U.S. patent application number 10/533872 was filed with the patent office on 2008-01-03 for intelligent data management system and method.
Invention is credited to Joseph C. Kurian, William Melek, Homayoun Najjaran, Hue Tran, Afshan Zahedi, Saeed Ziaee.
Application Number | 20080005054 10/533872 |
Document ID | / |
Family ID | 32304003 |
Filed Date | 2008-01-03 |
United States Patent
Application |
20080005054 |
Kind Code |
A1 |
Kurian; Joseph C. ; et
al. |
January 3, 2008 |
Intelligent Data Management System and Method
Abstract
An intelligent data management system and method are disclosed.
The system includes a database of stored data, a middleware layer
having access to the stored data, and at least one client device
for remotely accessing a provided course of action. The middleware
layer includes a fuzzy logic knowledge base for generating,
updating, or firing fuzzy logic rules and a fuzzy logic inference
engine for processing the stored data guided by the fuzzy logic
rules to provide the course of action.
Inventors: |
Kurian; Joseph C.; (North
York, CA) ; Tran; Hue; (North York, CA) ;
Melek; William; (Toronto, CA) ; Najjaran;
Homayoun; (Kelowna, CA) ; Zahedi; Afshan;
(Richmond Hill, CA) ; Ziaee; Saeed; (Richmond
Hill, CA) |
Correspondence
Address: |
PEARNE & GORDON LLP
1801 EAST 9TH STREET, SUITE 1200
CLEVELAND
OH
44114-3108
US
|
Family ID: |
32304003 |
Appl. No.: |
10/533872 |
Filed: |
November 3, 2003 |
PCT Filed: |
November 3, 2003 |
PCT NO: |
PCT/CA03/01686 |
371 Date: |
February 9, 2006 |
Current U.S.
Class: |
706/52 ;
718/105 |
Current CPC
Class: |
G16H 10/60 20180101;
G06N 5/048 20130101; G16H 50/20 20180101 |
Class at
Publication: |
706/52 ;
718/105 |
International
Class: |
G06N 7/02 20060101
G06N007/02; G06F 9/46 20060101 G06F009/46 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 5, 2002 |
CA |
2411203 |
Claims
1. An intelligent data management system comprising: a database of
stored data; a middleware layer having access to the stored data,
the middleware layer including: a fuzzy logic knowledge base for
generating, updating, or firing fuzzy logic rules; and a fuzzy
logic inference engine for processing the stored data guided by the
fuzzy logic rules to provide a course of action; and at least one
client device for remotely accessing the provided course of
action.
2. The system according to claim 1, wherein at least one of the
client devices is a wireless device.
3. The system according to claim 2, further including a gateway to
facilitate wireless access to the middleware layer from a wireless
client device leveraging existing wireless networks.
4. The system according to claim 1, further including a load
balancer for balancing loads between client devices and the
middleware layer.
5. The system according to claim 1, further including an integrated
billing component.
6. The system according to claim 2, further including an integrated
billing component.
7. The system according to claim 3, further including an integrated
billing component.
8. The system according to claim 4, further including an integrated
billing component.
9. An intelligent data management method comprising the steps of:
(i) accessing stored data; (ii) providing a course of action using
the accessed data by: a) generating, updating, or firing fuzzy
logic rules; and b) processing the stored data using fuzzy logic
inference guided by the fuzzy logic rules; and (iii) remotely
accessing the provided course of action.
10. The method according to claim 9, further including the step of
wirelessly connecting at least one client device.
11. The method according to claim 10, further including the step of
leveraging existing wireless networks to provide a gateway that
facilitates wireless access to the middleware layer from a client
device.
12. The method according to claim 9, further including the step of
balancing loads between client devices and the middleware layer
using a load balancer.
13. The method according to claim 9, further including the step of
integrating a billing component.
14. An intelligent data management system comprising: a module for
accessing stored data; a module for providing a course of action
using the accessed data including: a module for generating,
updating, or firing fuzzy logic rules; and a module for processing
the stored data using fuzzy logic inference guided by the fuzzy
logic rules; and a module for remotely accessing the provided
course of action.
