which the goal of disseminating
Evidence-Based Medical Practice can be achieved. Countless specialist organisations are publishing state of the
art guidelines for the management of specific conditions (such as the British Thoracic Society’s asthma
guidelines, and the American College of Psychiatrists’ guideline on the management of acute depression). Others
are carrying out systematic reviews of research across disciplines, and then distilling and publishing the results as
scientifically validated clinical guidelines for improving the consistency and quality of care (notably the
international Cochrane Collaboration).
This effort is widely seen as a way of bringing a more objective and scientific approach to developing clinical
practice. But it has a significant point of weakness; its results are generally published in the traditional way, on
paper. As we have discussed, busy doctors and other healthcare professionals have little time to read and absorb,
and hence properly apply, the contents of even modest-sized documents.
Decision support offers a bridge between the authors of evidence-based guidelines and the clinicians whose
behaviour they wish to influence. Figure 4 illustrates this relationship.
The technique of “proformalisation” - a contraction of proxy (“authorised to act for another”) and formalise (“give
definite form to”) - was developed by the Imperial Cancer Research Fund.
4
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As we discussed earlier, the development of decision support systems can be based on local practices or
individual clinical preferences. However, one could also start at the point where clinical research usually ends,
with the publication of a systematic review of trials of therapies and other procedures (point 1 in figure 4). At this
point the DSS designer can start to translate the validated evidence-based guidelines into a form that can be
enacted on a computer (point 2 in figure 4).
Figure 4: Synergy between decision support technology and evidence-based medicine
The next step is to test the logic of the computerised guideline against realistic data, though at this stage this
would not normally be in a clinical setting. The process progressively refines the medical content until agreed
quality criteria are met.
Point 4 is the first phase of clinical testing, in which a new guideline is evaluated in controlled conditions using
standard randomised trials techniques. If the outcome data from the trial warrant it then the system could be
published for use in routine clinical settings (point 5). Use of the Internet as a publishing medium is attractive
because of the simplicity of the process and speed of dissemination.
Finally, this approach offers a further bonus for clinical practice and research. If we are using electronic decision
support systems then it is possible to store clinical information about patients automatically, including the
decisions that were taken and the reasons for those decisions, and which actions were carried out and when. Such
a database is a rich source of information for clinical audit and local policy assessment. If outcome data are also
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recorded these can be automatically fed back to the research group that developed the guideline, in order for them
to carry out statistical and other analyses of outcome and inform discussions of clinical effectiveness.
Some possible negative consequences of decision support
The central theme of this report is the potential of decision support technology to improve consistency and quality
of care. But, whatever the improvements in clinical outcome or resource use, any new technology also has a
potential for negative side effects. It is likely that many professionals will be concerned about becoming deskilled, for example. There are real questions about whether the technology could come between the clinician and
patient, or otherwise undermine the human side of care. There are also problems of security, and the ethico-legal
issues which arise as the boundary between technical and human responsibility becomes blurred.
The time may be propitious to address these worries. Medical information technology, the requirements of
clinicians, the introduction of the NHS network and the efficacy of decision support systems in clinical practice,
are being widely debated. Changes to the NHS, and the role of IT in achieving these changes, are central to these
debates. The proposed National Institute for Clinical Effectiveness (NICE), described in the recent White Paper
on the NHS, could play an important role in monitoring and assessing the impact of decision technologies on
clinical practice.
Requirements for the successful introduction of clinical decision support systems
Even if the evidence for the value of decision support systems is widely accepted, there are significant obstacles
to be overcome before they can be put into general use.
Content provision.
The major requirement for developing any decision support system is, arguably, the medical knowledge base. The
database of drugs for a prescribing aid must include comprehensive information about their uses, indications,
contraindications, side-effects etc. In areas like pharmacology there are substantial sources of authoritative
information about drug use, but in other areas the background knowledge may not be available in a directly
usable form. The preparation of this knowledge base may be time-consuming since it may require extensive
review of evidence and discussion, and even some clinical controversy.
However, given the right organisation, the marginal cost of preparing a knowledge base in a form that is suitable
for computerisation may not be high. After all, the basic work described needs to be done as part of any effort to
improve clinical efficacy, whether the intended use involves computers or not. The movement for evidence-based
medicine is already a major source of high quality guidelines. Another possible role for the NICE could be to
oversee the translation of such content into a form that is suitable for use at the point of care.
Electronic medical records.
