U.S. patent application number 12/350560 was filed with the patent office on 2009-07-16 for system for delivering scenario specific, problem solving, decision support from non-intelligent computer systems.
Invention is credited to Martin Patrick Entwistle.
Application Number | 20090182699 12/350560 |
Document ID | / |
Family ID | 19927438 |
Filed Date | 2009-07-16 |
United States Patent
Application |
20090182699 |
Kind Code |
A1 |
Entwistle; Martin Patrick |
July 16, 2009 |
SYSTEM FOR DELIVERING SCENARIO SPECIFIC, PROBLEM SOLVING, DECISION
SUPPORT FROM NON-INTELLIGENT COMPUTER SYSTEMS
Abstract
Computational decision making systems and methods are provided
for delivering scenario specific information. A decision making
scenario has a number of variables and associated values. Input
variables may be matched to data objects to find a match or the
closest match. Decision information embodying the outcome of a
decision making process may be associated with a grouped data
object and the decision information associated with the best
matching grouped data object or objects is provided.
Inventors: |
Entwistle; Martin Patrick;
(Albany, NZ) |
Correspondence
Address: |
CARR & FERRELL LLP
2200 GENG ROAD
PALO ALTO
CA
94303
US
|
Family ID: |
19927438 |
Appl. No.: |
12/350560 |
Filed: |
January 8, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10211782 |
Aug 1, 2002 |
7493299 |
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12350560 |
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PCT/NZ01/00016 |
Feb 7, 2001 |
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10211782 |
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Current U.S.
Class: |
706/48 ; 706/46;
706/50 |
Current CPC
Class: |
G06Q 10/10 20130101;
G06N 5/022 20130101; G06N 20/00 20190101 |
Class at
Publication: |
706/48 ; 706/46;
706/50 |
International
Class: |
G06N 5/02 20060101
G06N005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 6, 2000 |
NZ |
337157 |
Claims
1. A computer system comprising: a computer readable medium
including a plurality of decision data objects, each decision data
object including one or more decision variables and associated with
one or more clinical scenarios, a processor coupled to the computer
readable medium and configured to execute an instruction set stored
on the computer readable medium, the instruction set instructing
the processor to perform a method comprising: providing one or more
input variables to a user, receiving values for at least one of the
provided input variables, structuring the input variables and
received values into an input data object, matching the input data
object to one or more of the decision data objects, and providing
decision information related to one or more of the clinical
scenarios associated with the one or more decision data objects
that match the input data object.
2. The computer system of claim 1, wherein the decision information
includes a best practice associated with one or more clinical
scenarios.
3. The computer system of claim 1, further comprising a data input
means coupled to the processor and the computer readable medium,
the data input means configured to provide input variables and
decision information and to receive values for the provided input
variables.
4. The computer system of claim 1, wherein matching includes
comparing the decision variables to the input variables.
5. The computer system of claim 1, wherein the method further
comprises: providing additional variables to the user, the
additional variables associated with one or more of the decision
data objects that match the input data object; receiving values for
at least one of the additional variables; structuring the input
variables, additional variables, and their respective received
values into an additional data object; matching the additional data
object to one or more decision data objects; and providing
additional decision information related to one or more of the
clinical scenarios associated with the one or more decision data
objects that match the additional data object.
6. The computer system of claim 5, wherein matching includes
comparing the decision variables to the input variables and
additional variables.
7. The computer system of claim 1, wherein the decision information
includes a request for values for one or more additional
variables.
8. The computer system of claim 1, wherein the decision information
includes a recommendation.
9. The computer system of claim 1, wherein if the input data object
matches a plurality of decision data objects, the method further
comprises: providing additional variables to the user, the
additional variables associated with one or more of the decision
data objects that match the input data object; receiving values for
at least one of the additional variables; structuring the input
variables, additional variables, and their respective received
values into an additional data object; matching the additional data
object to one or more decision data objects; and providing
additional decision information related to one or more of the
clinical scenarios associated with the one or more decision data
objects that match the additional data object.
10. The computer system of claim 9, wherein the additional decision
information is structured as one or more additional decision data
objects.
11. The computer system of claim 10, wherein the method further
comprises storing the additional decision data object on the
computer readable medium.
12. The computer system of claim 1, wherein the decision
information is structured as one or more new decision data
objects.
13. The computer system of claim 12, wherein the method further
comprises incorporating the one or more new decision data objects
into the decision knowledge base.