15. A computer program product for implementing an intelligent data
management method, the computer program product comprising: a
computer readable medium for storing machine-executable
instructions for use in the execution in a computer of the method,
the method including the steps of: accessing stored data; providing
a course of action using the accessed data by: generating,
updating, or firing fuzzy logic rules; and processing the stored
data using fuzzy logic inference guided by the fuzzy logic rules;
and remotely accessing the provided course of action.
Description
FIELD OF THE INVENTION
[0001] The present invention relates generally to electronic
databases, and more particularly to a data management system and
method.
BACKGROUND OF THE INVENTION
[0002] Today, faced with an increasingly mobile population, global
accessibility to healthcare information is becoming increasingly
essential. Unfortunately, early electronic approaches to automated
medical record systems tended to apply industrial engineering
concepts to understanding and automating the flow of healthcare
data, with the expectant failures.
[0003] It has been envisioned that the future patient record will
be that of a multimedia record capable of including text,
high-resolution images, sound, and full-motion video. These
systems, which have come to be known as Computer-Based Patient
Record (CPR) systems, will ultimately be expected to offer improved
access, quality, security, flexibility, connectivity, and
efficiency.
[0004] CPR systems are used to maintain patient records such as
histories, reports, charts, and images in digitized form within the
networked system of one or more health care institutions. This
enables authorized users to access patient records remotely
employing client devices such as desktop computers, laptops,
personal digital assistants (PDA's) and the like, coupled to a
networked system via wired and/or wireless network paths.
[0005] Today, knowledge bases provide machine-readable resources
for the dissemination of information, generally online or with the
capacity to be put online. An integral component of knowledge
management systems, a knowledge base is used to optimize
information collection, organization, and retrieval for an
organization, or for the public at large. A well-organized
knowledge base can save an enterprise a considerable amount of
money by decreasing the amount of employee time spent trying to
find information about such topics as tax laws, or company policies
and procedures. A knowledge base can give users easy access to
information that would otherwise require laborious contact with
many people.
[0006] In general, a knowledge base is not a static collection of
information, but a dynamic resource that can itself have the
capacity to "learn", as part of an artificial intelligence (AI)
expert system for example. An expert system is a computer
application that performs a task that would otherwise be performed
by a human expert. For example, there are expert systems that can
make financial forecasts or schedule routes for delivery vehicles.
Some expert systems are designed to take the place of human
experts, while others are designed to aid them. To design an expert
system, a knowledge engineer studies how human experts in a
particular field make decisions. They then create rules that are
subsequently translated into terms a computer can understand.
[0007] However, existing knowledge bases are so inherently tied to
their inference engines that they lack flexibility. Typically,
within a knowledge base, the tool of choice has been the "IF_THEN"
conditional statement. A basic IF-THEN statement is used when the
choice is whether to take an action or not; there is no alternative
action. The condition in an IF-THEN statement is considered true if
its value is non-zero, and false if its value is zero. The IF-THEN
statement provides direction only when a parameter is found to be
true. The problem is that IF-THEN statements are too rigid. What is
needed is a data management system that can better emulate the
superior reasoning processes of a human to facilitate diagnosis and
treatment in an efficient and effective manner.
[0008] For the foregoing reasons, there is a need for an improved
system and method for the management of medical and other
records.
SUMMARY OF THE INVENTION
[0009] The present invention is directed to an intelligent data
management system and method. The system includes a database of
stored data, a middleware layer having access to the stored data,
and at least one client device for remotely accessing a provided
course of action. The middleware layer includes a fuzzy logic
knowledge base for generating, updating, or firing fuzzy logic
rules and a fuzzy logic inference engine for processing the stored
data guided by the fuzzy logic rules to provide the course of
action.
[0010] In an aspect of the present invention, the system further
includes a gateway to facilitate wireless access to the middleware
layer from a client device leveraging existing wireless networks.