If decision support systems are to offer assistance that is appropriate for the individual patient, the means must
exist for acquiring and storing all the personal information that is relevant to the decision. Some applications will
only require basic data entry facilities. These are already available in commercial clinical information systems. It
is generally recognised, however, that in the longer term DSSs will require electronic record systems that can
store comprehensive and highly organised clinical data, reliably recording this information over days, weeks, or
even lifetimes.
The ability to record high quality structured data is needed for many reasons, not just decision support. Wellstructured records, using consistent medical terminology, easily accessible whenever and wherever they are
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needed, are the foundations of care. They facilitate good decision making, improved communication between
individuals and services, clinical (and financial) audit, and objective research on clinical effectiveness and
outcome. Political and economic forces as well as clinical needs are creating pressures to meet these
requirements, and efforts to deliver advanced record systems are a major focus of academic research and
commercial developments.
Fragmentation of research and development
There are two more practical problems that need to be addressed if DSS technology is to be disseminated and
used widely within a large health system such as the NHS. They are not primarily technical but organisational.
First, there is a particular need for agreed standards on data and knowledge representation. Decision support
technology has the potential to be used throughout medicine, but the scale of the problem should not be
underestimated. An indication of this is given by the widely used Oxford Handbook of Clinical Medicine. This
volume provides reference information for basic and routine medicine only, and its 800 pages cover about 400
common complaints and conditions, across 16 major disciplines, including 24 classes of emergency, and
miscellaneous other topics. This is the core knowledge base of the general physician, yet every topic in the
handbook points to a further and often large body of specialist information. If we develop DSSs for only a few of
the areas it covers there is obvious scope here for a Tower of Babel of magnificent proportions.
Another consequence of fragmentation is the common one of incompatible products. If there is no coordination of
developments then there will be incompatibilities between one hospital’s electronic record system and another
(already a problem) and between one supplier’s electronic system and another’s decision support products. A
further problem will be that clinical guidelines that are developed by one content provider may only “run” on
certain companies’ DSSs. Consequently, any general practitioner or medical director who wants to make a library
of guidelines available will have to purchase a number of products in order to use them. Not only will the cost
implications be unacceptable but the healthcare professionals who are asked to adopt them will naturally be
unwilling to spend their time learning to use many different products, with different functions and operational
procedures.
A great deal of work is currently in progress that demonstrates the need for coordination of developments,
including collaboration on standard knowledge components and decision support functions, such as drug
knowledge bases and generic guideline formats which can be used and reused in a variety of specific applications.
Current efforts to standardise clinical terminology and coding systems, such as the Read Codes are also important
here.
Good results will only be achieved through leadership and full consultation with clinicians, researchers and
industry. At the time of writing, when the new NHS IT strategy has not been finalised, it is not clear what will be
the appropriate mechanism for consultation. The NHS Executive itself may take the lead, through a revamped
Information Management Group, for example, or delegate responsibility to the NICE. Whatever the mechanism
the importance of consultation, particularly with the clinical end-users cannot be underestimated. A recent
editorial on clinical decision support systems in the New England Journal of Medicine (Garibaldi, 1998) argues
that lack of consultation and consequent lack of understanding of how healthcare professionals actually work, is
the main reason for failure in introducing decision support technology into the clinical setting.
Conclusions
The inexorable growth in medical knowledge is placing an impossible burden on healthcare professionals. It
is unreasonable to expect them to read, assimilate and correctly apply all the published information that is
potentially relevant to their work.
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Computer-based decision support systems offer a new form of publishing, permitting the electronic
dissemination and “enactment” of validated clinical guidelines to provide assistance in a patient’s
management. Such systems can provide timely prompts and reminders for busy clinicians, simplify data
recording and analysis, and assist in improved management decisions and workflow. There is good evidence
that these methods can help to meet growing demands for greater consistency and quality of care, while
simultaneously reducing costs.
Decision support technologies are not in competition with other approaches to improving clinical
effectiveness, such as improved patient data recording, clinical audit or evidence-based medicine. On the
contrary, the technologies add value to these efforts by providing a practical means of delivering their
benefits to the clinician at the point of care.
At a time when the NHS is contemplating major changes there is an exciting opportunity to foster the
introduction and use of these technologies, to improve services and relieve pressures on the organisations and
individuals in the NHS. The work of the proposed National Institute for Clinical Effectiveness could, in
particular, powerfully complement the existing initiatives of the NHS Executive and others in introducing
decision support into routine clinical work.
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