14. The computer system of claim 1, wherein if the input data
object matches a plurality of decision data objects, the decision
information includes a treatment that does not conflict with a best
practice associated with any of the clinical scenarios associated
with the decision data objects matching the input data object.
15. The computer system of claim 1, wherein the decision
information includes a hierarchy, whereby selected data portions of
the decision information are linked to further detailed data, and
the further detailed data are selectively outputted or selectively
extracted for output.
16. The computer system of claim 1, wherein any of the input data
objects, decision data objects, clinical scenarios, and decision
information is provided to or received from a party other than the
user.
17. The computer system of claim 1, wherein any of the additional
data objects, decision data objects, and additional decision
information is provided to or received from a party other than the
user.
18. The computer system of claim 1, wherein matching includes
multivariable pattern matching or filtering.
19. The computer system of claim 1, wherein one or more of the
decision data objects belongs to a class, and each class is
determined by a type or value of variables required to identify a
match with the one or more decision data objects.
20. The computer system of claim 19, wherein the knowledge base is
structured hierarchically according to class.
21. The computer system of claim 1, wherein the processor is
further configured to provide the input variables and/or receive
the values using a form or template.
22. The computer system of claim 1, wherein matching includes using
a search engine.
23. The computer system of claim 1, further comprising an editorial
tool module adapted to allow the input, management, update and
customization of the plurality of decision data objects.
24. A computer system comprising: a matching function module to
compute a match or closest match between a set of inputted variable
information and one or more predefined sets of variables, each of
the predefined sets of variables associated with a predefined
clinical scenario for a patient with a specific set of medical
conditions, wherein each predefined set of variables is stored by
the system as a grouped data object, wherein specific clinical
management decision information is selected for output to a user,
the selected clinical management decision information associated
with one or more grouped data objects that match or most closely
match the set of inputted variable information, the matching
function module configured to request additional variable
information upon a determination that the set of inputted variable
information equally matches a plurality of predefined sets of
variable; a data function module to create and retain further data
objects defining additional predefined information in response to
the selected specific clinical management decision information and
inputted variable information; and an output module to output the
specific clinical management decision information to the user.
25. A method of supporting a patient-specific clinical management
decision using a computational system, the method comprising:
providing a clinical medicine decision knowledge base including a
plurality of decision data objects and corresponding information
related to outcomes of clinical management decisions associated
with the decision data objects; identifying one or more decision
variables associated with a clinical management decision associated
with a decision data object; providing the decision variables to a
user; receiving input data corresponding to values for the decision
variables associated with a patient having a medical condition;
structuring the input data into an input data object having a
structure complementary to at least one of the decision data
objects; matching the input data object to a plurality of the
decision data objects; providing additional decision variables to
the user, the additional decision variables associated with one or
more of the decision data objects that match the input data object;
receiving additional input data corresponding to values for the
additional decision variables; structuring the input data and
additional input data into an additional data object; matching the
additional data object to one or more of the decision data objects;
identifying specific clinical management decision information
associated with one or more of the decision data objects matching
the additional input data object; and outputting the specific
clinical management decision information to the user.
26. A computer system comprising: a processor; a computer readable
medium having instructions for execution by the processor, the
processor executing the instructions to: provide an input variable
to a user; receive a value for the provided input variable;
structure the input variable and the received value into an input
data object; match the input data object to a decision data object;
and provide decision information related to a clinical scenario
associated with the decision data object.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation and claims the
priority benefit of U.S. application Ser. No. 10/211,782, now U.S.
Pat. No. ______, filed Aug. 1, 2002, which is a continuation of
P.C.T. application number PCT/NZ01/00016, filed Feb. 7, 2001, which
claims the priority benefit of New Zealand application number
337157, filed on Feb. 6, 2000, each of which is incorporated herein
by reference.
FIELD OF THE INVENTION
[0002] The present invention relates to decision making or
knowledge-based systems. Such systems are part of a movement
towards the development of "intelligent" systems for use in problem
solving and decision making. Such systems are generally subject
independent, although certain applications may be more suitable for
implementation using a knowledge-based system than others.
[0003] More particularly, although not exclusively, the present
invention relates to applied, knowledge-based decision support
systems adapted to operate in a computing environment.