In an aspect of the present invention, the system further includes
a load balancer for balancing loads between the client device and
the middleware layer.
[0011] The method includes the steps of accessing stored data,
providing a course of action using the accessed data, and remotely
accessing the provided course of action. The step of providing a
course of action further includes the steps of generating,
updating, or firing fuzzy logic rules and processing the stored
data using fuzzy logic inference guided by the fuzzy logic
rules.
[0012] By generating fuzzy rules for capturing expert knowledge,
rules can be later updated based on feedback from the system, and
without having to change the inference engine programming code. By
using hand-held client devices, the system provides mobile data
management for medical care units, and enables efficiencies in
patient treatment, cost efficiencies, paper-work reduction,
resource allocation and utilization management, error minimization,
and clinical research and data mining capabilities.
[0013] By providing an intelligent system capable of assisting in
accurate diagnosis and treatment, the system provides timely
information through the use of a wireless hand-held client device
to cost-effectively deliver global accessibility to patient
records. The system further provides near real-time information to
aid in the making of clinical decisions, and streamlines the
clinical process to improve decision-making quality to facilitate
medical procedures, speed up processing times, and eliminate
paper-related errors.
[0014] Other aspects and features of the present invention will
become apparent to those ordinarily skilled in the art upon review
of the following description of specific embodiments of the
invention in conjunction with the accompanying figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] These and other features, aspects, and advantages of the
present invention will become better understood with regard to the
following description, appended claims, and accompanying drawings
where:
[0016] FIG. 1 is an overview of an intelligent data management
system in accordance with an embodiment of the present
invention;
[0017] FIG. 2 is an overview of an intelligent data management
method in accordance with an embodiment of the present
invention;
[0018] FIG. 3 illustrates an overview of a wireless architecture in
accordance with an embodiment of the present invention;
[0019] FIG. 4 illustrates a fuzzy inference engine structure
flowchart; and
[0020] FIG. 5 illustrates a fuzzy knowledge base structure
flowchart.
DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENT
[0021] The present invention is directed to an intelligent data
management system and method. As illustrated in FIG. 1, the system
10 includes a database 12 of stored data 14, a middleware layer 16
having access to the stored data 14, and at least one client device
18 for remotely accessing a provided course of action 20. The
middleware layer 16 includes a fuzzy logic knowledge base 22 for
generating, updating, or firing fuzzy logic rules 24 and a fuzzy
logic inference engine 26 for processing the stored data 14 guided
by the fuzzy logic rules 24 to provide the course of action 20.
[0022] In an embodiment of the present invention, as illustrated in
FIG. 3, the system 10 further includes a gateway 28 to facilitate
wireless access to the middleware layer 16 from a client device 18
leveraging existing wireless networks. In an embodiment of the
present invention, the system 10 further includes a load balancer
30 for balancing loads between the client device 18 and the
middleware layer 16.
[0023] As illustrated in FIG. 2, the method 100 includes the steps
of accessing stored data 102, providing a course of action using
the accessed data 104, and remotely accessing the provided course
of action 106. The step of providing a course of action 104 further
includes the steps of generating, updating, or firing fuzzy logic
rules 108 and processing the stored data using fuzzy logic
inference guided by the fuzzy logic rules 110.
[0024] The following described embodiments are directed to medical
record management embodiments, provided as exemplary examples of
the present invention. Although what is described herein is
directed to medical record management embodiments, it should be
noted that other embodiments are contemplated and envisioned such
as, but not limited to, material handling systems, transportation
systems, and governmental services.
[0025] The system 10 is provided in a flexible object model to
provide analysis and design of patient charts, as well as billing
and scheduling capabilities. The database 12 is a patient-based
design with data 14 categorized and stored based on its importance
in diagnosis. An object-oriented analysis provides an effective
approach for communicating with the application and domain
expert.
