U.S. patent application number 11/251580 was filed with the patent office on 2006-05-25 for systems and methods for adaptive medical decision support.
This patent application is currently assigned to Catalis, Inc.. Invention is credited to Michael D. Dahlin, Randolph P. Lipscher, Risto Miikkulainen.
Application Number | 20060112050 11/251580 |
Document ID | / |
Family ID | 24772132 |
Filed Date | 2006-05-25 |
United States Patent
Application |
20060112050 |
Kind Code |
A1 |
Miikkulainen; Risto ; et
al. |
May 25, 2006 |
Systems and methods for adaptive medical decision support
Abstract
A computer-implemented method for adaptively supporting medical
decisions of at least one user includes receiving a first input
from a first device, receiving a second input from a second device,
determining a suggested medical decision based at least in part on
the first input and the second input, and transferring the
suggested medical decision to the second device.
Inventors: |
Miikkulainen; Risto;
(Austin, TX) ; Dahlin; Michael D.; (Austin,
TX) ; Lipscher; Randolph P.; (Austin, TX) |
Correspondence
Address: |
LARSON NEWMAN ABEL POLANSKY & WHITE, LLPL.L.P.
5914 WEST COURTYARD DRIVE
SUITE 200
AUSTIN
TX
78746
US
|
Assignee: |
Catalis, Inc.
Austin
TX
|
Family ID: |
24772132 |
Appl. No.: |
11/251580 |
Filed: |
October 14, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
09690354 |
Oct 17, 2000 |
6988088 |
|
|
11251580 |
Oct 14, 2005 |
|
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Current U.S.
Class: |
706/46 |
Current CPC
Class: |
G16H 50/20 20180101;
G16H 10/60 20180101 |
Class at
Publication: |
706/046 |
International
Class: |
G06N 5/02 20060101
G06N005/02 |
Claims
1. A computer-implemented method for adaptively supporting medical
decisions of at least one user, the method comprising: receiving a
first input from a first device; receiving a second input from a
second device; determining a suggested medical decision based at
least in part on the first input and the second input; and transfer
the suggested medical decision to the second device.
2. The method of claim 1, wherein the first device is an automated
patient monitoring device.
3. The method of claim 1, wherein the first device is a patient
input device.
4. The method of claim 1, wherein the first device is a nurse input
device.
5. The method of claim 1, wherein the second device is a physician
input device.
6. The method of claim 1, further comprising receiving a medical
decision input from the second device based at least in part on the
suggested medical decision.
7. The method of claim 1, wherein the suggested medical decision
includes a diagnosis.
8. The method of claim 1, wherein the suggested medical decision
includes an order.
9. (canceled)
10. The method of claim 1, wherein transferring includes
transferring a user interface including pre-selected interface
controls.
11. The method of claim 1, wherein the second device includes a
wireless user interface device.
12. A computer-implemented method for adaptively supporting medical
decisions of at least one user, the method comprising: receiving a
medical input from a user device; determining a fist suggested
decision from a first model; determining a second suggested
decision from a second model; and transferring the first suggested
decision and the second suggested decision to the user device.
13. The method of claim 12, wherein the first model is trained
based on input from a group of users.
14. The method of claim 12, wherein the first model is associated
with a particular medical specialty.
15. The method of claim 12, wherein the second model is trained
based on inputs received from a particular user of the user
device.
16. The method of claim 12, wherein transferring included
transferring a user interface including pre-selected interface
controls associated with at least one of the first suggested
decision and the second suggested decision.
17. The method of claim 12, wherein the user device includes a
wireless user interface device.
18. A computer-implemented method for adaptively supporting medical
decisions of at least one user, the method comprising: receiving a
medical input and a medical decision from a device; determining a
suggested result based at least in part on the medical input;
comparing the medical decision to the suggested result; and
providing an indication based at least in part on comparing the
medical decision to the suggested result.
19. The method of claim 18, wherein the medical decision includes a
coding decision.
20. The method of claim 19, wherein the suggested result includes a
suggested coding decision, and wherein providing the indication
includes providing a coding alert.
21. The method of claim 18, wherein determining the suggested
result includes accessing a model.
22. (canceled)
23. (canceled)
24. (canceled)
25. (canceled)
26. (canceled)
27. (canceled)
28. (canceled)
29. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] The present application claims priority from U.S.
non-provisional patent application Ser. No. 09/690,354, filed Oct.
17, 2000, entitled "SYSTEMS AND METHODS FOR ADAPTIVE MEDICAL
DECISION SUPPORT," naming inventors Risto Miikkulainen, Michael
Dahlin, and Randolph Lipscher, which application is incorporated by
reference herein in its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates generally to
computer-implemented systems and methods of gathering and analyzing
medical information and adaptively supporting medical
decision-making.
BACKGROUND
[0003] As a result of increasing populations, the per capita number
of physicians in decreasing. Thus, medical professionals have
ever-increasing pressure to be more efficiently in serving their
growing patient numbers, while maintaining consistent levels of
quality and accuracy. Many medical professionals use electronic
medical records systems (EMR systems) to aid their practices. EMR
systems can bring standardization to the storage and presentation
of medical information and can provide consistent access to medical
information.
[0004] Though EMR systems bear some advantages, the systems do not
always increase efficiency to degrees that merit the time and cost
of building and implementing them. For instance, many such systems
have one or a few centralized points of access-terminals or other
computing devices at which data may be entered and received. Users
often collect data themselves and subsequently enter the collected
data into the system, nearly doubling the work. These points of
access are also used to access data. While electronic access is
typically faster than sorting through paper files, the data may
often be accessed, printed or written, and delivered or relayed to
another medical professional or patient who is not present at the
access point. Again, the advantage of the systems over paper
methods is only slight, when weighed against the time and cost
required to build and implement the systems.
[0005] Because of the inefficiencies involved with using
centralized points of access, electronic medical systems have
rarely been adopted, except for storage purposes. Thus, systems
that might support medical professionals, or other users, with
medical decision-making have been slow to develop. In the 1970's,
systems began to develop, which attempted to integrate clinical
decision support with electronic medical records, by flagging
errors or symptoms and by suggesting questions, tests, diagnoses or
treatments. But again, users could access the systems only after
locating one of a certain few designated hardware devices. The user
was required to enter information, wait for system suggestions, and
relay the information to others at remote locations. In medical
practices, this often frustrated both the medical professional and
the patient, by disrupting patient-doctor interactions and the
fluid course of business within medical care facilities.
[0006] Over time, the systems have become more specialized. But, as
expensive and time-consuming as these systems are to build, they
are only made more cumbersome by tailoring them to meet the desires
of individual users. Medical practitioners, for example, often
practice in specialized fields, such as cardiology or pediatric
surgery. General practitioners often serve specific patient
populations. practitioners would be helped by tailoring systems to
account for the peculiarities of their particular medical field and
the history of cases that they have served, while also integrating
their individual habits or preferences for routine diagnostic
methods, terminology, certain medication types or brands, etc.,
into the systems. Thus, the current systems are not nearly as
efficient, helpful, accurate, or easy to use, as they could be, or
as users desire them to be.
[0007] As such, improved systems and methods of gathering and
analyzing medical information would be desirable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIGS. 1 and 2 include illustrations of exemplary systems, in
which at least one user device and at least one general use device
communicate data with a host computer.
[0009] FIG. 3 includes a table illustrating examples of patient
data and medical data that may be transmitted to the host
computer.
[0010] FIG. 4 includes a table illustrating examples of information
that may be transmitted to users from the host computer.
[0011] FIG. 5 includes an illustration of an exemplary method for
use by a system, such as the exemplary systems illustrated in FIGS.
1 and 2.
[0012] FIGS. 6 and 7 include illustrations of exemplary systems
that may implement an exemplary method, such as the exemplary
method illustrated in FIG. 5.
[0013] FIG. 8 includes an illustration of an electronic medical
chart graphical user interface.
[0014] FIG. 9 includes an illustration of an exemplary method that
may be implemented by a system, such as the exemplary systems
illustrated in FIGS. 1, 2, 6 and 7.
[0015] FIG. 10 includes an illustration of an exemplary
implementation of a learning-based model.
[0016] FIG. 11 includes an illustration of an exemplary
implementation of a neural networks system.
DETAILED DESCRIPTION
[0017] In a particular embodiment, a computer-implemented method
for adaptively supporting medical decisions of at least one user
includes receiving a first input from a first device, receiving a
second input from a second device, determining a suggested medical
decision based at least in part on the first input and the second
input, and transferring the suggested medical decision to the
second device.
[0018] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving a medical input from a user device,
determining a first suggested decision from a first predictive
model, determining a second suggested decision from a second
predictive model, and transferring the first suggested decision and
the second suggested decision to the user device.
[0019] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving a user medical input and a user medical
decision from a device, determining a suggested result based at
least in part on the user medical input, comparing the user medical
decision to the suggested result, and providing an indication based
at least in part on comparing the user medical decision to the
suggested result.
[0020] In an additional embodiment, a computer-implemented method
for adaptively supporting decisions of at least one user includes
receiving a set of user inputs, training a model based at least in
part on the set of user inputs, and adjusting the model based at
least in part on a set of outlier inputs.
[0021] Typically, electronic medical records (EMR) provide a user
interface via terminals or other computing devices (collectively,
user interface devices). The user interface may include one or more
methods of displaying data, such as text arranged on a screen,
icons, schematics, pictures, or audio, and this user interface can
include one or more methods of input, such as keyboard text input,
virtual keyboard text input, handwriting recognition text input,
mouse position or selection input, stylus position or selection
input, touch screen position or selection input, mouse drawing
input, touch-screen drawing input, stylus drawing input, voice
input, medical device input, remote computer input. An EMR may also
be referred to as a graphical medical record. In a particular
embodiment, an EMR may include discrete input controls, such as
check boxes, three-mode controls, radio buttons, which collect
discrete findings for storage in relation to a patient. Findings
include, for example, associated conditions, ailments, corporal
location, medical history, patient data, testing, prescriptions,
and other medical data.
[0022] Referring to FIG. 1, an exemplary embodiment of a system
includes at least one user device 100. These devices 100 allow
users to communicate medical data with a host computer 102. In an
example, the user device 100 includes a portable computing device
that is capable of communicating with other computing devices via a
wireless communication link 101. The user device 100 may include a
portable computing device, such as a handheld wireless computing
device, a wireless tablet form factor device, or a desktop or
laptop computing device. The user device 100 may also include a
computing device linked or integrated with a medical instrument.
Typically, the user interacts with the user device 100 using a
graphical user interface (GUI). In an embodiment, the GUI includes
an electronic medical chart interface that presents and organizes
data, such as EMR data.
[0023] In an exemplary embodiment, a user enters data via the user
device 100, which transmits the data via the wireless communication
link 101 to the host computer 102. The data may include patient
data, clinical data, or instructions to be executed by the host
computer 102. The host computer 102 may be a computing device or a
set of computing devices capable of receiving, transmitting,
storing, and analyzing data, and executing operations. The host
computer 102 may be a portable personal computer, a desktop
personal computer, a handheld computing device that is capable of
communicating remotely with other computers, or it may be a web
server computer.
[0024] The wireless communication link 101 may include technology
that can relay signals between a wireless computing device 100 and
a host computing device 102. Such wireless links 101 may include
infrared signals, radio frequency signals, pulse codes, or
frequency-diode modulation. In a particular embodiment, the
wireless communication link 101 may use wireless protocols, such as
IEEE 802.11 a, b, or g, IEEE 802.15, or IEEE 802.16.
[0025] In an embodiment, one or more additional users may enter
data via the user devices 100, which transmit the data via the
wireless communication link 101 to the host computer 102. The data
may include patient data, clinical data, or instructions to be
executed by the host computer 102. For example, a patient may enter
data about his condition using a user device 100 and those data may
be transmitted to the host computer 102 via the wireless
communication link 101. In another example, a medical technician or
laboratory may enter data via a user device 100, which transmits
the data via the wireless communication link 101 to the host
computer 102. This data may include the results of medical tests on
a patient.
[0026] The host computer 102 may also retrieve additional data from
computers that are external to the host computer 102. This data may
include patient data, clinical data, or instructions to be executed
by the host computer 102. In the embodiment illustrated in FIG. 1,
the external computers include at least one informational computer
104.
[0027] The host computer 102 may retrieve information that it
transmits to the user from its own memory. The host computer 102
may also, or alternatively, retrieve information that the host
computer 102 transmits to the user from computers that are external
to the host computer 102. In the embodiment illustrated in FIG. 1,
the host computer 102 retrieves information from at least one
informational computer 104. Where multiple informational computers
104 are used, they may geographically distributed and remotely
located. The host computer 102 may communicate with each
informational computer 104, such as wired or wireless methods, and
may include communication via global network, such as Internet,
wide area network, or local area network. In an embodiment, each
informational computer 104 is a server computer with which the host
computer 102 communicates across a network.