BACKGROUND
[0004] Decision making is an abstract concept that can generally be
thought of as a stimulation/response process usually seen in the
context of problem solving. The process is stimulated by a set of
information, including for example a set of criteria, a specific
question, or a set of factors which define an issue to be
addressed. The relevance of each piece of information relating to
the problem needs to be gauged, both individually and collectively
and ultimately the decision or outcome is made by matching these
inputs to rules, knowledge or experience pertinent to the matter in
hand.
[0005] At a less abstract level, decision making may be thought of
as a simple question/answer process whereby an almost infinite
potential source of information may be analyzed in order to match
the question with an answer on an isomorphic (i.e. one-to-one
basis). However, many decision making paradigms do not satisfy
simple single valued isomorphism as there may be any number of
competing variables which may influence or affect the outcome of
the decision making process.
[0006] Further, the decision making process should conform to an
accepted or pre-determined standard or "rule". In an abstract
sense, it is increasingly common that decisions are made based on
what is known as a "best practice" approach. Such decision making
processes may not be necessarily solely focused on the
determination of an empirical answer to a specific question. The
process may also or entirely involve subjective answers relating to
experience, intuition and instinct (articulated appropriately)
which have, over time, been associated with specific criteria or
variable patterns and/or values.
[0007] Such rules are frequently created and documented by
authorities or bodies of experts, or via a meta-analysis of the
pertinent body of knowledge. That is, the standards can be
evidence-based and can be thought of as including empirical as well
as experiential data.
[0008] Thus, the standards in effect describe the "rules" around
which decisions should be made and are intended to cover all or
most of the possible eventualities or variable patterns/values.
[0009] In everyday experience, we are often presented with a
specific instance of these possible eventualities or a specific
example of a pattern of variables with which a decision needs to be
made. An example might be determining the probability of
precipitation given specific data relating to the present weather.
In this case, the eventualities may include variables relating to
temperature, humidity, lapse rates and the like. The output of the
decision making process may be a probability of precipitation
within a set period.
[0010] In endeavouring to determine the "best" or optimal decision,
it may not be practical to be presented with or have access to the
full body of the relevant knowledge and expect to distil from it
information relevant to the particular instance or scenario in
question. Rather, an effective knowledge-based system should
address the specific scenario, be responsive to a users input and
provide a clear, relevant and focused decision or output based on
the input criteria.
[0011] Computer systems provide an ideal environment in which to
develop and model knowledge-based systems. Their abilities in
relation to data capture and storage, along with rapid search
capabilities and other data processing functions make them ideal
vehicles for the development and implementation of decision making
systems.
[0012] It is considered that the prior art solutions do not fully
meet the requirements of a flexible decision making system for the
following reasons. Prior art techniques are generally unable to
provide the specificity and speed required. Such techniques
generally use a subject/predicate approach or fuzzy logic, rather
than an object based approach, to deliver the required information,
and are reductionist in nature rather than attempting to support
real world situations.
[0013] In addition prior art solutions do not capture a body of
expert opinion and make it available so that a less experienced
user will be presented with the expert's solution in response to
given scenarios in a way that is entirely controllable and
reproducible through the way the knowledge base is established and
maintained.
[0014] Also, prior art architectures are not easily extensible.
Such a characteristic is considered desirable in that it allows a
variable range of situations or scenarios and a greater depth of
information. Generally many prior art systems require that the
decision making process and interface be an integral part of the
computer program which requires the knowledge base to be itself
integrated into the program.
[0015] In such models, the knowledge base is not managed in a
natural language and is generally concealed from the user. This is
particularly problematic when the knowledge and rules exist in a
narrative format (e.g. Standard Operating Procedures, protocols
etc). An individual with a working knowledge of the area can
determine the scenario matches from the advice presented, but would
struggle to interpret these as a set of logic based formulae.
[0016] To the applicant's knowledge, there are no decision making
systems which are built on open system principles, whereby any
client program conforming to the architecture specification can
interact with the knowledge base. The consequence of this is that
the accessibility and usability of the system is severely limited.
Finally, many prior art systems do not allow real time up updating
of the knowledge base. These types of system tend to rely on
distributing updates via email or CD ROM. Having the knowledge base
reside on a remote server operating on a client/server basis from a
central location overcomes these problems.
[0017] The applicant is aware of attempts in the past to develop
knowledge-based systems. Most deal with methodologies for defining,
capturing and storing the knowledge or rules, but are silent on how
the stored knowledge may be returned in a real world, situation
specific manner.