[0026] As illustrated in FIG. 4, the fuzzy logic inference engine
26 provides the decision-making capacity. The fuzzy logic inference
engine 26 has been designed and implemented for medical diagnosis
purposes. However, since the engine is modular, it can be
restructured for other uses. As illustrated in FIG. 5, the system
10 leverages expert knowledge to create a knowledge base 22 and
fuzzy logic rules 24 to capture this knowledge and be available for
process by the independent fuzzy inference engine 26. By creating a
modular inference engine 26 that is separate from the knowledge
base 22, updating the diagnosis and treatment rules 24 is made
easier.
[0027] The modular inference engine 26 is parameterized with
various indices that help cover a wide variety of inference
mechanisms between the Mamdani and Formal Logical extremes. A
precise diagnosis is computed through a parameterized
defuzzification method with parametric norms used for rule firing
using information obtained from the fuzzy rules 14. The
parameterized inference engine 26 provides the flexibility to adapt
to one of many possible methods of reasoning, some of which can
perform better than others in the diagnosis of a specific disease
within a medical discipline.
[0028] The inference engine 26, as a class entitled
`InferenceEngine`, has been designed to include the database 12 and
procedures required for diagnosis, cause and treatment of medical
conditions. The class retrieves patient information from the
database 12 through `GetFactor` functions, and launches a fuzzy
logic procedure for diagnosis. The system 10 provides guidance for
experts on how to structure their knowledge for rule generation. As
illustrated in Table 1, the fuzzy logic inference engine is modular
with multiple operators.
TABLE-US-00001 TABLE 1 Fuzzy Logic Inference Engine Operators Fuzzy
numbers: Creat Create a fuzzy number in two steps GetFiredHeight
Find the height of a fuzzy number to the effect of an event GetMin
Find the minimum range of a fuzzy number GetMax Find the maximum
range of a fuzzy number GetArea Find the area of a fuzzy number
Rule Base: SNorm Obtain the summation of all fired fuzzy numbers
TNorm Find the interaction of the fuzzy numbers Defuzzify A generic
defuzzification method that uses a parameter to determine the
defuzzification method used DoProcess Perform the diagnosis process
Inference Perform the fuzzy inference GetDrawPoint Only for
displaying the result
[0029] Knowledge structuring can include a list of diagnosis and/or
lab tests, family histories, medical examinations, as well as
medical procedures related to diagnosis. In addition, knowledge
structuring can further include defining relationships, logical
expression of relationships, choosing keywords for processing,
prioritizing of all related parameters to diagnosis, sensitivity
analyzing, and classifying parameters.
[0030] For handling numeric and linguistic inputs, a rule base is
constructed to provide a suitable course of action 20 through
"defuzzification", with a non-zero output membership area. An
object-oriented approach has been used in designing the database 12
structure. The database 12 gathers patient related data 14 and
stores it in several main categories such as Personal Information,
Lab Test, Diagnostic Imaging, Report, Medical History Physical
Examination, Surgical Pathology and Diagnosis categories.
[0031] In an embodiment of the present invention, data 14 from a
patient's medical records can be utilized by a billing component to
better automate the system 10, and to provide cost savings through
the elimination of data entry overlap. This provides a flexible
billing system that meets the diverse requirements of a variety of
providers, and can further include a laboratory central pool, a
pharmacy central pool, and/or a material central pool.
[0032] In an embodiment of the present invention, the system 10 is
implemented at least in part as a wireless solution. While the
system 10 can be run from standalone computer platforms, the system
10 is advantageously utilized within a wireless network environment
to take advantage of the power and flexibility inherent within the
system 10. Medical personnel can then carry hand-held devices that
provide them with real time access to a patient's medical records
instead of the limited and cumbersome traditional methods of
carrying paper folders, clipboards, and the like.
[0033] FIG. 3 illustrates a wireless architecture overview in
accordance with an embodiment of the present invention. The system
10 provides a flexible architecture designed to support wireless
handheld client devices 18. All information relating to patients
can be stored on a server with a handheld device 18 used to
retrieve the information from the server leveraging the wireless
infrastructure. Multiple wireless access points can be provided to
ensure connectivity throughout an entire medical unit. All
diagnostic images, prescriptions, laboratory results and treatment
information are then stored on a server and can be retrieved
through the handheld client device 18.