[0028] The user device 100 may retrieve the information it provides
to the user from its own memory, or from one or more external
computers. In the embodiment shown in FIG. 1, the user device 100
retrieves information from at least one informational computer (IC)
104, with which the user device 100 communicates. The user device
100 may communicate with each informational computer 104 via a
suitable component for communication among computing devices. In
the embodiment shown in FIG. 1, the user device 100 communicates
with each informational computer 104 by transmitting signals to the
host computer 102, via the wireless communication link 101. The
signals are transmitted to the informational computers 104. The
informational computers 104 return information to the host computer
102, which sends signals back to the user device 100.
[0029] The host computer 102 may communicate with the informational
computers 104, via a wireless communication link 101.
Alternatively, the host computer 102 may communicate with
informational computers 104, via wired communication, such as a
global network (such as the Internet), a wide area network, or a
local area network. Alternatively, the host computer 102 may be
integrated or attached with at least one informational computer
104.
[0030] The system may also provide at least one port (not
illustrated). The user device 100 may be "docked" at a port (i.e.,
connected to a wired network) to facilitate wired communication
with the host computer 102 and informational computers 104. This
communication may include communication via a global network (such
as Internet), a wide area network, or a local area network. Docking
may be used, for example, for interacting with the host computers
102 and informational computers 104 in circumstances where
patient/medical professional relationships are not disrupted or may
be disrupted. Examples of circumstances where relationships are not
disrupted include maintenance of the system, software installation,
or where "batch" entry or review of medical data is desired. An
example of circumstances where the relationship may be disrupted
includes a case that is especially difficult and requires an
atypically lengthy amount of study and analysis. The user device
100 may be connected at a port by a docking component connecting a
computing device to a docking port. These may include plugging one
end of an electrical or optical cable into a port and the other end
into a port or socket on the user device 100.
[0031] The host computer 102 receives data and responds by
executing operations and transmitting information. The host
computer 102 may also store the data. In an embodiment, the data is
stored on the host computer 102 in the form of an electronic chart.
The data may also be stored on computers that are external to the
host computer 102. In the embodiment illustrated in FIG. 1, the
data is stored on informational computers 104 with which the host
computer 102 communicates. The host computer 102 transmits
information to the user device 100 via wireless communication link
101. Examples of transmitted information may include recommended
diagnostic questions or tests that serve to reduce the probability
of oversights during medical examination; past medical information
for the patient; alternatives for diagnoses and treatment orders;
and medical information, such as journal articles and the like. The
host computer 102 may provide relevant information and
recommendations to the user and may execute operations and provide
information automatically or in conformity with user
instructions.
[0032] The host computer 102 may also retrieve information that it
transmits to the user from a user device 100 or a general use
device 103 used by a different user to enter medical information.
In an embodiment, a patient logs in and enters medical information
about his condition into one user device 100 and this information
is transmitted to the user device 100 used by a medical doctor via
the host computer 102. In another embodiment, a patient logs in and
enters medical information about his condition into one general use
device 103 and this information is transmitted to the user device
100 used by a medical doctor via the host computer 102. In such an
embodiment, the general use device 103 used by the patient is a
personal computer, which receives information from and transmits
information to the host computer 102 via the Internet. For example,
the host computer 102 can implement a web server interface, the
general use device 103 can implement a web browser interface, and
an electronic medical record interface can be implemented using
JavaScript and HTTP to allow the general use device 103 to act as
an electronic medical record interface to allow log-in and data
entry by a patient. In an embodiment, the data is stored in the
form of an electronic chart, onto one or more informational
computers 104, with which the user device 100 communicates.
[0033] Where the host computer 102 acts automatically, it does so
by predicting decisions that the user, a separate user, or group of
users can make during the course of treating each patient. Such
decisions may pertain to elements of medical examination, such as
questions, findings, lab tests, clinical tests, or imaging;
diagnosis and resulting treatment orders; and information that the
user may desire, such as patient history information, the opinions
and recommendations of medical specialists or other medical
professionals, similar cases that the user has served, and
instructive information, such as journal articles and the like.
Upon predicting user decisions, the host computer 102 forwards data
pertaining to each decision that it has predicted. For example,
upon receiving data from the user, the host computer 102 may
predict a certain diagnosis and forward pertinent journal articles
or suggested treatment orders to the user, to a separate user, or
to a collection of users. In another embodiment, the host computer
102 may suggest diagnostic tests or questions that can eliminate
potential oversights in the user's rendering of diagnoses or
treatment orders.
[0034] Where the host computer 102 performs operations and forwards
information according to user choice, the host computer 102
predicts the decisions that may be made by user and presents these
decisions to the user by displaying the decisions on the user
device 100. The user may select from the decisions predicted by the
host computer 102, or enter alternative selections. In an
embodiment, the host computer 102 displays the predicted decisions
in the selected state (e.g., by showing a check mark in a check
box) and the user may leave the box checked to select the decision
predicted by the host computer 102 or the user may uncheck the box
and select an alternative selection, such as an alternative check
box. The host computer 102 provides information or executes
operations according to the user's input, which may include one or
more of the predicted decisions or decisions different from the
predicted decisions. For example, upon receiving patient or
clinical data about a patient from the user, the host computer 102
may predict a certain diagnosis or range of diagnoses and display
the diagnoses to the user via the user device 100. The user may
enter a selection via the user device 100, such as a selection from
among the diagnoses displayed. The user may also enter a diagnosis
or range of diagnoses that are not suggested by the host computer
102. In another example, upon receiving patient or clinical data
about a patient from the user, the host computer 102 may suggest
that a particular test may be ordered and display that test with a
checkbox indicating that that test has been selected. The user may
leave the checkbox in the checked state to select the suggested
decision, or the user may uncheck the box to override the decision.
In another embodiment, the host computer 102 suggests that a
certain medication be prescribed and display that medication with a
checkbox and a suggested dosage selected. The user may override the
selection or change the dosage or leave the selection as suggested.
For another example, upon receiving patient or clinical data about
a patient from the user, the host computer 102 may predict a list
of likely decisions by the user, such as a list of tests to
perform, medications to prescribe, or diagnoses to identify; after
displaying this list, the system receives a selection of a decision
from this list or from other orders displayed as options to the
user. Upon receiving the user's entry, the host computer 102 may
execute operations such as retrieving information, such as journal
articles, updating files, or suggesting treatment orders or further
diagnostic tests or questions. The host computer 102 displays
resulting information to the user via the user device 100.
[0035] The host computer 102 makes suggestions about a user's or a
group of users' medical decisions, via a learning component that
may execute behavioral models; rule-based algorithms, including
rules, static lists, and decision support systems, such as MEDCIN;
learning-based algorithms; neural networks; or any combination of
these. In an embodiment, the host computer 102 utilizes a
combination of rule-based algorithms and learning-based algorithms.
In this embodiment, the host computer 102 maintains a behavioral
model of the user to make suggestions of decisions that the user
can make. During an initial period of use, the behavioral model is
essentially empty. Thus, the host computer 102 makes suggestions
based on rule-based algorithms. The behavioral model is updated
when data and decisions are received from the user. As the
behavioral model develops, suggestions may be made based on both
rule-based algorithms and the learning-based behavioral model. The
resulting information may be merged together, producing a single
output, or the information of each type may be separate and made
selectable by the user. In an example, the user has the option to
disable the rule-based algorithms, when the user determines that
the behavioral model has progressed beyond the rule-based
algorithm. Additionally, the user may disable the learning
component, such that the predictive capability remains, without
updating.
[0036] In another embodiment, each operation performed by the host
computer 102 includes a plurality of decision nodes. The host
computer 102 may employ a neural network at each decision node, for
suggesting the decision that can be made by a user at the decision
node. The host computer 102 may be programmed to execute the
suggested decision or the decision input by the user. The host
computer 102 updates each neural network, after receiving data and
actual decisions from the user.
[0037] Regardless of the embodiment of the learning component, the
suggestion process is thus adapted to the user, such that the host
computer 102 attempts to predict and suggests the decisions
actually input by a user in one case, when similar data or
combinations of data are received in another case. But, the
predictive process may also be updated, by predictively customizing
the operations to the user's habits and preferences, while taking
into account the characteristics of the user's specialty and
patient populations. For instance, by updating its learning module
with user habits, preferences, etc., the host computer 102 can
increase its ability to predict when the user is likely to consult
the virtual specialist feature, what medications the user prefers
to prescribe for various ailments, what tests or diagnoses, if any,
are commonly or uniformly rendered among the user's patient
populations, etc. Thus, in addition to better predicting diagnoses
and pertinent information, the host computer 102 can tailor the
details of its operations to the user's habits and preferences.
[0038] Regardless of the algorithms or models employed by the
learning component, the host computer 102 may update the learning
component each time that data or decisions are received from the
user. Alternatively, updating may occur in "batch form," whereby
updating occurs after a set period, such as after each case is
complete, after a pre-defined number of cases are complete, after a
pre-defined time period elapses, or after a pre-defined amount of
data or decisions are received from the user, or a combination of
these.
[0039] The system also provides at least one general use device 103
for interacting with the host computer 102. The general use device
103 is a computing device that communicates with the host computer
102 and may be non-portable. For example, the general use device
103 may be a terminal computer that is dedicated to the host
computer 102. The communication link between the general use device
103 and the host computer 102 may be wireless or wired. The
communication may include communication via a global network, such
as the Internet, a wide area network, or a local area network. In
an embodiment, the general use device 103 includes at least one
desktop computer that is centrally-located in an environment, such
as a hospital, employing the system and is used for interacting
with the host computer 102, in circumstances where patient/medical
professional relationships are not disrupted or may be disrupted.
Examples of circumstances in which relationships are not disrupted
include maintenance of the system or software installation, or
where "batch" entry or review of medical data is desired. Examples
of circumstances in which relationships may be disrupted include
cases that are especially difficult and that use an atypically
lengthy amount of study and analysis.
[0040] In an embodiment, the system provides a user interface that
displays medical data elements using one or more human readable
natural languages or variations of natural language for different
audiences (e.g., technical v. lay). Internally, however, the system
represents medical data elements as numerical codes or
distinguished nomenclature items. For example, the numerical code
for hypertension might be "1000", and the display text for
hypertension might be "hypertension" (English, technical), "high
blood pressure" (English, lay), "Bluthochdruck" (German),
"hipertension" (Spanish), or "hypertensie" (Dutch.) Such a
translation may facilitate developing models of doctors who speak
language A and using those models for doctors who speak language B.
Such a translation may also facilitate communication by doctors who
speak language A with patients who speak language B. Additionally,
such translation may also facilitate input of medical data by
non-medically-trained users such as patients in a first language
and output of the medical data in a second language readable by a
physician.
[0041] Regardless of the embodiment of the learning component, the
prediction process is thus adapted to the user, such that the user
device 100 may predict the decisions actually input by a user in
one case, when similar data or combinations of data are received in
another case. But, the predictive process may also be updated, by
predictively customizing the operations to the user's habits and
preferences, while taking into account the characters of the user's
specialty and patient populations. For instance, by updating its
learning module with user habits, preferences, etc., the user
device 100 can increase its ability to predict when the user is
likely to consult the virtual specialist feature, what medications
the user prefers to prescribe for various ailments, what tests or
diagnoses, if any, are commonly or uniformly rendered among the
user's patient populations, etc. Thus, in addition to better
predicting diagnoses and pertinent information, the user device 100
can tailor the details of its operations to the user's habits and
preferences.
[0042] Referring to FIG. 2, an alternative embodiment includes at
least one user device 300. The user device 300 includes a portable
computing device that is capable of communicating with other
computing devices via a wireless communication link. The user
device 300 may include a portable computing device, including those
examples described with reference to FIG. 1. The user interacts
with the user device 300 using a graphical user interface (GUI). In
an embodiment, the GUI includes an electronic medical chart
interface that presents and organizes the data. A user enters data
via the user device 300, which transmits the data via wireless
communication link 301 to the receiver/transmitter 305. The data
may include patient data, clinical data, or instructions to be
executed by the host computer 302, such as particular information
that the host computer 302 is to retrieve, analyze, or transmit.
The receiver/transmitter 305 relays signals that correspond to
information and instructions between the host computer 302 and each
user device 300. The receiver/transmitter 305 may be attached or
integrated with the host computer 302, or it may communicate with
the host computer 302 by remote component, such as wireless or
network technologies. The host computer 302 may include an
embodiment selected from the examples described in relation to FIG.