[0018] The Unified Modelling Language (UML) is a notation for
Object Oriented Analysis and Design outlined by Booch, Rumbaugh and
Jacobsen. This does not identify how stored information is returned
in the manner addressed in the proposed solution.
[0019] Common Object Request Broker Architecture (CORBA) is an
emerging open distributed object computing infrastructure being
standardised by the Object Management Group (OMG). CORBA automates
many common network programming tasks such as object registration,
location and activation, request demultiplexing, framing and
error-handling, etc. The CORBA ORB Architecture requires extensive
processing time in searching the knowledge base.
[0020] In the medical area an example of this is Arden Syntax for
Medical Language Modules which provides subject/predicate logic to
address very narrowly defined situations, but has no inherent
method for returning advice.
[0021] Another known technique includes the use of GLIF--the
Guideline Interchange Format. This corresponds to a standard
architecture for describing a guideline in a reproducible,
understandable and shareable format. Further related material may
be found in a project established by Stanford Medical Informatics
at the University of Stanford, California, known as Protege. This
system allows developers to build knowledge-based systems by
selecting and modifying reusable problem-solving methods and
epistemologies. This system corresponds to a suite of tools that
generate domain-specific knowledge-acquisition tools and
applications from the epistemologies.
[0022] It is an object of the present invention to provide a
decision making system which is capable of being distributed across
a network, is adaptable and efficient. A further or alternative
object of the present invention is to provide a decision making
system which overcomes or at least ameliorates some of the
deficiencies of the prior art or provides the public with a useful
choice.
[0023] Further objects of the present invention may become apparent
from the following description.
SUMMARY
[0024] In one aspect, the present invention provides a
computational decision making system suitable for delivering
scenario specific information, the decision making system including
a matching function to compute a match or closest match between a
set of inputted variable information with a plurality of predefined
and stored sets of variables, each associated with a predefined
scenario, wherein each set of inputted variable information is
encapsulated and retained by the system as a grouped data object
and wherein the scenario specific information is selected for
delivery dependent on which grouped data object or objects has
variables that match or most closely match the inputted variable
information.
[0025] Preferably, the system includes a data entry function to
allow creation and retention of further data objects that define
additional scenarios in response to the occurrence of one or more
particular scenarios.
[0026] In a further aspect the invention provides a method of
performing a decision making process using a computational system,
the method including the steps of: [0027] identifying a plurality
of discrete decision making scenarios; [0028] identifying a
plurality of decision variables and their values which are relevant
as affecting the outcome of a decision making process in relation
to each decision making scenario; [0029] collecting the decision
variables into one or more computer readable, logically grouped and
distinctly identifiable decision data objects; [0030] creating a
knowledge base, said knowledge base containing the decision data
objects and a set of corresponding decision information embodying
the outcome of the decision making process; [0031] receiving
through a data input means, input data representative of the values
of a plurality of input variables, wherein the input variables
correspond to the decision variables; [0032] structuring the input
data to form an input data object that has a structure
complementary to the structure of the decision data objects; [0033]
computing a best matching decision data object to the input data
object by comparison of the decision variables and input variables
associated with the decision data object and input data object and
identifying the corresponding decision information to the best
matching decision data object or objects.
[0034] Preferably, said decision data object may be structured and
handled according to object-oriented or object-relational
methodologies.
[0035] Preferably, if a plurality of equally best matching decision
data objects are computed, the method further includes the step of
requesting the input of at least one additional input variable and
computing which decision data object best matches the input data
object including the at least one more variable.
[0036] The method preferably includes the step of passing the input
data object to the knowledge base, wherein the knowledge base is
structured so that when it is queried using an input data object, a
further data object is returned containing only the decision
information.
[0037] In a preferred embodiment, the decision making scenario each
correspond to a particular medical diagnosis or condition, or
equally to a combination of medical diagnoses or conditions.
[0038] Preferably the best matching decision data object or objects
are computed by means of multivariable pattern matching or
filtering.
[0039] The best matching decision data object may be computed by
any other process which compares variable values of the input data
object with variable values of the decision data objects to
determine whether a match exists.