[0034] In general, the architecture is divided into 3 layers, the
client device 18 layer, the middleware layer 16, and the database
12 layer. The client device 18 layer can be an application client,
web client, or wireless handheld client device. The middleware
layer 16 contains all business rules, business objects and
entities, and any supporting services such as security, reports,
and queries. Thirdly, the database 12 layer is where all data 14 is
stored, and normally where the database 12 resides. With this
flexible architecture, the system 10 can support wireless
applications with less effort because all rules, entities, and
services reside in the middleware layer 16 and not within the
database 12. Therefore, developers need only concentrate on
developing communications between the wireless handheld client
device 18 and the middleware layer 16, presenting information to
the handheld client devices 18, and supporting databases in the
handheld client devices 18.
[0035] The system's architecture includes a wireless client device
18, such as a smart phone, handheld devices, pocket PC, and the
like, which communicates through a wireless network such as CDMA
(Code Division Multiple Access), CDPD (Cellular Digital Packet
Data) or GSM (Global System for Mobile communication) to a gateway
28. The gateway 28 then communicates with the middleware layer 16
through TCP/IP (Transmission Control Protocol/Internet Protocol).
The middleware layer 16 then manipulates the data 14 in the data 14
layer and communicates with any legacy systems using protocols such
as JDBC (Java Data Base Connectivity), JMS (Java Messaging
Services), XML (extensible Markup Language), and HTTP (Hyper Text
Transfer Protocol). The system's wireless architecture is capable
of handling a variety of wireless communications such as batch
transfers from server to hand-held device, real-time transfers from
server to hand-held device, and real-time transfers between
hand-held devices.
[0036] Because the system 10 uses a standard XML (Extensive Markup
Language) or other similarly flexible markup language to exchange
and structure information, it can support both wired clients and
wireless clients. By using XML language, the system 10 can use XSL
(Extensible Stylesheet Language) to transform the data 14 into
different formats or different presentations. For example, if a web
client makes a request using the HTTP protocol, then the system 10
transforms the XML into HTML (Hyper Text Markup Language) using XSL
before responding to the client 18. However, if a wireless handheld
client device 18 makes a request using WAP (Wireless Application
Protocol), then the system 10 transforms the XML into WML (Wireless
Markup Language) using XSL before passing the response back to the
client 18. Such a flexible architecture is needed in order to
handle different types of wireless handheld client devices 18 using
different markup languages for content delivery. The application
server 161 can readily be used as a wireless application server. In
addition, the system 10 can support Java clients, web clients,
and/or wireless clients.
[0037] The system 10 architecture utilizes J2EE (Java 2 Enterprise
Edition) technology, a widely adopted technology for building
enterprise applications. Since the system 10 is based on a flexible
architecture technology, it can be deployed on virtually any
platform. J2EE technology includes elements such as EJB (Enterprise
Java Bean(s)), JSP (Java Server Pages)/Servlet, JDBC, JMS, Java
mail, and JNDI (Java Naming and Directory Interface). The
application server 161 can be configured for use as a wireless
application server 161. As discussed previously, XML is used for
information exchange and for structuring information. As for the
handheld client device 18, the system 10 can support java clients,
web clients, and/or wireless clients.
[0038] As well, the system 10 can support wireless applications
including wireless web, lightweight database, and thin
client/server applications. The wireless web technology is provided
as a browser technology. For this type of wireless application, the
system 10 supports WML/WAP. The system 10 architecture makes it
flexible enough to support all major mark-up languages for wireless
devices.
[0039] In this architecture, the client device 18 is used only for
presenting information. All business logic resides in the
middleware layer 16. As illustrated in FIG. 3, the middleware layer
16 includes the application server 161, business process 162,
business entities 163, application services 164, business
intelligence module 165, and request processor 166. The application
server 161 manages transactions, resources, and persistent data 14.