1.
[0043] Communication with the receiver/transmitter 305, by the host
computer 302 and each user device 300, may be achieved using
technology for relaying signals between a wireless computing device
and a signal receiver/transmitter 305. Such wireless technologies
may include infrared signals, radio frequency signals, pulse codes,
or frequency-diode modulation. In an embodiment, radio frequency
signals are used to accomplish the wireless transmission of data
from the wireless devices to a radio signal receiver/transmitter.
In particular, the receiver/transmitter 305 may communicate using
standard protocols, such as IEEE 802.11 a, b, g, IEEE 802.15, or
IEEE 802.16.
[0044] In an embodiment, multiple decisions are predicted or
executed. For example, if a triage nurse enters into the system
that a patient reports chest pain, the system may predict the
following actions: (1) page doctor, (2) page nurse, (3) get chest
x-ray, and (4) order an electro-kardio-gram (EKG). In an
embodiment, the system could communicate with a network to initiate
some of these actions by (1) sending a message to the on-call
doctor's pager or EMR device, (2) sending a message to the on-call
nurse's pager or EMR device, (3) scheduling a chest x-ray with the
radiology department, and (4) reserving an EKG machine and
transmitting a message to a staff member to bring the EKG machine
to the patient and connect it to the patient.
[0045] In an embodiment, the step of executing a decision includes
electronically transmitting instructions to a medical device. For
example, executing a decision may include requesting that a medical
monitoring device take a reading, store a reading, change
monitoring parameters, or transmit a reading. In a particular
example, the host device 302 may direct a blood pressure monitor
connected to a patient to obtain a blood pressure of the patient.
In another example, the host system may query a heart rate monitor
for a heart rate associated with the patient.
[0046] In an embodiment, data may be received from various sources.
For example, a doctor may enter data via an EMR interface, a nurse
may enter data via an EMR interface, a patient may enter data via
an EMR interface, a medical device may transmit data
electronically, a remote lab computer may transmit data
electronically, and a remote medical data storage computer may
transmit data electronically. In an embodiment, multiple clinics
may receive information about a patient, and when a doctor at a
first clinic is working with a patient, data entered about the
patient at other clinics may be used as input to the prediction
process. For example, if a patient is visiting a primary care
physician and multiple specialists, data sharing capability allows
the system to detect and warn a user of redundant prescriptions
issued by different clinics. Such a capability can both prevent
inadvertent mistakes and also "doctor shopping" where a patient
attempts to get multiple prescriptions for the same controlled
substance from multiple physicians.
[0047] In an embodiment, one or more additional users may enter
data via the user devices 300, which transmit the data via the
wireless communication link 301 to the host computer 302. This data
may include patient data, clinical data, or instructions to be
executed by the host computer 302. For example, a patient may enter
data about his condition using a user device 300 and those data may
be transmitted to the host computer 302 via the wireless
communication link 301. For example, a medical technician or
laboratory may enter data via a user device 300, which transmit the
data via the wireless communication link 301 to the host computer
302. The data may include the results of medical tests on a
patient.
[0048] The host computer 302 may also retrieve additional data from
computers that are external to the host computer 302. This data may
include patient data, clinical data, or instructions to be executed
by the host computer 302. In the embodiment shown in FIG. 2, the
external computers include at least one informational computer
304.
[0049] As illustrated in FIG. 2, the system may also provide at
least one port 303. The user device 300 may be "docked" at ports 1
through n 303 (i.e., connected to a wired network) to facilitate
wired communication with the host computer 302. The communication
may include communication via global network (such as Internet),
wide area network, or local area network. Docking is used for
interacting with the host computer 302, in circumstances where
patient/medical professional relationships are not disrupted or may
be disrupted. Examples of circumstances where relationships are not
disrupted include maintenance of the system, software installation,
or where "batch" entry or review of medical data is desired. An
example of circumstances where the relationship may be disrupted
includes a case that is especially difficult and uses an atypically
lengthy amount of study and analysis. The user device 300 may be
connected at ports 1 through n 303, by a method for connecting a
computing device to a docking port. The method may include plugging
one end of an electrical or optical cable into port n 303 and the
other end into a port or socket on the user device 300.
[0050] In an embodiment, the host computer 302 receives data and
responds by executing operations and transmitting information. The
host computer 302 may store the data received from the user. In an
example, the data is stored in the form of an electronic chart onto
one or more informational computers 304 with which the host
computer 302 communicates. The host computer 302 transmits
information to the user device 300 via a wireless communication
link 301 and a receiver/transmitter 305. Examples of the
information may include recommended diagnostic questions or tests
that serve to reduce the probability of oversights during physical
examination; past medical information for the patient; alternatives
for diagnoses and treatment orders; and medical information, such
as journal articles and the like. The host computer 302 may provide
relevant information and recommendations to the user and may
execute operations and provide information automatically or in
conformity with user instructions. In an embodiment, the host
computer 302 displays the suggested decisions in the selected state
(e.g., by showing a check mark in a check box) and the user may
leave the box checked to select the decision predicted by the host
computer 302 or the user may uncheck the box and select an
alternative selection, such as an alternative check box. Where the
host computer 302 acts automatically, the host computer 302
suggests decisions that the user may make during the course of
treating each patient. The suggested decisions may pertain to
elements of physical examination, such as questions, lab tests,
clinical tests, or imaging; diagnosis and resulting treatment
orders; and information that the user desires, such as patient
history information, the opinions and recommendations of medical
specialists or other medical professionals, similar cases that the
user has served, and instructive information, such as journal
articles and the like.
[0051] Upon suggesting user decisions, the host computer 302
forwards data pertaining to each user decision that is suggested.
For example, upon receiving patient or clinical data about a
patient from the user, the host computer 302 may predict a certain
diagnosis or range of diagnoses and display the diagnoses to the
user via the user device 300. The user may enter a selection via
the user device 300, such as a selection from among the diagnoses
displayed. The user may also enter a diagnosis or range of
diagnoses that are not suggested by the host computer 302. In
another example, upon receiving patient or clinical data about a
patient from the user, the host computer 302 may suggest that a
particular test may be ordered and display that test with a
checkbox indicating that that test has been selected. The user may
leave the checkbox in the checked state to select the suggested
decision, or the user may uncheck the box to override the decision.
In another embodiment, the host computer 302 suggests that a
certain medication be prescribed and display that medication with a
checkbox and a suggested dosage selected. The user may override the
selection or change the dosage or leave the selection as suggested.
In another example, upon receiving patient or clinical data about a
patient from the user, the host computer 302 may predict a list of
likely decisions by the user, such as a list of tests to perform,
medications to prescribe, or diagnoses to identify; after
displaying this list, the system receives a selection of a decision
from this list or from other orders displayed as options to the
user. In a further example, upon receiving data from the user, the
host computer 302 may predict a certain diagnosis and forward
pertinent journal articles or suggested treatment orders to the
user. In another embodiment, the host computer 302 may suggest
diagnostic tests or questions that can eliminate potential
oversights in the user's rendering of diagnoses or treatment
orders.
[0052] Where the host computer 302 performs operations and forwards
information according to user choice, the host computer 302
suggests the decisions that may be made by user and presents these
decisions to the user by displaying them on the user device 300.
The user may select from the decisions suggested by the host
computer 302, or enter alternatives. The host computer 302 provides
information or executes operations according to the user's input,
which may include one or more of the predicted decisions or
decisions other than those that were predicted. For example, upon
receiving patient or clinical data about a patient from the user,
the host computer 302 may predict a certain diagnosis or range of
diagnoses and display these diagnoses to the user via the user
device 300. The user may enter a selection via the user device 300
from among the diagnoses displayed, or the user may enter a
diagnosis or range of diagnoses that are not displayed in response
to the host computer 302 suggestion. Upon receiving the user's
entry, the host computer 302 may execute operations such as
retrieving information, such as journal articles, updating files,
or suggesting treatment orders or further diagnostic tests or
questions. The host computer 302 displays resulting information to
the user via the user device 300.
[0053] In an exemplary embodiment, the host computer 302 makes
suggestions about a user's medical decisions via a learning
component that may execute behavioral models, rule-based or
learning-based algorithms, and neural networks, or a combination
thereof, such as in a manner described in relation to FIG. 1.
[0054] In an embodiment, the host computer 302 utilizes a
combination of rule-based algorithms and learning-based algorithms.
In such an embodiment, the host computer 302 maintains a behavioral
model of the user to suggest decisions that the user may make.
During an initial period of use, the behavioral model is
essentially empty. Thus, the host computer 302 makes suggestions
based on rule-based algorithms. The behavioral model is updated
when data and decisions are received from the user. As the
behavioral model develops, suggestions may be made based on both
rule-based algorithms and the learning-based behavioral model. The
resulting information may be merged together, producing a single
output, or the information of each type may be presented separately
and made separately selectable by the user. The user has the option
to disable the rule-based algorithms, such as when the user
determines that the behavioral model has progressed beyond the
rule-based algorithms. Additionally, the learning component may be
disabled by the user instruction such that the predictive
capability remains without updating.
[0055] Regardless of the embodiment of the learning component, the
prediction process is thus adapted to the user, such that the host
computer 302 may predict the decisions actually input by a user in
one case, when similar data or combinations of data are received in
another case. The prediction process is thus adapted to the user,
such that the host computer 302 may predict the decisions actually
input by a user in one case, when similar data or combinations of
data are received in another case. But, the predictive process may
also be updated by customizing the operations to the user's habits
and preferences, while taking into account the characteristics of
the user's specialty and patient populations. For instance, by
updating its learning module with habits, preferences, etc., of the
user, the host computer 302 can increase its ability predict when
the user is likely to consult a virtual specialist feature, what
medications the user prefers to prescribe for various ailments,
what tests or diagnoses, if any, are commonly or uniformly rendered
among the user's patient populations, etc. Thus, in addition to
better predicting diagnoses and pertinent information, the host
computer 302 can tailor the details of its operations to the user's
habits and preferences.
[0056] Regardless of the algorithms or models employed by the
learning component, the host computer 302 may update the learning
component each time data or decisions are received from the user.
Alternatively, updating may occur in "batch form," whereby updating
occurs after a set period, such as after each case is complete,
after a pre-defined number of cases are complete, after a
pre-defined time period elapses, or after a pre-defined amount of
data or decisions are received from the user, or a combination
thereof. The host computer 302 may also update the learning
component each time that the user device 300 is docked at a port n
303.
[0057] The host computer 302 may retrieve the information
transmitted to the user from one or more informational computer
(IC) 304 with which the host computer 302 communicates. The host
computer 302 may communicate with each informational computer 304
via a method of communicating among computing devices. This may
include wireless and wired methods, and may include communication
via a global network (such as Internet), a wide area network, or a
local area network. In an embodiment, each informational computer
304 is a server computer with which the host computer 302
communicates across a network.
[0058] FIG. 3 includes a table 400 that displays exemplary data
that may be transmitted by a user to the host computer, via a user
device or a general use device, as described in relation to FIGS. 1
and 2. The column at 401 shows examples of patient data that may be
transmitted to the host computer. The column at 402 shows examples
of clinical data that may be transmitted to the host computer.
Examples of patient data 401 that may be transmitted to the host
computer include a patient's name and personal contact information,
name and contact information of one to contact in emergency, as
well as social security number and birthdate. Patient data may also
include, but is not limited to, information bearing upon medical
diagnosis and treatment, such as ethnicity, medical history,
Do-Not-Resuscitate (DNR) orders, allergies to drugs and other
allergens, current and prior medications, and health habits, such
as smoking, toxic exposure, and use of drugs or alcohol. Examples
of patient data may also include payment-related information, such
as insurance information and employment data. The list in FIG. 3 is
intended to be illustrative and not all inclusive. It may be
appreciated that many other types of patient data may be
transmitted also.
[0059] Examples of clinical data 402 that may be transmitted to the
host computer include vital information, such as height, weight,
body temperature, pulse rate, blood pressure, pulse oxygenation,
blood type, and blood pH. Examples of medical data may also include
data that is directly pertinent to diagnosing a medical problem,
such as the patient's complaints and symptoms, physical examination
findings and laboratory results, and the patient's answers to
diagnostic questions. Example medical data also may include the
medical professional's diagnosis and treatment orders. When
medication is dispensed for treatment, the medical data may include
prescription instructions and information and information and
instructions for the dispensing pharmacy. In addition, examples of
medical data may include the date of visit by the patient,
follow-up recommendations, and the name of the medical professional
attending the patient on the date of visit. The list in FIG. 3 is
intended to be illustrative and not all inclusive. It may be
appreciated that many other types of clinical data also may be
transmitted.