[0040] In a further aspect the invention provides a decision making
system including: [0041] a knowledge base, said knowledge base
containing a plurality of decision data objects, wherein each of
said decision data objects includes a plurality of decision
variables, which are identified as relevant in affecting the
outcome of a decision making process in relation to a decision
making scenario and wherein said knowledge base includes a set of
decision information embodying the outcome of the decision making
process corresponding to each decision data object; [0042] data
input means suitable for receiving data indicative of the value of
a plurality of input variables of the same type as the decision
variables; [0043] a processing means; and [0044] an instruction set
readable by the processing means including instructions to cause
the processing means to structure the input variables as a grouped
input data object, compute a matching or closest matching decision
data object to the input data object through comparison of input
variables with decision variables and output decision information
corresponding to the matching or closest matching decision data
object or objects.
[0045] Preferably, the set of decision information is structured as
a plurality of data objects.
[0046] The decision information may be of any length and may be
stored in a hierarchy, whereby key points may be linked to further
detail, and the further detail can be selectively outputted or
selectively extracted from the output for display.
[0047] Preferably, the decision information may include a
combination of media, including text, tables, graphics, sound.
[0048] Preferably, the knowledge base may contain distinct decision
data objects having similar variable values or overlapping variable
ranges, wherein each set of decision information corresponding to
each decision data object having similar variable values or
overlapping variable ranges, embodies an outcome of a decision that
may or may not overlap with one another, but do not conflict with
each other.
[0049] Preferably, if a plurality of equally matching decision data
objects are computed, the method further includes the step of
requesting the input of at least one more variable and computing
which decision data object or objects best matches the input data
object including the at least one more variable.
[0050] Preferably each decision data object may belong to a class,
wherein each class is determined by the type or value of variables
required to identify a match with the decision data objects and
wherein the knowledge base is structured hierarchically according
to class.
[0051] The exact nature of the software solution which can be
conceived is not predetermined and may be implemented in a number
of different methodologies.
[0052] The system may collect the input data object from a separate
database or via an interface at the time of use.
[0053] The knowledge base may be implemented using a variety of
software, which might include object, object-relational and
relational bases having an appropriate structure.
[0054] In a preferred embodiment, the decision making scenario
corresponds to a particular medical diagnosis or condition, or
equally to a combination of medical diagnoses or conditions.
[0055] In a preferred embodiment, the system includes: [0056] a
data input means which presents an input form to collect the value
of input variables, wherein the processing means extracts the
variable values from the input form to create the input data object
or objects; [0057] a search engine or engines which pass the input
data object or objects to the knowledge base and return a search
output indicative of the matching or closest matching decision data
object or objects; and [0058] an output form through which at least
the decision information is communicated.
[0059] Preferably the search engine operates by means of
multivariable pattern matching, or filtering.
[0060] The input form, knowledge base and output form may be
combined or in separate applications.
[0061] The input form, the knowledge base and output form may be
located at the same spatial location or physically remote from each
other, in which case they are connected by a network.
[0062] Preferably, the system architecture includes coding or
structure which allows the output to be transmitted to or
interrogated by one or more third party applications.
[0063] Preferably the system architecture includes an editorial
tool adapted to allow the input, management, update and
customisation of the knowledge base.
[0064] Preferably the editorial tool may be designed so as to be
useable at least by individuals familiar with the area but
unskilled in the translation of scenarios into logic
statements.
[0065] Preferably the editorial tool organises information in the
knowledge base so that the decision data object fits the object
structure which is defined for each input form.
[0066] The system may be programmed in such a way that the
knowledge base is editable without necessarily reprogramming or
recompiling any other elements of the system.
[0067] In a further aspect the invention provides a method of
creating a decision making system in a computational system, the
method including the steps of: [0068] identifying a plurality of
discrete decision making scenarios; [0069] identifying a plurality
of decision variables which are identified as relevant in affecting
the outcome of a decision making process in relation to each
decision making scenario; [0070] collecting the decision variables
into one or more computer readable logically grouped decision data
objects, said one or more decision data objects structured and
handled according to object oriented or object relational
methodologies; [0071] creating a knowledge base, said knowledge
base containing the decision data objects and a set of
corresponding decision information embodying the outcome of the
decision making process; [0072] providing a data input means
suitable for receiving data indicative of the value of a plurality
of input variables; [0073] providing a computer readable
instruction set suitable for causing a computer processing means to
structure the input variables as a grouped input data object,
compute a matching or closest matching decision data object to the
input data object and output the corresponding decision information
to the matching or closest matching decision data object or
objects.
[0074] Preferably, the method includes structuring and handling the
decision data objects in a computational environment according to
object oriented or object relational methodologies.