The business process 162 is where all business rules and logics
reside. The purpose of the business process 162 is to process the
information within the business objects, which encapsulates the
business information. The advantage of separating business
processes from business objects is that business objects are then
reusable entities that can be reused within other applications.
This provides great advantages since business objects are seldom
changed, while on the other hand business rules and processes are
constantly changing.
[0040] The application services 164 are services that support the
business such as security services, query services, report
services, and messaging services. The business intelligence module
165 is an artificial intelligence module that assists a physician
or hospital in providing patient healthcare. The request processor
module 166 processes requests from the client devices 18 and
presents information to the client device 18. The data layer 12
includes the databases 12 where all data 14 is stored, and can
further include legacy systems 121.
[0041] Wireless devices can be supported with modules such as
"scheduler", "view/transfer patient chart", and "order entry"
modules. Schedulers are generic and flexible, and can automatically
make appointments based on available resources and the needs of a
specific situation. Appointment parameters include date, time,
people to meet, and/or equipment required and procedures to be
performed, as well as the space required for these procedures. In
order for appointments to take place in an orderly fashion, all
parameters need to be scheduled to come together at a pre-selected
date and timeslot for a particular appointment.
[0042] In addition, a scheduler is capable of re-adjustment if and
when an appointment has to be changed. The purpose of the scheduler
is to make the most efficient use of the aforementioned parameters
for any appointments for which they are required. To ensure that
the scheduler is generic and flexible, it has been designed to
treat all appointment parameters as resources so that the scheduler
can be used in other applications. For medical applications,
resources include patients, providers, equipment, and
locations.
[0043] The scheduler, through the use of search strategies, has the
capacity to maximize the number of medical procedures performed in
medical institutions such as emergency room admission, MRI imaging,
X-ray, open-heart surgery, CT scans, and consultation for given a
set of medical resources.
[0044] The Scheduler is also capable of maximizing the number of
procedures that can be completed within a constraint time horizon
through the use of active schedule generation to analyze the
unavailability patterns of human and equipment resources, as well
as medical personnel work hours.
[0045] In addition, the scheduler enables the efficient scheduling
of available professional resources like nurses and physicians, as
well as other resources like beds and emergency rooms for a given
number of medical procedures that need to be completed over a fixed
time span. Since this process is currently being performed
manually, automating this process involves the generation of
schedules that abide by the time constraints and unavailability
patterns of existing hospital resources, as well as the level of
dependency of a given procedure on other procedures that need to be
completed concurrently or sequentially.
[0046] While this process may be constrained for a given time
horizon, set of procedures, or resources available at a given
medical institution, over a sufficient learning period of time, the
scheduler can optimize the above criteria to re-schedule patients
using the system's 10 incorporated AI (Artificial Intelligence)
technology.
[0047] An AI engine has been incorporated into the scheduler that
learns how a particular provider utilizes appointment times for
different types of appointments, so that the scheduler is able to
suggest an appropriate duration for appointments of differing types
and/or providers. The scheduler's `learned intelligence` is capable
of suggesting the earliest available time slot for an appointment
based on the type of appointment and resources required.
[0048] The scheduler's AI engine facilitates the efficient use of
resources for various appointments and medical procedures that are
required without sacrificing the capacity to prioritize
appointments or procedures based on the urgency of a matter. For
example, one procedure might require three resources with a
procedure duration of one hour. However, the third resource may
only be required ten minutes after the start of the procedure, and
for just twenty minutes. The scheduler can then make that resource
available for other procedures where it is required, thereby making
efficient use of that resource.
[0049] The scheduler is user-customizable since it's design is
based on a template and dictionary, or knowledge base. A user can
create different types of appointments with different types of
resources. A user can also pre-assign resources specifically
required for a particular appointment as a default value in a
template, such as when a specific surgeon is required or requested
by a patient. Therefore, complicated appointments such as those
that have "fixed resources" as default values and other resources
as variables ones, which when done in the traditional manner may
take a day or more to schedule, can be made in near real time.