[0060] FIG. 4 includes a table 500 illustrating examples of
information that may be output to a user by the host computer, as
described in relation to FIGS. 1 and 2, in order to provide medical
decision support to the user. Examples of information pertaining to
patient assessment that may be output by the host computer to the
user include but are not limited to recommendations for diagnostic
questions, physical examinations, and medical tests, such as blood
tests or imaging (X-ray, MRI, CT, etc.); medical history data
concerning the patient; the name(s) and comments of any referring
medical personnel(s); alerts to common oversights in patient
assessment, which may be checked at medical personnel discretion;
and medical information, such as journal articles and the like.
[0061] In addition, examples of information pertaining to diagnoses
that may be output by the host computer to the user include, but
are not limited to, names, or other identifying references, for
potential diagnoses, along with a brief description of each
diagnosis that is presented; recommendations for additional data
(e.g., pertinent negatives) that would exclude one or more
potential diagnoses; and medical information, such as journal
articles and the like.
[0062] Further, examples of information pertaining to treatment
orders that may be output by the host computer to the user include,
but are not limited to, recommendations for treatment orders or
alternative treatment orders, including presenting to the user
alternative medication types and brands; alternative
recommendations for surgical or non-surgical procedures;
alternative recommendations for behavior modifications (e.g., bed
rest) or diet modifications (e.g., fluids); and presenting the user
with medical information, such as journal articles and the
like.
[0063] An exemplary embodiment may also include a "virtual
specialist" feature. This feature is useful for supporting the user
with decisions and information pertaining to injuries or ailments
that are beyond the scope of the user's judgment of assessment,
diagnosis, or treatment. The feature may be based upon the
experience, input, or predictive model of a separate user or a
group of users. Such a feature may be accessed by user selection,
or the host computer can automatically select and query a virtual
consultant model based on data received from the user. For example,
the host computer may use rule-based or learning-based algorithms
to determine when to access the virtual specialist feature and
which virtual consultant to use. To supply the virtual specialist
information, the host computer draws upon information retrieved and
analyzed from memory or from at least one informational computer,
such as those described in relation to FIG. 1 and FIG. 2.
[0064] When executing the virtual specialist feature, the host
computer supplies the user with decisions and information that
pertain to the specific ailment or injury and information regarding
the probable actions or recommendations of a medical professional
or group of medical professionals that specialize in a medical
discipline that addresses the ailment or injury. For instance, a
general medical practitioner who encounters a child suffering from
poor blood circulation may not have the ability to immediately
consult a pediatric surgeon or cardiologist. In an example, the
system may provide a virtual specialist to meet me practitioner's
query, by dispensing information about what such a specialist may
most likely do or recommend, allowing treatment to continue until a
consultation with a specialist can be performed. The virtual
specialist may also provide the user with information that allows
"meantime care," which suggests actions that may maintain the
patient in sustainable condition, until a specialist arrives for
in-person consultation.
[0065] In an embodiment, the host computer receives input from one
or more collections of different medical personnel, and develops a
behavioral model for each collection. A user may view predictions
from collections of medical personnel, where each collection may
include just one medical personnel, which may be the user or a
medical personnel that is not the user, or a group of medical
personnel that includes or does not include the user. Using the
virtual specialist feature, the user can direct the host computer
to provide information corresponding to the likely actions of
"practitioner X or group Y," given the data that has been input
about a patient to the host computer. For example, practitioner X
may be a specialist or even a hypothetical practitioner that is
programmed to reflect standard protocol among practitioners of a
certain type. Group Y may be a collection of specialists, such as
cardiologists, a collection of elite medical personnel, such as the
group of medical personnel at Johns Hopkins, or even a hypothetical
group of medical professionals in general, that reflect standard
protocol among medical personnel of a certain type. As such, the
virtual specialist feature may make suggestions from various
perspectives. For example, upon the user accessing the virtual
specialist feature in regard to a specific patient, the host
computer may, for example, output a likely treatment or action to
be rendered by medical personnel X, by group Y specialists, and the
"standard choices" by the medical community. Such output may
provide the user with several choices in an efficient manner.
[0066] In another embodiment of the virtual specialist feature, a
general medical practitioner who is interested in improving the
effectiveness of his documentation of medical encounters and of his
coding or billing practices may not have billing or coding
expertise. The system may provide a "virtual" specialist to meet
the practitioner's interests, by predicting what an expert coding
specialist would document at each phase of the encounter, by
predicting questions an expert coder would ask to enhance the
current documentation to increase reimbursement levels, or
predicting the code an expert coder would select with regard to a
particular patient encounter.
[0067] In an embodiment, the system may be used to improve the
coding accuracy of users. For example, in an embodiment, the system
stores coding decisions by users of the system. In an example, the
system may learn a particular user's coding practices based on the
users coding decisions. In another example, the system displays
comparisons of a user's coding decision to the coding decisions of
a target, such as another user, a group of users (e.g., the
department or clinic as a whole, another clinic, or an exemplary
coding decision model). In an embodiment, the comparison compares
the coding decisions made by the user for the user's population of
patients to the coding decisions made by the target for the
target's population of patients. In another embodiment, this
comparison adjusts for the different patient populations and
findings by generating a predictive coding model for the user or
for the target and (1) comparing the user's actual coding to the
coding decisions that the target's predictive model generates for
the same set of patients and findings or (2) comparing the
decisions that the user's predictive model generates for the
target's patients and findings to the targets actual decisions or
(3) compares the decisions of the user's model and the target's
model for a common set of input patients and findings.
[0068] In an embodiment, the system notifies a user that he may be
over-coding when past user decisions reflect higher codes than the
target decisions. Over-coding can be risky for a doctor because it
may invite regulatory audits. Such a notification may be triggered
when the user's mean or median coding is significantly above the
targets. In another example, the notification may be triggered when
at least one coding decision results in a code higher than at least
one target coding decision.
[0069] In an embodiment, the system notifies a user that he may be
under-coding when user decisions reflect lower codes than the
target decisions. Such a notification is typically triggered when
the user's mean or median coding is significantly below the
targets. In another example, the notification may be triggered when
at least one coding decision results in a code lower than at least
one target coding decision.
[0070] In another embodiment, the stored past coding decisions or
models may be viewed by a user to evaluate decisions by multiple
medical staff. For example, a manager of a practice may compare the
coding decisions of different physicians to determine when they are
commonly over- or under-coding compared to peers. Alternatively, a
manager of a practice may examine the aggregate coding decisions of
the practice and compare them to a target model.
[0071] In a further embodiment, the system may be used to improve
the diagnostic, prescribing, or disease management decisions by
users by analyzing past decisions. For example, the system stores
medical decisions by users of the system. In an example, the system
displays comparisons of a user's medical decision to the medical
decisions of a target, such as another user or a group of users
(e.g., the department or clinic as a whole, another clinic, or an
exemplary coding decision model). In an embodiment, the comparison
compares the medical decisions made by the user for the user's
population of patients to the medical decisions made by the target
for the target's population of patients. In a further embodiment,
the comparison adjusts for the different patient populations and
findings by generating a predictive medical model for the user or
for the target and (1) comparing the user's actual medical
decisions to the medical decisions that the target's predictive
model generates for the same set of patients and findings, (2)
comparing the decisions that the user's predictive model generates
for the target's patients and findings to the targets actual
decisions, or (3) comparing the decisions of the user's model and
the target's model for a common set of input patients and
findings.
[0072] For example, a medical decision includes a decision to refer
a patient with a chronic illness to a disease management program
that can coordinate and monitor patient care. Such disease
management programs have the potential to save significant amounts
of health-care spending and improve treatment effectiveness for
conditions like diabetes. The system stores decisions to refer
patients to disease management programs and compares such decisions
by one user to the decisions by other users or a model and notifies
a user when he fails to refer one or more patients to a program.
Such a notification may be immediate, such as when a chart is
signed, or may be delayed (e.g., via an email to the user at the
end of the day). In an example, such a notification may be
triggered by a single decisions or by multiple decisions (e.g.,
notify a user when he fails to assign a patient to a disease
management program 50% or less of the predicted times). Similarly,
the system can track whether a user is prescribing a medication or
class of medications when predicted, and generate a notification
when prescribed when not predicted. In an embodiment, the
notification goes to the user whose actions are being predicted. In
another embodiment, the notification goes to a second user, such as
a manager, trainer, or pharmaceutical company representative.
[0073] Now referring to FIG. 5, the disclosure is also directed to
a method for providing adaptive medical decision support. As
illustrated at 601, a user transmits data and instructions to the
host computer. The data transmitted by the user includes patient
and clinical data, and may include data that are exemplified in
FIG. 3. The user may use the user device to communicate with the
host computer via a wireless communication link. The user may also
use a general use device to communicate with the host computer, via
global, wide area, or local area network technology. The user may
communicate with the host computer via a graphical user interface,
such as an electronic medical chart interface.
[0074] As illustrated at 602, the host computer stores and analyzes
the data and executes the instructions it receives. The host
computer may store the data temporarily or permanently using its
own memory components, or may communicate the data to one or more
informational computers for storage. As illustrated at 603, the
host computer determines whether additional information is desired
or recommended and may request the additional information from the
user, as illustrated at 604. When such requests are made, the user
may enter data and instructions, resulting in at least one
additional iteration of 601-604.
[0075] As illustrated at 605, the host computer predicts or
suggests the user's decisions from the data received and outputs
these predicted decisions to the user. The host computer may also
output information to the user that the host computer suggests
obtaining if the user had actually made each of the decisions. For
example, if the data received from the user pertains to the
patient's symptoms or a medical problem, the host computer may
suggest and output potential diagnoses and may also suggest further
diagnostic actions, give warnings pertaining to particular
diagnoses that merit further investigation of patient symptoms,
provide journal articles and the like that discuss each potential
diagnosis, and suggest treatment orders or courses of action. The
host computer may retrieve information from its own memory
components, for example, from a database, or from at least one
informational computer with which the host computer communicates.
Alternatively, the host computer may output the suggested decisions
and await the user's instruction to retrieve pertinent
information.
[0076] The host computer makes suggestions about a user's medical
decisions as illustrated at 605, via a learning component that may
execute behavioral models, rule-based algorithms, learning-based
algorithms, neural networks, or a combination thereof. Where a
combination of rule-based algorithms and learning-based algorithms
are used, the resulting information may be merged together,
producing a single output, or the information of each type of
algorithms may be separately displayed and made separately
selectable by the user.
[0077] The output from the host computer as illustrated at 605 may
include decisions and information output from a virtual specialist
feature. Such decisions and information may pertain to injuries or
ailments that are beyond the scope of the user's judgment of
assessment, diagnosis, or treatment, and may reflect the
experience, decisions, or input of a separate user or a group of
users. The virtual specialist feature may be accessed by user
selection, or the host computer can automatically select and query
a virtual consultant model based on data received from the user.
The host computer may use rule-based or learning-based algorithms
to determine when to access the virtual specialist feature and
which virtual consultant to use. To supply the virtual specialist
information, the host computer draws upon information retrieved and
analyzed from memory or from at least one informational
computer.
[0078] When executing the virtual specialist feature, the host
computer supplies the user with decisions and information that
pertain to the specific ailment or injury and information regarding
the probable actions or recommendations of a medical professional,
or group of medical professionals, that specializes in a medical
discipline that addresses the ailment or injury. For example, a
general medical practitioner who encounters a child suffering from
poor blood circulation may not have the ability to immediately
consult a pediatric surgeon or cardiologist. The present system may
provide a "virtual" specialist to meet the practitioner's queries
by dispensing information suggesting what such a specialist may
likely do or recommend, allowing time until an actual consultation
can be made. The virtual specialist may also provide the user with
information that allows "meantime care," which suggests actions
that may maintain the patient, until a specialist arrives for
in-person consultation.
[0079] As illustrated at 606, the user receives output from the
host computer, which includes the decisions predicted by the host
computer, whether or not accompanied by additional pertinent
information. The user may transmit more data to the host computer,
in response to the output information that the user has received,
resulting in an additional iteration of 601-606.
[0080] As illustrated at 607, the user transmits decisions to the
host computer. The decisions transmitted by the user may be
selected from among those decisions output to the user, or the user
may input decisions that the host computer did not predict. The
decisions input by the user may pertain, for example, to patient
assessment, such as medical tests or physical examinations to be
employed by the user. In an example, the decisions may pertain to
diagnosis, such as the user's adjudged identification of a disease
or injury. In another example, the decisions may pertain to
treatment orders that are to be given by the user, including for
example, specific procedures, types and brands of medication, or
modifications in a patient's behavior or diet. In a further
example, the decisions may pertain to multiple aspects of patient
care. As illustrated at 608, the host computer stores and analyzes
the decisions transmitted by the user. The host computer may store
the decisions temporarily or permanently using its own memory
components, or on one or more informational computers with which
the host computer communicates.