[0075] Further aspects of the present invention may become apparent
from the following description, given by way of example and in
reference to the accompanying drawings.
BRIEF DESCRIPTION OF THE FIGURES
[0076] FIG. 1: illustrates a representation of the editorial
function of the system--creation of the knowledge base;
[0077] FIG. 2: illustrates schematically the passing of an input
data object to the knowledge base whereupon data output in the form
of management forms is produced;
[0078] FIG. 3: illustrates a simplified schematic of the knowledge
base operating on a network;
[0079] FIG. 4: illustrates schematically the decision process
according to the system; and
[0080] FIG. 5: illustrates a schematic layout of a practical
implementation of the present system.
DETAILED DESCRIPTION
[0081] The present invention is particularly suitable in contexts
where decision making is critical and the knowledge on which the
decisions are to be made is extensive, dynamic, distributed or
dispersed and difficult to retain.
[0082] Clinical medicine is a prime example of such a situation.
Thus, although it is envisaged that the present decision making
system may find application in a large number of situations, the
following description will focus primarily on clinical medicine.
This application illustrates well the operation and applicability
of the present invention to this and analogous applications.
[0083] Health care implementation is increasingly being driven by
the principles of evidence-based-practice. That is--the idea that
clinical management decisions should be based on what is known as
"best practice" where medical interventions have shown to produce a
demonstrable positive impact on the outcomes which are to be
achieved.
[0084] Considerable efforts are being made to gather and evaluate
such evidence which, to be effective, must be available at a time
and in a form where it can promote a change in behaviour in respect
of the practitioners using this information. This is not readily
achievable with the currently available information/knowledge
systems.
[0085] Characteristics of the present invention include the
capability for delivering patient specific clinical management
prompts (i.e. "decision support") at the time of decision making.
This technique is recognised to be the most effective method for
changing behaviour. Further, the present invention provides a way
to associate the patient management system and clinical decision
knowledge bases. This facilitates professional management,
maintenance and updating of other clinical support information.
Further, the structure of the decision making system according to
the preferred embodiment of the present invention promotes
functions such as reporting, feedback and monitoring capabilities
outcomes and performance.
[0086] In terms of implementing the present invention, recent (and
projected) increases in available bandwidth along with the
associated supporting technology means that it is now feasible to
build the present knowledge base system in such a way that would
allow real time transactions at the required volumes. Further, the
knowledge bases and transaction servers can be, though not
necessarily, remote from the providers machines (i.e. the user
interfaces) resulting in an increase in efficiency in the
management of the knowledge base data. In the view of the
applicant, open systems are important to these developments and it
is envisaged that this will be the communication model which is
most suitable for the implementation of the present invention.
[0087] The first step in implementing the present decision making
system is the creation of a suitable knowledge base. This process
is illustrated in FIG. 1, whereby a knowledge base 2 is created
using evidence-based guidelines for the management of a specific
disease or condition. This information or data would normally be
articulated as a large body of (usually printed) information
relevant to a decision associated with a particular scenario,
protocols for handling the scenario, and the like, generally
referenced 1. Relevant information to a decision making process is
identified and used to define a range of discrete clinical
scenarios.
[0088] Information relevant to making decisions for the management
of a scenario, is defined as a set of variables and entered into
the knowledge base 2. Decision information embodying the outcome of
one or more decisions that need to be made in the scenario in
question, is also entered into the knowledge base 2 through an
input template 3. The appropriate decision information may vary
depending on the values of the variables and therefore, the
decision information is structured so that only the appropriate
information can be identified in the occurrence of a particular
pattern of variables.
[0089] A particular scenario for example, may be the evaluation of
a patient with a specific set of medical conditions, displaying a
number of symptoms. Thus, variables may include, among other
things: whether or not a patient is an asthmatic; whether they are
suffering from a cough; whether they have coughed up a particular
coloured phlegm; and the duration of the condition. Faced with this
scenario, the outcome of the decision as to an appropriate
treatment may depend on any or all of these variables. The
variables may be binomial in character, for example, the patient
either is or is not an asthmatic, or allow a range of values, for
example the duration of the cough.
[0090] The variables required for identifying the appropriate
decision information are distilled from protocols and like and
edited into the knowledge base according to a predefined hierarchy.
For example, the variable of the colour of the phlegm may be
located in the next lower level below the variable of whether or
not the patient has a cough. Thus, a series of input screens may be
displayed or queries for input information sent in order to obtain
values for variables at all required levels of the hierarchy.