[0050] The scheduler is capable of handling highly sophisticated
and complex appointment schedules. One example of a complex
appointment is that of a main appointment that depends upon several
additional appointments. For example, a surgical appointment might
require that the patient have a physical and X-rays taken before a
surgical procedure can be performed.
[0051] As an example, a View/Transfer patient chart module will
typically include patient information, the patient's history, a
patient problem list, patient treatments, and patient diagnostic
results such as X-Rays, MRI's, and laboratory results. An Order
Entry module will typically include laboratory requisitions,
prescriptions, and diagnostic procedures. Examples of patient
record management tasks include the creation, editing, and updating
of patient records, retrieving pertinent information and
manipulating diagnostic images. Image handling can include
rotation, side-by-side and overlapping comparisons.
[0052] The inclusion of a thin client/server technology enables
seamless communication between the handheld client device 18 and
the middleware layer 16. The handheld client device 18 can then
make use of the services provided by the middleware layer 16.
However, since different handheld client devices 18 have different
platforms, it would be preferable that the client programming be
written using J2ME (Java 2 Micro Edition), a lightweight version of
Java technology, or other similarly lightweight language that
targets small devices to avoid having to write client programs for
every platform. Because J2ME is Java technology, it resolves the
problem of security since the code can be downloaded, is platform
independent, provides full-color graphics, and supports robust
applications. In addition, a user can enjoy full color graphics and
manipulate diagnostic images, such as X-ray images, on the
hand-held device 18.
[0053] The use of lightweight databases on the handheld client
devices 18 is highly desirable within healthcare applications where
there often is a need to work offline, either for cost-saving
reasons or for locations where no network coverage exists. With the
system 10, a user is able to store information locally on a
handheld client device 18 using a JDBC interface. Any changes will
then be synchronized with a master database over a wireless
connection when network access again becomes available. A further
use for this lightweight database is for the preloading of static
information such as drug formulary, making it possible for
physicians to prepare prescriptions on the hand-held client device
18 before sending them to a pharmacy.
[0054] The system 10 provides an artificial intelligence engine 165
with an intelligent graphical web browser interface that
facilitates decision-making in diagnosis and treatment. The
deployment of mobile wireless technology facilitates the use of
hand-held client devices 18 to access patient information from a
central pool so that, by applying the intelligent graphical web
browser interface to a database 12 of electronic medical records,
critical information can be reviewed before any decision-making
takes place. This not only expedites processing time, but also will
ultimately eliminate paperwork and associated errors.
[0055] In addition, the system 10 incorporates elements that ensure
compliance with privacy regulations or confidentiality
requirements. The system 10 provides global accessibility to
patient records through the use of an electronic medical record via
centralized storage of patient records including diagnostic
images.
[0056] By generating fuzzy rules 24 for capturing expert knowledge,
rules 24 can be later updated based on feedback from the system 10,
and without having to change the inference engine 26 programming
code. By using hand-held client devices 18, the system 10 provides
mobile data management for medical care units, and enables
efficiencies in patient treatment, cost efficiencies, paper-work
reduction, resource allocation and utilization management, error
minimization, and clinical research and data mining
capabilities.
[0057] By providing an intelligent system capable of assisting in
accurate diagnosis and treatment, the system 10 provides timely
information through the use of a wireless hand-held client device
18 to cost-effectively deliver global accessibility to patient
records. The system 10 further provides near real-time information
to aid in the making of clinical decisions, and streamlines the
clinical process to improve decision-making quality to facilitate
medical procedures, speed up processing times, and eliminate
paper-related errors.
[0058] Although the present invention has been described in
considerable detail with reference to certain preferred embodiments
thereof, other versions are possible. Therefore, the spirit and
scope of the appended claims should not be limited to the
description of the preferred embodiments contained herein.
* * * * *