[0081] As illustrated at 609, it is determined whether the user's
decisions are final. When not, as illustrated at 610, the host
computer may output to the user additional information, such as
suggestions, alternatives, warnings, and/or highlights pertaining
to the decision(s) received from the user, and new suggested
decisions and pertinent information as described previously. The
user may receive the output, resulting in an additional iteration
of 606. The user may transmit decisions to the host computer, in
response to the output suggestions, alternatives, warnings, or
highlights, resulting in an additional iteration of 607-609.
Alternatively, the user may update or reenter data, prior to
entering decisions, resulting in an additional iteration of
601-609.
[0082] As illustrated at 611, once the user's decisions have been
made final, the host computer processes the data and decisions
pertaining to a patient's case and may enhance the predictive
operations by updating the host computer learning component. Where
the host computer uses rule-based algorithms, the host computer may
customize operations, by updating the rules. Where the host
computer uses learning-based algorithms, such as a Bayesian
network, inductive logic, or linear regression, in order to
maintain a model a user's behavior and preferences, the host
computer may update operations by updating the model. Where the
host computer employs neural networks at various decision nodes, as
described with reference to FIG. 1, the host computer updates each
neural network, after receiving data and actual decisions from the
user.
[0083] At 611, the user may be part of one or more groups that are
being modeled. In such a case, the host computer processing the
data and decisions pertaining to a patient's case, and updating its
operations and predictive models, take place once the user's
decisions have been made final.
[0084] The prediction process is thus adapted to the user, such
that the host computer may predict the decisions actually input by
a user in one case, when similar data or combinations of data are
received in another case. In another example, the predictive
process may also be updated, by predicatively customizing the
operations to user habits and preferences, while taking into
account the characteristics of the user's specialty and patient
populations. For example, by updating the learning module with user
habits and preferences, the host computer can increase its ability
to predict when the user is likely to consult the virtual
specialist feature, what medications the user prefers to prescribe
for various ailments, or what tests or diagnoses, if any, are
commonly or uniformly rendered among the user's patient
populations. Thus, in addition to better predicting diagnoses and
pertinent information, the host computer can tailor the details of
its operations to the user's habits and preferences.
[0085] Regardless of the algorithms or models employed by the
learning component, the host computer may update the learning
component each time that data or decisions are received from the
user. Alternatively, updating may occur in "batch form," whereby
updating occurs after a set period, such as after each case is
complete, after a pre-defined number of cases are complete, after a
pre-defined time period elapses, or after a pre-defined amount of
data or decisions are received from the user, or a combination
thereof.
[0086] FIG. 6 illustrates an embodiment for using the present
system and method for adaptive decision support, when the system
and method are implemented in a multi-user medical environment. In
this embodiment, medical professionals are placed into groups that
may include one or more members. The groups may be categorized by
type of professional, such as nurses, surgeons, medical personnel,
medical personnel assistants (P.A.s), and medical students, among
others. Groups may also be categorized by specialty or department,
such as in a hospital. In such a case, medical professionals
(nurses, doctors, P.A.s, etc.) who work in one specialty or
department would potentially belong to the same corresponding
group. Groups may alternatively include doctors who practice in one
specialty, such as "cardiologists," with nurses, etc., falling into
a nurse group or even a cardiology nurse group, for example. Groups
may also include further specialized doctors, such as "Johns
Hopkins cardiologists," or "Sarnoff fellows." Groups may
alternatively include different external authorities with their own
predictive models. For example, different payers may recommend or
predict different medications for the same problem. Each such payer
produces a model based on their own predictions, and the system
uses different models for different patients depending on which
payer is associated with a patient.
[0087] In an embodiment, the host computer receives and stores each
predictive model, and it uses different models for different
patients. In another embodiment, different groups may choose not to
transmit their models to host computers. Instead, a host computer
selects a remote prediction computer that stores a model, transmits
medical findings to the remote prediction computer, the remote
prediction computer generates at least one suggestion based on the
medical findings and the predictive model, and the prediction
computer transmits the at least one suggestion to the host
computer.
[0088] For example, a Group I 701 may include individual
internists. Each internist communicates with the host computer 704,
whether by a user device or a general use device, as are described
with reference to FIGS. 1 and 2. In a particular example, the host
computer 704 implements the exemplary method described in relation
to FIG. 5. Data, decisions, and information that are stored on the
host computer 704, or on the informational computers 706 with which
the host computer 704 communicates, are separated into a grouping
that corresponds to the Group I 701. The host computer 704
customizes operations and predictions to suit each specific
internist, in accordance with the above-described method, and may
create a model of the behavior, preferences, patient populations,
or medical specialties of the Group I 701. Alternatively, the Group
I 701 may devise its own model and communicate the model to the
host computer 704 as a Model A. Group N 702 and Group S 703 may
interact with the host computer 704 in the same fashions as
described for the Group I 701.
[0089] One risk of adaptively trained prediction is that a system
may be commonly exposed to common conditions and become unable to
predict rare decisions for rare conditions. In an embodiment, the
system incorporates models that include rare conditions and
decisions in their prediction algorithms. For example, Model M
generated by Group M 705 may include a set of findings relating to
rare but dangerous conditions and the predicted decision for each
such set of findings. This model may act as a training set of data
to seed the customized prediction algorithms used by users with
these rare cases to ensure that such rare cases can be
recognized.
[0090] In another embodiment, the system learns to predict the set
of inputs associated with a decision, such as a diagnosis or
treatment. When a decision is made but the current input data falls
outside of the predicted range or input data, the system predicts
another decision. For example, the system can learn the set of
findings typically associated with a given diagnosis. When that
diagnosis is made, but the entered findings are inconsistent with
that finding, the system can predict that either the findings is to
be updated or the diagnosis is to be changed. In an embodiment, the
system displays a warning to the user indicating that the findings
are unusual for the diagnosis. In one such embodiment, the system
detects "outlier" inputs that may indicate an unusual situation
that warrants extra diagnostic or treatment care. For example,
typically the finding of "diarrhea" predicts a diagnosis of one ore
more relatively minor gastrointestinal problems. Similarly, the
diagnosis of several relatively minor gastrointestinal problems
predicts a finding of diarrhea. However, when the findings include
both diarrhea and recent foreign travel, the unusual recent foreign
travel finding is not predicted by the model for minor
gastrointestinal illnesses, and the system would detect that an
outlier finding exists and display a list of "outliner", "unusual",
or "unexplained" finding as a warning to the user. Of course, over
time, a doctor that sees a large number of patients with diarrhea
and foreign travel may produce enough input/decision cases for the
learning algorithm to predict decisions in such cases. Such an
"outlier" mechanism can provide a component to flag cases where the
model may not sufficiently account for inputs so the user should
exercise special care.
[0091] In an embodiment, a learning algorithm or model may be
periodically trained, adjusted or updated using a set of outlier
inputs. For example, a user may provide inputs, such as a sequence
of findings and diagnoses associated with patient visits occurring
over time. These inputs may be used to train and adjust a model to
accommodate a particular user's practice. However, over time, the
model may tend to lose the ability to predict unusual diagnoses. To
compensate, a set of outlier inputs may periodically be used to
adjust the model to reintroduce unusual diagnoses not usually
encountered by the user.
[0092] In an embodiment, updating individual and group models may
be slightly altered to provide for the status of individual users.
In this embodiment, only certain users' input may be used to update
individual or group models that are used to implement the
predictive capabilities. As a corollary, some users' models may be
updated based not on their input, but upon the input of other
users. For instance, the Group I 701 in FIG. 6 may include both
resident doctors at a hospital, in addition to interns who have
only recently begun practicing. Thus, the system operators may
elect for only the resident doctors' input and decisions to be used
to update the model; and, the system may select the input and
decisions of the resident doctors to be used in updating the
internists' individual predictive models also.
[0093] In a particular example, the Group S 703 includes medical
students. The system operators may select certain doctors' input
and decisions to be used to update both the models and the
individual predictive models for each student, even though the
doctors may be placed in an entirely different group. The same may
apply for the Group N 702, for example, if the group consisted of
nurses. Alternatively, system operators may allow for individual
users to update their own predictive models, but particular users'
input and decisions to update group models, thereby allowing for
other users to evaluate their progress in learning the practice of
medicine or a certain field of medicine.
[0094] In a further example, the Group M 705 may represent groups
that transmit external or standardized models to the host computer
704, which are not developed by the host computer 704 from
processing the actions of individuals within the environment. For
example, models for certain types of care that are not extensively
served by a certain hospital, such as trauma, can be communicated
to the host computer 704 from sources external to the hospital.
Such models may be the result of standard medical practices,
protocols established by those who manage the environment, such as
a hospital protocol, protocols developed from evidence-based
medicine, protocols developed by a payor, protocols provided by a
pharmacy benefits management company, protocols developed by a
pharmaceutical company, or protocols developed by business
managers, including billing and coding specialists. Alternatively,
protocols may be developed by elite groups of medical personnel,
such as medical schools or teaching hospitals. In such cases, only
the Model M is communicated to the host computer by the Group M
705. The Group M 705 may include the creators of the model. System
operators may elect for the external models to include the
individual or group models for certain users, in accordance with
the embodiments described above.
[0095] As a result of the embodiment illustrated in FIG. 6, the
host computer 704 may provide the "virtual specialist" feature, as
described in relation to FIGS. 5 and 6, by allowing users from
different groups to access the developing models and data of other
groups of users, models of individual users, and models placed on
the host computer 704 by groups such as the Group M 705. Such
access can result in the capacity for users to receive virtual
second opinions, for example, by accessing the models of other
groups, or of individual users, such as managing medical
personnel.
[0096] FIG. 7 illustrates another embodiment for using embodiments
of the system and method for adaptive decision support. In such
embodiments, medical professionals are placed into groups. The
users of each group communicate directly with each iteration of the
adaptive system. For example, each of the users in the Group I 801
communicates with the host computer HCi, whether by a user device
or a general use device. HCi, for example, may implement the method
described in relation to FIG. 5. Data, decisions, and information
for each user in the Group I 801 and corresponding models are
stored on HCi or on the informational computers with which HCi
communicates. HCi customizes its operations to suit each specific
user in the Group I 801 and may create a model of the behavior,
preferences, patient populations, or medical specialties of the
Group I. A Group N 802 and a Group S 803 interact with the host
computers HCn and HCs, respectively, each of which also may
implement the method described in relation to FIG. 5, in the same
fashion as the Group I interacts with HCi. In an embodiment,
updating individual and group models may be slightly altered to
provide for the status of individual users. In this embodiment,
particular users' input may be used to update individual or group
models that are used to implement the predictive capabilities. As a
corollary, some users' models may be updated based not on their
input, but upon the input of other users. For instance, the Group I
801 in FIG. 7 may include both resident doctors at a hospital, in
addition to interns who have only recently begun practicing. Thus,
the system operators may elect for only the resident doctors' input
and decisions to be used to update the Modeli; and, also may select
only the input and decisions of the resident doctors to be used in
updating the internists' individual predictive models.
[0097] In an exemplary embodiment, the Group S 803 includes medical
students. The system operators may select certain doctors' input
and decisions to be used to update both the Models and the
individual predictive models for each student, even though the
doctors may be placed in an entirely different group. The same may
apply for the Group N 802, for example, if the group consisted of
nurses. Alternatively, system operators may allow for individual
users to update their own predictive models, but for particular
users' input and decisions to update group models, thereby allowing
for other users to evaluate their progress in learning the practice
of medicine or a certain field of medicine.
[0098] The Group M 804 may represent groups that transmit external
or standardized models to the host computer, such as those
described in relation to FIG. 6. In such cases, only the Model M is
communicated to the host computer HCm by the Group M 804. The Group
M may include the creators of the model. The separate host
computers HCi, HCn, HCs, and HCm, communicate with each other
directly or via a hub 805. The hub 805 may include a switching
device or a computing device, such as a server computer.
Communication among the host computers may take place by a method
of computing devices to communicate remotely with each other.
Examples include global, wide area, and local area networks. System
operators may elect for the external models to include the
individual or group models for certain users.
[0099] As a result of the embodiment illustrated in FIG. 7, each
host computer may provide the "virtual specialist" feature by
allowing users from different groups to access the continually
developing models and data of other groups of users, models of
individual users, and models placed on the system by groups such as
the Group M 804. Such access may result in the capacity for users
to receive virtual second opinions, for example, by accessing the
models of other groups, or by individual users, such as managing
medical personnel.