Support information, which may be required to assist in
identification of the variables is also entered into the knowledge
base where required for use in guiding a user to the correct
identification of the required variables. For example, a test may
need to be performed before a variable can be identified and the
support information may include instructions on how or where to
obtain the results of the test.
[0091] The decision information may also be arranged in a
hierarchy. For example, an important aspect of the decision
information may have a link to a more detailed description of that
aspect, which in turn may have more links to related subjects.
Using the system in this way, a user may learn about the same and
related scenarios, expanding their knowledge.
[0092] Referring now to FIG. 4, in effect, the inputting of a set
of variables associated with each scenario forms a template,
hereinafter decision template 9, which is stored in the knowledge
base 2. The decision template 9 incorporates the variables and
information relating to the variable values and/or range of
variable values which can be identified with a particular clinical
scenario. Thus, each variable is defined by its type, which
identifies the parameter to be measured or described, and by its
value, which quantifies the existence of a particular fact or
quantifies the variable when the variable could have three or more
values. A user inputs the values of variables to form an input
template 10. The structure of the input template 10 is
complementary to the decision template 9 to allow comparison of the
values of the variables in each template.
[0093] The decision templates 9 may thus also be arranged in a
hierarchy, determined by the type of each template 9. Templates 9
belonging to a particular class may be grouped according to that
class and the user may select the class and therefore the templates
9 to which their variable information is to be compared. The input
template 10 may vary in form depending on the type of decision
template 9 that is to be searched for a match or closest match.
[0094] Thus, both the input data object and decision data object
are logically identifiable, grouped portions of data. The decision
data objects may each be defined in the knowledge base as a
distinct object, in which case it is directly amenable to treatment
according to object oriented methodologies, which focus on groups
of related data or processes. The decision information embodying
the outcome of a decision should an input data object match a
specific decision data object is stored in relation to the decision
data object, preferably as a distinct data object itself. In an
alternative embodiment, the decision data objects may be stored in
tables in a relational database, with the variables listed as
specific entries in the table and the decision information
corresponding to each decision data object stored in a related
table. In this case, each decision data object is defined by a
number of entries in the table and these entries are treated as a
grouped distinct data object in accordance with object-oriented or
object-relational methodologies.
[0095] The outcome of the decision making process, embodied in the
decision information may include, for example a list of a course of
actions, recommendations, or comments appropriate to the particular
scenario. This information is inputted together with the decision
information as a separate data object linked to at least one
decision template. The data objects containing decision information
are stored in a many-to-many relationship with the decision
templates 9.
[0096] To query the decision making system, what is known as the
"clinical support system" is used. This is illustrated in FIG. 2.
This component includes the end-user input interface 4 of the
system and entails inputting patient specific features in the form
of an electronic record. This information is entered by means of a
predetermined input form or input template 10, which requests
information based on the variables which are required to identify a
set of decision information for extraction from the knowledge base
to the data output 5.
[0097] A practitioner enters the variable data, ranges of variable
data, flags or other information. Alternatively, this information
is populated from another information source or base. Once this
template is completed, pattern matching, variable matching or
similar is used to identify previously compiled or entered decision
information which is then output, for example as a set of "patient
management prompts". An appropriate search engine may be used to
search the decision information and associated variables for a
match. It is not obligatory that the template physically exists nor
that it is visually presented to the system user. The required
variables could be collected in a virtual manner and passed unseen
to the knowledge base.
[0098] The function of the search engine can be broadly described
as attempting to match a patient's particular clinical scenario
with a previously existing scenario or scenarios which are stored
in the knowledge base. At a broad level, this can be thought of as
a search for an abstract volume of information embodying a
previously determined clinical condition matching that of the
presently unknown clinical condition. The output data is in the
form of patient specific management prompts which embody the
clinical determinations or decisions which are required by the
practitioner.
[0099] In many cases, the best matching decision data object may
not be sufficient, or may provide incorrect information. This is
especially apparent in a medical diagnostic system. Therefore, the
decision making process or system may be limited to only output
decision information if the inputted variables match exactly with a
decision data object or fall within a range defined by a decision
data object. Alternatively, individual critical decision data
objects may require such an exact match, whereas less critical
decision data objects may allow the decision information to be
displayed if an exact match is not obtained, with a warning of a
lack of an exact match and identification of the variables that do
not match.