[0100] FIG. 8 illustrates an example of an electronic medical chart
graphical user interface 900 that may be used in conjunction with
an embodiment of the systems and methods, such as the exemplary
systems described in relation to FIGS. 1-3. In this example
embodiment of the GUI, various categories of information are
selectable from tabs 901 labeled with the informational categories.
Example categories may include Patients, Schedule, Health Plans,
and the like. When a tab 901 is selected, a user may enter or
choose information within an informational region 902. When
information is chosen, such as a particular patient listed under
the Patients tab, subcategories of information 903 are selectable
by the user. The subcategories 903 (exemplified first by
HPI--History of Present Illness) provide certain types of
information within the informational region that are specific to,
for example, the chosen patient. The information illustrated may
include data and data fields to provide the user with information
about the chosen information, including information exemplified in
FIG. 3, but also including user-directed information, such as
information about individual correspondence, schedules, messages,
forms, other administrative tasks, and narratives. The GUI may also
provide a login/logout feature and may include the user's name, as
exemplified at 904.
[0101] FIG. 9 includes a flow diagram. As illustrated at 1001, the
user logs into the system. The user may login via a portable
computing device, or a general use device. In an embodiment, the
user logs in, using a portable computing device that is provided
with an electronic medical chart GUI. As illustrated at 1002, the
user may next select the task to be performed, such as entering new
data, updating data, or reviewing data. As illustrated at 1003, the
user selects the patient for whom the task may be performed, which
may include selecting an existing patient or opting to begin a new
patient record. The user may enter, update, and review, data for a
plurality of tasks 1004, such as preliminary patient information,
physical examination and assessment, diagnosis, and treatment
orders. During each task 1004, the method described in relation to
FIG. 5 is executed. The system may allow for each task 1004 to be
performed and ended directly after patient selection, without
proceeding to the other tasks. The system also allows for the tasks
1004 to be performed consecutively, for example, with new patients.
Once a task 1004 is completed, the user may select a new patient or
a new task 1004, or the user may proceed to a finishing step 1005.
The finishing step 1005 allows the user to review the results of
the session and to complete administrative tasks, such as
submitting narratives, changing scheduling, drafting
correspondence, and the like. After the finishing 1005, the user
may proceed to another patient or task, or log out of the system at
1006.
[0102] FIG. 10 describes an exemplary implementation of a
learning-based model for a user at a decision point implemented via
neural networks. FIG. 10 displays a learning model 1100 that
receives examples of input data 1101, which may include findings
about a current patient, such as allergies, symptoms, test results,
and medical history. In general, such input data are data that the
medical professional considers in making a medical decision in
regard to the patient. FIG. 10 displays example outputs 1103, which
include the potential medical decisions that the medical
professional may make, such as diagnoses, diagnostic tests,
questions, or treatment orders. Each input variable 1101 is
represented as one unit at the input layer 1102, and is assigned an
activation value. The activation value may include, for example, a
numerical scale, such as a 0/1 decision, with missing values
represented by e.g. 0.5. In the output 1103, the activation of each
unit represents the a posteriori probability that the choice is
correct, given the training data. Thus, the system indicates what
the choices are and how confident the system is in each of the
choices. The network is trained with back-propagation based on the
actual cases of inputs and decisions collected by the system.
Standard methods of cross-validation can be used to decide when to
stop training. Different training sets are constructed to model
different physicians or groups of physicians. Periodically, as new
data come in, the networks may be further trained with the more
comprehensive data set to improve accuracy and coverage of
different cases.
[0103] A neural network based learning system may be implemented
using standard techniques, such as that illustrated in FIG. 11.
This exemplary neural networks system includes an input layer 1201,
having input units 1202; a hidden layer 1203, having hidden units
1204; and an output layer 1205, having output units 1206; and
target patterns 1207. The input layer 1201 is connected to the
hidden layer 1203 by input connections that connect the input units
1202 to each of the hidden units 1204. Similarly, the hidden layer
1203 is connected to the output layer 1205 by output connections
that connect each of the hidden units 1204 to each of the output
units 1206.
[0104] The values of input variables are used to activate the input
units 1202. Each hidden unit 1204 computes a weighted sum of the
input unit activations. The sums are weighted by the connection
weights, which increase as one moves from the output connections to
the input connections. The hidden unit 1204 outputs an activation
that is a nonlinear, continuous function (such as a sigmoid or a
Gaussian) of the sum. Analogously, each output layer unit 1206
computes the weighted sum of the hidden layer activations, and
generates a nonlinear function of the sum as its output. The output
activations are interpreted as values of output variables.
[0105] The network is trained by providing target patterns 1207,
which are correct values for the output units, with regard to each
input variable. The weights of the network are changed using, for
example, a back-propagation training procedure. An error signal for
each output unit 1206 is formed as a difference between the output
unit 1206 and the target patterns 1207.
[0106] A gradient of the error signal with regard to the network
weight values is computed, and weights are changed a step along the
gradient. After the input variables and target patterns are shown
several times and weights changed this way, the weights converge to
values such that the network generates the correct output values
for each input variable. The network may also compute the correct
outputs for new examples by nonlinearly interpolating between the
examples in the training set of input variables.
[0107] In an exemplary embodiment, the disclosure is directed to a
computer-implemented method for adoptively supporting medical
decisions of one or more users. The method includes receiving data
and predicting one or more medical decisions based on the data.
[0108] Data may be received via a wireless communication component,
such as infrared signals, radio signals, and pulse codes. The data
may be received from the immediate user, from a user who is not the
immediate user, from information computers on which data are
stored, from medical devices, and from network ports. The method
also includes displaying the predicted medical decision(s).
[0109] The method also includes receiving one or more
user-decisions. Each user-decision may be a predicted medical
decision or may not be a predicted medical decision. The method
also includes learning to predict the at least one user-decision
from the data received. Learning may include updating one or more
learning modules chosen from a group consisting of behavioral
models, rule-based algorithms, learning-based algorithms, or neural
networks. Learning may further include customizing operations to at
least one parameter, such as preferences of a user, habits of a
user, medical specialties of a user, patient populations of a user,
preferences of a group of users, habits of a group of users,
medical specialties of a group of users, and patient populations of
a group of users.
[0110] The method may also include, after the step of receiving
user-decisions, executing the user-decisions. The method may also
include automatically executing the predicted medical decisions,
before user-decisions are received. Executing a decision may
include changing the state of a computation or process or
communicating with an entity external to the system in some manner
such as storing the decision, altering a computer display, updating
a diagnosis or finding, ordering a medication, ordering a
laboratory test, ordering an imaging test, ordering a consultation,
retrieving information, displaying an article, changing the control
path of a task, asking the user a question, sending information to
a user, controlling a medical device, and the like. In an
embodiment, the method may provide a "virtual specialist" to a
user, by providing information pertaining to at least one medical
specialty to the at least one user. The method may also include
displaying an electronic medical chart graphical user
interface.
[0111] In an exemplary embodiment, the method may be implemented on
at least one portable computing device. Alternatively, the method
may be implemented on a host computer that receives data
transmitted from one or more portable computing devices, which also
receive and display output from the host computer.
[0112] An embodiment includes receiving data and transmitting the
data to one or more neural networks. One or more medical decisions
may be predicted by neural networks, and the predicted medical
decisions are displayed. One or more user-decisions are received
from the user or users. Each user-decision may be a predicted
medical decision or may not be a predicted medical decision. The
method also includes learning to predict the user-decisions from
the data received, by updating the neural networks.
[0113] Another embodiment is directed to an example in which the
learning is based on the decisions of one or more first users who
are not the immediate user or users. This embodiment includes
receiving one or more first quantities of data and one or more
user-decisions from one or more first users. The embodiment
includes learning to predict the user-decisions from the first
quantities of data received. The embodiment next includes receiving
one or more second quantities of data, predicting one or more
medical decisions, and displaying the predicted medical decisions.
The embodiment includes receiving one or more second
user-decisions, but not learning from them.
[0114] Another embodiment is directed to using rule-based
algorithms to make predictions while learning develops. This
embodiment includes receiving a first quantity of data and using at
least one rule-based algorithm to predict one or more first medical
decisions. These first medical decisions are displayed. The
embodiment includes receiving one or more user-decisions from one
or more first users. The method may include executing the
user-decisions, after they are received. The embodiment includes
learning to predict the user-decisions from the data received. The
embodiment may also include executing the first predicted medical
decision, before receiving the user-decisions. The embodiment also
includes receiving a second quantity of data and using one or more
learning-based algorithms to predict one or more second medical
decisions. One or more third medical decisions are also predicted
by the one or more rule-based algorithms. The method involves
displaying the second predicted medical decisions, or the third
predicted medical decisions, or both. Which decisions are displayed
may be selected automatically by a computing device or by one or
more users. The embodiment may also include automatically executing
either the second or third predicted medical decisions, or
both.
[0115] Users may include different classes of users such as medical
doctors, nurses, nurse practitioners, residents, medical students,
medical staff, administrative staff, technicians, patients, payers,
pharmacy benefits managers, insurance companies, and consultants.
In an embodiment of the method, decisions are predicted for a first
user or group of users, via the predictive model of a second user
or group of users who may be of a different class than the first
user.
[0116] The disclosure is also directed to a software program,
embodied on a computer-readable medium, incorporating the exemplary
method.
[0117] The disclosure is also directed to a computer-based system
for adaptively supporting medical decisions of one or more users.
The system includes component for receiving data; component for
predicting medical decisions; component for receiving at least one
user-decision; display component; and component for learning to
predict the at least one user-decision, from the data received. The
system may include one or more portable computing devices, or it
may include both a host computer and one or more portable computing
devices. The portable computing devices may be linked or integrated
with a medical instrument. Each computing device may communicate
with the host computer via a wireless communication component
consisting of radio signals, infrared signals, or pulse codes. The
component for learning may include one or more learning modules
selected from a group consisting of at least one behavioral model,
at least one rule-based algorithm, at least one learning-based
algorithm, and at least one neural network.
[0118] In a particular embodiment, a computer-implemented method
for adaptively supporting medical decisions of at least one user
includes receiving a first input from a first device, receiving a
second input from a second device, determining a suggested medical
decision based at least in part on the first input and the second
input, and transferring the suggested medical decision to the
second device. In an example, the first device is an automated
patient monitoring device, a patient input device or a nurse input
device. In another example, the second device is a physician input
device. The method may also include receiving a medical decision
input from the second device based at least in part on the
suggested medical decision. The suggested medical decision may
include, for example, a diagnosis, an order, or a prescription. In
a further example, transferring includes transferring a user
interface including pre-selected interface controls. The first and
second devices may be wireless interface devices.
[0119] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving a medical input from a user device,
determining a first suggested decision from a first predictive
model, determining a second suggested decision from a second
predictive model, and transferring the first suggested decision and
the second suggested decision to the user device. In an example,
the first predictive model is trained based at least in part on
input from a group of users. In another example, the first
predictive model is associated with a medical specialty. In a
further example, the second predictive model is trained based at
least in part on inputs received from a particular user of the user
device. The user device may be a wireless user interface
device.
[0120] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving a user medical input and a user medical
decision from a device, determining a suggested result based at
least in part on the user medical input, comparing the user medical
decision to the suggested result, and providing an indication based
at least in part on comparing the user medical decision to the
suggested result. In an example, the user decision may include a
coding decision. The suggested result may include a suggested
coding decision and providing the indication may include providing
a coding alert. In another example, determining the suggested
result includes accessing a model. The model may be associated with
inputs of a group of users or may be associated with a payer, such
as an insurance company or a government entity. In a further
example, the model may be associated with instructive practices,
such as practices of a physician overseeing medical students.
[0121] In an additional embodiment, a computer-implemented method
for adaptively supporting decisions of at least one user includes
receiving a set of user inputs, training a model based at least in
part on the set of user inputs, and adjusting the model based at
least in part on a set of outlier inputs. In an example, the set of
user inputs includes a set of findings and a diagnosis based at
least in part on the medical findings. The set of outlier inputs
may represent a set of unusual diagnoses. In another example,
receiving the set of user inputs includes receiving user inputs
resulting from consultation with patients. In a further example,
adjusting the model includes periodically introducing the set of
outlier inputs for training the model.
[0122] In an exemplary embodiment, a computer-implemented method
for adaptively supporting medical decisions of at least one user
includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on a wireless portable
interface device. The method also includes receiving data at a host
computer from a second electronic medical record interface
associated with a second medical workflow. The second electronic
medical record may be implemented on a second interface device. In
addition, the method may include predicting at least one medical
decision based on the received data, displaying the at least one
predicted medical decision via the first electronic medical record
interface, and receiving at least one user-decision from the at
least one user via the first electronic medical record interface.