[0100] The decision making system support architecture can be in
the form of a distributed system whereby patient management systems
are linked via secure networks to an application server which
delivers the clinical support information. As noted above, the
connection is preferably implemented using an open systems
architecture such as TCP/IP or the like. This is illustrated in
FIG. 3, where a number of general practitioners, collectively
referenced by box 6 and a hospital 7 communicate using a
communications network with the knowledge base 2. The
communications network may be an intranet or use a wide area
network such as the Internet. However, to prevent misuse of
information from the knowledge base and preserve privacy of
information, the communication channel should be secure.
[0101] Referring to FIG. 5, the decision making system, referenced
by Box A may interface to an existing patient management system B.
The existing system B includes information relating to the patient
including patient data 11, a separate patient management
functionality 12 and an input 13 to allow entry of patient and
patient disease and disorder data. The function of providing an
input template 14 may be provided by the patient management system
B, allowing individual end users to custom design their own
templates. This results in the formation of a master input template
10 in a form readable by the decision system of the present
invention. The patient management system receives the decision
information 16 and displays the recommendations 17 through an
appropriate display device and stores the recommendations 17 to
supplement the patient data 11.
[0102] The decision making system receives the master input
template 10 through an interface 18, matches it with a decision
data object 9 in step 15, extracts the corresponding decision
information 19 and sends this information to the patient management
system in step 20. Optionally, a decision is made whether to
forward the decision information 19 depending on the closeness of
the match in step 21.
[0103] The present invention may include the development of an
editorial tool which conforms to the system architecture. It is
envisaged that this tool will allow the reuse of object components
already in existence for related projects, such as a drug object
for the management of one condition to be applied to an unrelated
condition where the same drug is required. It is further envisaged
that a wide-range of standard input forms will be developed in
order to handle pre-defined scenarios. A consequence of the
approach to the present system is that the input forms, knowledge
bases and output forms could be created by different organisations
following the particular architecture standard. The application
modules could thus be implemented so as to be readily interact with
the knowledge base and its components. Further, as noted above, the
present invention has been described in a particular
application--that of clinical medicine. However, the principles of
the invention are equally applicable to any decision making
environment where knowledge exists to provide a defined set of
information in discreet decision making scenarios or situations.
Further, examples may be the law, engineering, manufacturing or the
like.
[0104] The same principles may also be applied in more uncertain
situations to define a response should a set of variables ever
occur in a certain pattern, even if this has never been previously
experienced. This has relevance, amongst other areas, to the field
of science and engineering in the iterative analysis of
multivariate problems.
[0105] A particular advantage of the present system includes the
creation of an efficient and effective decision making system which
provides scenario specific advice based on previously read rules,
guidelines and protocols. The information in the knowledge base is
available at the time of the decision making and may be adapted to
deliver common messages following standard formats, content and
instructions. Due to the relative simplicity and association of the
various components, it is envisaged that the present decision
making system may be integrated with existing systems depending on
those systems architecture.
[0106] Another advantage of the present system includes the speed
with which multiple output objects can be matched to the input
object. The matching process avoids complex and time consuming
looped searches, resulting in enhanced functionality.
[0107] A further advantage of the system is that it lends itself to
the handling of decision making on the face of multi-faceted
scenarios, which currently available systems struggle to address,
but pattern matching provides a more effective solution.
[0108] A further advantage of the present invention is that its
architecture allows the knowledge base to be modified without the
other elements of the system needing to be reprogrammed or
recompiled in any way. Further, a consequence of having a centrally
accessible knowledge base dispenses with the need of distributing
up-to-date data to the users in piecemeal form and at sporadic
intervals. This also removes the obvious problems associated with
those techniques whereby revision tracking and the necessity to
keep the information up to date is paramount.
[0109] Thus the present invention provides a novel decision making
system which is flexible and based primarily on "best practice",
rules, knowledge and experience. Of course the system could be
implemented using any suitable programming environment. No specific
details will be given as it is considered that any specific
implementation will be within the ambit of those who are skilled in
the art.
[0110] Where in the foregoing description, reference has been made
to specific components or integers of the invention having known
equivalents then such equivalents are herein incorporated as if
individually set forth.
[0111] Although this invention has been described by way of example
and with reference to possible embodiments thereof, it is to be
understood that modifications or improvements may be made thereto
without departing from the scope of the appended claims.
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