In an example, each user decision may be a predicted medical
decision or may not be a predicted medical decision. The method may
also include learning to predict the at least one user decision
from the data received. In another example, the second electronic
medical record interface is an interface that affords log-in by a
patient. In a further example, the medical data received by the
host computer from the second interface device travels via a
network path that includes the Internet. In an additional example
the second electronic medical record interface is an interface that
affords log-in by a nurse.
[0123] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on a wireless portable
interface device. The method also includes receiving medical data
at a host computer from a remote computer, predicting at least one
medical decision based on the received data, displaying the at
least one predicted medical decision via the first electronic
medical record interface, and receiving at least one user-decision
from the at least one user via the first electronic medical record
interface. In an example, each user decision may be a predicted
medical decision. The method may also include learning to predict
the at least one user decision from the data received. In a further
example, the medical data from a remote computer includes medical
test result data. In another example, the medical data from a
remote computer includes recommended diagnostic questions or tests.
Further, the medical data from a remote computer may include
treatment orders or medical information about a patient.
[0124] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on a wireless portable
interface device. The method may also include predicting at least
one medical decision at the host computer based on the received
data, displaying the at least one predicted medical decision in the
electronic medical record interface implemented on the wireless
portable interface device, and receiving at least one user-decision
from the at least one user via the electronic medical record
interface. Displaying the at least one predicted medical decision
may include displaying a selectable item initialized according to
at least one predicted medical decision. In an example, the method
may include learning to predict the at least one user-decision
using the host computer based on the received data and the at least
one user-decision. In another example, a selectable item of the
electronic medical record interface may be initialized to the
selected state. In a further example, the method may include
receiving a deselect input and the selectable item transitions to
the deselected state. In an additional example, the selectable item
may indicate a medication order and the predicted medical decision
includes at least one prescribing parameter, and the selectable
item is initialized with the at least one predicted prescribing
parameter.
[0125] In an additional embodiment, a computer-implemented method
for adaptively supporting medical decisions of at least one user
includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on a wireless portable
interface device. The method may also include predicting at least
one medical decision at the host computer based on the received
data, displaying the at least one predicted medical decision in the
electronic medical record interface implemented on the wireless
portable interface device, receiving at least one user-decision
from the at least one user via the electronic medical record
interface, and learning to predict the at least one user-decision
using the host computer based on the received data and the at least
one user-decision. Predicting the at least one medical decision may
include combining a first set of predictions that do not depend on
past decisions by the user and a second set of predictions that
depend on past decisions by the user.
[0126] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on a wireless portable
interface device. The method may also include predicting at least
one medical coding decision at the host computer based on the
received data, displaying the at least one predicted medical coding
decision in the electronic medical record interface implemented on
the wireless portable interface device, and receiving at least one
user-decision from the at least one user via the electronic medical
record interface. In an example, the method may further includes
receiving data at a host computer from an electronic medical record
interface associated with a medical workflow, receiving at least
one user-decision from at least one additional user via the
electronic medical record interface on at least one additional
interface device, and learning to predict the at least one
user-decision using the host computer based on the received data
from the first portable interface device, the received data from at
least one additional interface device, the at least one
user-decision, and at least one user decision from at least one
additional interface device. In an example, the method also
includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow. The
electronic medical record may be implemented on at least one
additional interface device. In another example, the method may
include receiving at least one user-decision from at least one
additional user via the electronic medical record interface on at
least one additional interface device and learning to predict the
at least one user-decision using the host computer based on the
received data from at least one additional interface device and at
least one user decision from at least one additional interface
device. In a further example, the method may include displaying a
comparison of the at least one user-decision and the predicted
decision. In another example, the method may include storing past
user-decisions and displaying a comparison of past user-decisions
with predicted decisions. In a further example, the method may
include notifying a user whose past user-decisions comprise higher
codes than predicted decisions. In an additional example, the
method may include notifying a user whose past user-decisions
comprise lower codes than predicted decisions. In another
embodiment, the method may include receiving data at a host
computer from an electronic medical record interface associated
with a medical workflow, receiving at least one user-decision from
at least one additional user via the electronic medical record
interface on at least one additional interface device, storing past
user-decisions from the first user and from at least one additional
user, and simultaneously displaying information relating to past
user decisions for at least two users. Further, displaying the at
least one predicted medical coding decision may include displaying
at least one additional diagnostic finding that may be selected or
test that may be ordered.
[0127] In a further embodiment, a computer-implemented method for
adaptively supporting medical decisions of at least one user
includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow,
predicting at least one medical decision at the host computer based
on the received data and one of at least two predictive models,
displaying the at least one predicted medical decision in the
electronic medical record interface implemented on the wireless
portable interface device, and receiving at least one user-decision
from the at least one user via the electronic medical record
interface. In an example, the interface device affords selection of
a patient and that a first predictive model is used to predict
medical decisions regarding a first patient and a second predictive
model is used to predict medical decisions regarding a second
patient. In another example, the at least two predictive models are
stored on the host computer and the predicting of at least one
medical decision is done at the host computer. In a further
example, the method includes receiving at least one predictive
model from a first remote computer, receiving a second predictive
model from a second remote computer and storing the received
predictive models on a host computer.
[0128] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow,
storing at at least one prediction computer at least one predictive
model, transmitting medical data from a host computer to a
prediction computer, receiving medical data at a host computer,
predicting at least one medical decision at the prediction computer
based on the received medical data and one of at least one
predictive model, transmitting at least one prediction from the
prediction computer to the host computer, displaying the at least
one predicted medical decision in the electronic medical record
interface implemented on the wireless portable interface device,
and receiving at least one user-decision from the at least one user
via the electronic medical record interface. In an example,
predicting includes predicting at least one medical decision based
on the received data and one of at least two predictive models. In
another example, transmitting data from a host computer to a
prediction computer includes selecting a prediction computer from a
set of at least two prediction computers. In addition, a first
prediction computer stores a first prediction model and a second
prediction computer stores a second prediction model.
[0129] In an additional embodiment, a computer-implemented method
for evaluating medical decisions of at least one user includes
receiving data at a host computer from an electronic medical record
interface associated with a medical workflow, predicting at least
one medical decision at the host computer based on the received
data, displaying the at least one predicted medical decision in the
electronic medical record interface implemented on the wireless
portable interface device, receiving at least one user-decision
from the at least one user via the electronic medical record
interface, and storing past user-decisions and displaying a
comparison of past user-decisions with predicted decisions. In an
example, the method further includes displaying a notification that
a predicted action was omitted in at least one instance. In another
example, the predicted action is one of the following types of
action: to refer the patient to a disease management program, to
prescribe a specific medication for the patient, to prescribe a
medication from a specific class of medications for the patient. In
a further example, the notification is displayed on a second
interface device to a second user.
[0130] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user including receiving data at a host computer from an electronic
medical record interface associated with a medical workflow,
predicting at least one medical decision at the host computer based
on the received data, displaying the at least one predicted medical
decision in the electronic medical record interface implemented on
the wireless portable interface device, receiving at least one
user-decision from the at least one user via the electronic medical
record interface; and learning to predict the at least one
user-decision using the host computer based on the received data
and the at least one user-decision. In an example, the host
computer function and the interface device function reside on the
same computer device. In another example, the predicted medical
decision is to select a diagnosis. In a further example, the
predicted medical decision is to order a medication. In an
additional example, the predicted medical decision is to order a
test. In another example, the predicted medical decision is to
order a procedure.
[0131] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow,
predicting at least one medical decision at the host computer based
on the received data, displaying the at least one predicted medical
decision in the electronic medical record interface implemented on
the wireless portable interface device, receiving at least one
user-decision from the at least one user via the electronic medical
record interface, learning to predict the at least one
user-decision using the host computer based on the received data
and the at least one user-decision, and transmitting at least one
user decision to a remote computer via a network. In an example,
the user decision is transmitted to a remote computer via a network
path that comprises a wireless network. In another example, the
user decision is transmitted to a remote computer via a network
path that comprises the Internet.
[0132] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving at least one first quantity of computer
readable data associated with a medical workflow, receiving at
least one user-decision associated with the medical workflow via an
electronic medical records interface, learning to predict the at
least one received user-decision based on the at least one first
quantity of computer readable data and the at least one
user-decision by adapting a computer implemented prediction model,
receiving at least one second quantity of computer readable data
associated with the medical workflow, predicting at least one
medical decision based on the at least one second quantity of
computer readable data using the computer implemented prediction
model, the at least one medical decision being associated with the
medical workflow, displaying the at least one predicted medical
decision via the electronic medical records interface, receiving at
least one second user-decision associated with the medical workflow
via the electronic medical records interface, and storing at least
one user-decision in at least one computer readable medium. In an
example, at least one user decision is to record a medical finding
about the condition of a patient. In another example, at least one
user decision is to issue an order regarding patient treatment. In
a further example, the method includes a first user logging into
the system using a first identity, a second user logging into the
system using a second identity, the system receiving the first at
least one user decision from a logical session associated with the
first user, and the system receiving the second at least one user
decision from a logical session associated with the second user. In
an additional example, the method further includes a first
plurality of login sessions associated with a first plurality of
user identities, a second login session associated with a user
identity, the system receiving the first at least one user decision
from the first plurality of login sessions, and the system
receiving the second at least one user decision from a logical
session associated with the second user login session.
[0133] In an exemplary embodiment, a computer-implemented method
for adaptively supporting medical decisions includes receiving a
first quantity of computer readable data associated with a medical
workflow, predicting a first at least one medical decision
associated with the medical workflow based on the computer readable
data, via at least one prediction algorithm, displaying the first
at least one medical decision in an electronic medical interface,
receiving at least one user-decision associated with the medical
workflow from a first at least one user via the electronic medical
interface, learning to predict the at least one user-decision based
on the at least one user-decisions and the computer readable data
wherein learning to predict the at least one user-decisions
includes adapting the at least one prediction algorithm, receiving
a second quantity of computer readable data associated with the
medical workflow via the electronic medical interface, predicting
via at least one learning-based algorithm a second at least one
medical decision associated with the medical workflow based on the
second quantity of computer readable data, and storing at least one
user-decision in at least one computer readable medium.
[0134] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a host computer from an electronic
medical record interface associated with a medical workflow wherein
the electronic medical record is implemented on a wireless portable
interface device, predicting at least one medical decision at the
host computer based on the received data, displaying the at least
one predicted medical decision in the electronic medical record
interface implemented on the wireless portable interface device,
receiving at least one user-decision from the at least one user via
the electronic medical record interface, receiving a plurality of
training sets, each of data and at least one user-decision from a
remote computer, and learning to predict the at least one
user-decision using the host computer based on the training sets,
received data, and the at least one user-decision. In an example,
the training sets include at least one example of at least one rare
medical decision.
[0135] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving at least one first quantity of computer
readable data associated with a medical workflow, receiving at
least one user-decision associated with the medical workflow via an
electronic medical records interface, learning to predict the at
least one first quantity of computer readable data based on at
least one user-decision by adapting a computer implemented
prediction model, receiving at least one second quantity of
computer readable data associated with the medical workflow,
receiving at least one second user-decision associated with the
medical workflow via the electronic medical records interface,
predicting a third quantity of computer readable data associated
with the medical workflow based on the second user-decision, and
comparing the predicted third quantity of computer readable data to
the received second quantity of computer readable data. In an
example, the method includes displaying information relating to the
comparison. In another example, the method further includes
notifying the user of outlier findings present in the second
quantity of data but not predicted by the prediction algorithm.
[0136] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes providing an electronic medical record interface
affording log-in by a first group of users, receiving medical data
regarding each patient in a plurality of patients at a host
computer, receiving at least one user decision regarding each
patient in the plurality of patients from at least one user of the
first group of users via an electronic medical record interface,
generating a predictive model that predicts one or more user
decisions from data regarding a patient based on the received
medical data and the received at least one user decision, providing
an electronic medical record interface affording log-in by a second
group of users, receiving medical data regarding a patient, using
the predictive model to predict a decision from the medical data
regarding a patient, displaying this prediction via an electronic
medical record interface via a log-in session associated with a
user from the second group of users. In an example, the method
includes receiving a user-decision via an electronic medical record
interface via a log-in session associated with a user from the
second group of users.
[0137] In a further embodiment, a computer-implemented method for
adaptively supporting medical decisions of at least one user
includes providing an electronic medical record interface affording
log-in by a first group of users, receiving medical data regarding
each patient in a plurality of patients at a host computer,
receiving at least one user decision regarding each patient in the
plurality of patients from at least one user of the first group of
users via an electronic medical record interface, generating a
predictive model that predicts one or more user decisions from data
regarding a patient based on the received medical data and the
received at least one user decision, providing an electronic
medical record interface affording log-in by a second group of
users, receiving medical data regarding a patient, using the
predictive model to predict a decision from the medical data
regarding a patient, receiving a user-decision via an electronic
medical record interface via a log-in session associated with a
user from the second group of users, and displaying information
comparing the received user-decision and the predicted decision. In
an example, displaying information comparing the received
user-decision and the predicted decision includes alerting the user
if the decisions differ.
[0138] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes providing an electronic medical record interface
affording log-in by a first group of users, receiving medical data
regarding at least one patient from at least one user of the first
group of users via an electronic medical record interface,
providing an electronic medical record interface affording log-in
by a second group of users, receiving medical data regarding a
patient from a second at least one user of the second group of
users via an electronic medical record interface, predicting at
least one user decision based on the medical data entered by the
first at least one user and on the medical data entered by the
second at least one user, and displaying the predicted decision via
an electronic medical record interface via a log-in session
associated with a user from the second group of users. In an
example, the method also includes receiving a user-decision via an
electronic medical record interface via a log-in session associated
with a user from the second group of users. In another example, the
method also includes adapting the prediction means based on the
received user decision.
[0139] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving data at a prediction computer from an
electronic medical record interface associated with a medical
workflow wherein the electronic medical record implemented on an
interface device, predicting at least one medical decision at the
prediction computer based on the received data, displaying the at
least one predicted medical decision in the electronic medical
record interface implemented on the interface device, receiving at
least one user-decision from the at least one user via the
electronic medical record interface, and learning to predict the at
least one user-decision using the prediction computer based on the
received data and the at least one user-decision. In an example,
receiving data further includes receiving data via a network
communication means. In another example, executing the at least one
user-decision, after the step of receiving 1the at least one
user-decision. In a further example, the method also includes
executing the at least one predicted medical decision, before the
step of receiving the at least one user-decision.
[0140] In another exemplary embodiment, a computer-implemented
method for adaptively supporting medical decisions of at least one
user includes receiving at least one first quantity of computer
readable data associated with a medical workflow, receiving at
least one user-decision associated with the medical workflow from a
first at least one user via an electronic medical records
interface, learning to predict the at least one received
user-decision based on the at least one first quantity of computer
readable data and the at least one user-decision by adapting a
computer implemented prediction model, receiving at least one
second quantity of computer readable data associated with the
medical workflow, predicting at least one medical decision based on
the at least one second quantity of computer readable data using
the computer implemented prediction model, the at least one medical
decision being associated with the medical workflow, displaying the
at least one predicted medical decision via the electronic medical
records interface, and receiving at least one second user-decision
associated with the medical workflow via the electronic medical
records interface. In an example, receiving the at least one second
quantity of computer readable data further includes receiving data
via a network communication method. In an example, the method is
implemented on at least one portable computing device. In another
example, receiving and predicting are implemented on at least one
host computer, the host computer receives data from at least one
portable computing device, and the at least one portable computing
device receives and displays output from the host computer. In a
further example, executing the at least one user-decision, after
the step of receiving the at least one user-decision. In another
example, the method further includes automatically executing the at
least one predicted medical decision, before the step of receiving
the at least one user-decision. In addition, the first at least one
user may include a specialist in a field of medicine or a billing
specialist or a coding specialist. In another example, the second
at least one decision is received from a second at least one user
with a log-in identity to the electronic medical records interface
that is distinct from the log-in identity of the first at least one
user. In a further example, the displayed at least one medical
decision is displayed in a different language than the first
received user decision. In another example, displaying the at least
one medical decision comprises displaying information relating to
prescriptions issued by another user. In a further example,
displaying the at least one medical decision comprises displaying a
warning that a prescription being issued conflicts with another
prescription issued. In an additional example, displaying the at
least one medical decision includes displaying a warning that a
prescription being issued is redundant with another prescription
issued by a different user.
[0141] In an additional embodiment, a computer-implemented method
for adaptively supporting medical decisions includes receiving a
first quantity of computer readable data associated with a medical
workflow, predicting a first at least one medical decision
associated with the medical workflow based on the computer readable
data, via at least one prediction algorithm, displaying the first
at least one medical decision in an electronic medical interface,
receiving at least one user-decision associated with the medical
workflow from a first at least one user via the electronic medical
interface, learning to predict the at least one user-decision based
on the at least one user-decisions and the computer readable data,
wherein learning to predict the at least one user-decisions
includes adapting via the at least one learning algorithm,
receiving a second quantity of computer readable data associated
with the medical workflow via the electronic medical interface, and
predicting via at least one learning-based algorithm a second at
least one medical decision associated with the medical workflow
based on the second quantity of computer readable data. In an
example, the method also includes displaying the second at least
one medical decision. In a further example, receiving the second
quantity of computer readable data further includes receiving the
second quantity of computer readable data via a network
communication method. In an additional example, receiving a first
quantity of data, predicting a decision, and learning to predict
are implemented on a host computer, the host computer receives data
from at least one portable computing device, and the at least one
portable computing device receives and displays output from the
host computer. In another example, the method includes executing
the first at least one medical decision, before receiving the at
least one user-decision. In a further example, the method includes
receiving a second at least one user-decision, after predicting the
second at least one medical decision. In an example, the method
includes executing the second at least one user-decision after
receiving the second at least one user-decision. In a further
example, learning further includes updating at least one learning
module chosen from a group consisting of behavioral models,
rule-based algorithms, learning-based algorithms, and neural
networks. In another example, learning further includes customizing
a plurality of operations to at least one parameter chosen from a
group consisting of preferences of a user, habits of a user,
medical specialties of a user, patient populations of a user,
preferences of a group of users, habits of a group of users,
medical specialties of a group of users, and patient populations of
a group of users. In a further example, the method further includes
predicting via the at least one rule-based algorithm a third at
least one medical decision and displaying at least one predicted
medical decision chosen from a group consisting of the second at
least one medical decision, the third at least one medical
decision, and both the second and third at least one medical
decisions. In another example, the method includes executing the
predicted medical decision chosen from the group consisting of the
second at least one medical decision, the third at least one
medical decision, and both the second and third at least one
medical decision. In a further example, the predicted medical
decision chosen from the group consisting of the second at least
one medical decision, the third at least one medical decision is
selected by at least one user, and both the second and third at
least one medical decisions, is selected by at least one user. In
an example, the predicted medical decision chosen from the group
consisting of the second at least one medical decision, the third
at least one medical decision, and both the second and third at
least one medical decision, is selected by a computing device. In a
further example, the method includes receiving a second at least
one user-decision, after predicting the third at least one medical
decision. In another example, the method includes executing the
second at least one user-decision after receiving the second at
least one user-decision. In an example, the method further includes
learning to predict the second user-decision from the second
quantity of data received. In a further example, learning further
includes updating at least one learning module chosen from a group
consisting of behavioral models, rule-based algorithms,
learning-based algorithms, and neural networks. In another example,
learning further includes customizing a plurality of operations to
at least one parameter chosen from a group consisting of
preferences of a user, habits of a user, medical specialties of a
user, patient populations of a user, preferences of a group of
users, habits of a group of users, medical specialties of a group
of users, and patient populations of a group of users. In an
example, the method further includes displaying an electronic
medical chart user interface. In another example, the first at
least one user is a specialist in a field of medicine or a billing
specialist or a coding specialist.
[0142] In a further exemplary embodiment, a computer-implemented
method for adaptively supporting decisions of at least one user
includes receiving at least one first quantity of computer readable
data associated with a workflow in which one or more users record
information about a subject and make and act on decisions,
receiving at least one user-decision associated with the workflow
from a first at least one user via an electronic records interface,
learning to predict the at least one received user-decision based
on the at least one first quantity of computer readable data and
the at least one user-decision by adapting a computer implemented
prediction model, receiving at least one second quantity of
computer readable data associated with the workflow, predicting at
least one decision based on the at least one second quantity of
computer readable data using the computer implemented prediction
model, the at least one decision being associated with the
workflow, displaying the at least one predicted decision via the
electronic records interface, and receiving at least one second
user-decision associated with the workflow via the electronic
records interface. In an example, the workflow is an auto repair
workflow, a law-enforcement workflow, an emergency-response
workflow, a customer service workflow, or a computer repair
workflow.
[0143] Using the foregoing, systems and methods may be implemented
using standard programming or engineering techniques including
computer programming software, firmware, hardware or any
combination or subset thereof Any such resulting program, having a
computer readable program code component, may be embodied or
provided within one or more computer readable or usable media,
thereby making a computer program product, i.e. an article of
manufacture, according to the invention. The computer readable
media may be, for example, a fixed (hard) drive, disk, diskette,
optical disk, magnetic tape, semiconductor memory such as read-only
memory (ROM), or a transmitting/receiving medium, such as the
Internet or other communication network or link. The article of
manufacture including the computer programming code may be made
and/or used by executing the code directly from one medium, by
copying the code from one medium to another medium, or by
transmitting the code over a network.
[0144] An apparatus for making, using or selling the invention may
be one or more processing systems including, but not limited to, a
central processing unit (CPU), memory, storage devices,
communication links, communication devices, server, I/O devices, or
any sub-components or individual parts of one or more processing
systems, including software, firmware, hardware or any combination
or subset thereof, which embody the systems and methods as set
forth in the claims.
[0145] User input may be received from the keyboard, mouse, pen,
voice, touch screen, or any other component by which a human can
input data to a computer, including through other programs such as
application programs.
[0146] It may be apparent that the methods described here in the
context of a medical workflow may be equally applicable in any
context in which one or more users record information about a
subject and make and act on decisions that are based on information
learned about a subject. Examples of such workflows include auto
repair, law enforcement, emergency response, customer service, and
computer repair.
[0147] In accordance with various embodiments, the methods
described herein may be implemented as one or more software
programs running on a computer processor. Dedicated hardware
implementations including, but not limited to, application specific
integrated circuits, programmable logic arrays and other hardware
devices can likewise be constructed to implement the methods
described herein. Further, alternative software implementations
including, but not limited to, distributed processing or
component/object distributed processing, parallel processing, or
virtual machine processing can also be constructed to implement
methods described herein.
[0148] It should also be noted that software that implements the
disclosed methods may optionally be stored on a tangible storage
medium, such as: a magnetic medium, such as a disk or tape; a
magneto-optical or optical medium, such as a disk; or a solid state
medium, such as a memory card or other package that houses one or
more read-only (non-volatile) memories, random access memories, or
other re-writable (volatile) memories. The software may also
utilize a signal containing computer instructions. A digital file
attachment to e-mail or other self-contained information archive or
set of archives is considered a distribution medium equivalent to a
tangible storage medium. Accordingly, the disclosure is considered
to include a tangible storage medium or distribution medium as
listed herein, and other equivalents and successor media, in which
the software implementations herein may be stored.
[0149] Although the present specification describes components and
functions that may be implemented in particular embodiments with
reference to particular standards and protocols, the invention is
not limited to such standards and protocols. For example, wireless
communication protocols, such as IEEE 802.11, IEEE 802.15, and IEEE
802.16, represent examples of the state of the art. Such standards
are periodically superseded by faster or more efficient equivalents
having essentially the same functions. Accordingly, replacement
standards and protocols having the same or similar functions as
those disclosed herein are considered equivalents thereof.
[0150] One or more embodiments of the disclosure may be referred to
herein, individually and/or collectively, by the term "invention"
merely for convenience and without intending to voluntarily limit
the scope of this application to any particular invention or
inventive concept. Moreover, although specific embodiments have
been illustrated and described herein, it should be appreciated
that any subsequent arrangement designed to achieve the same or
similar purpose may be substituted for the specific embodiments
shown. This disclosure is intended to cover any and all subsequent
adaptations or variations of various embodiments. Combinations of
the above embodiments, and other embodiments not specifically
described herein, will be apparent to those of skill in the art
upon reviewing the description.
[0151] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn.1.72(b) and is submitted with the understanding that
it will not be used to interpret or limit the scope or meaning of
the claims. In addition, in the foregoing Detailed Description,
various features may be grouped together or described in a single
embodiment for the purpose of streamlining the disclosure. This
disclosure is not to be interpreted as reflecting an intention that
the claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter may be directed to less than all of the
features of any of the disclosed embodiments. Thus, the following
claims are incorporated into the Detailed Description, with each
claim standing on its own as defining separately claimed subject
matter.
[0152] The descriptions of FIGS. 1-12 are provided for illustrative
purposes only, and are not meant to be limiting to the present
invention. Although the present invention has been described in
detail with reference to certain embodiments, it should be apparent
that modifications and adaptations to those embodiments may occur
to persons skilled in the art without departing from the spirit and
scope of the present invention as set forth in the following
claims.
* * * * *