U.S. patent application number 13/800421 was filed with the patent office on 2013-08-01 for systems and methods for referring physicians based on hierarchical disease profile matching.
This patent application is currently assigned to GENERAL ELECTRIC COMPANY. The applicant listed for this patent is GENERAL ELECTRIC COMPANY. Invention is credited to Matthew Cardoso, Steven Matt Gustafson.
Application Number | 20130197926 13/800421 |
Document ID | / |
Family ID | 46381570 |
Filed Date | 2013-08-01 |
United States Patent
Application |
20130197926 |
Kind Code |
A1 |
Gustafson; Steven Matt ; et
al. |
August 1, 2013 |
SYSTEMS AND METHODS FOR REFERRING PHYSICIANS BASED ON HIERARCHICAL
DISEASE PROFILE MATCHING
Abstract
Systems, apparatus, and methods for referring physicians based
on hierarchical disease profile matching are disclosed. An example
system includes a data store to include a plurality of disease
profiles, each disease profile associated with a patient condition,
a user interface to accept a user request for a referral of a
patient to a physician, and a referral processor to compare a
profile associated with the patient including a patient symptom to
the plurality of disease profiles to generate one or more physician
recommendations for referral, the referral processor to refine the
one or more physician recommendations based on one or more
characteristics associated with each of the one or more physician
recommendations, the referral processor to provide the refined one
or more physician recommendations to a user for review and
selection via the user interface.
Inventors: |
Gustafson; Steven Matt;
(Niskayuna, NY) ; Cardoso; Matthew; (Boston,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GENERAL ELECTRIC COMPANY; |
SCHENECTADY |
NY |
US |
|
|
Assignee: |
GENERAL ELECTRIC COMPANY
SCHENECTADY
NY
|
Family ID: |
46381570 |
Appl. No.: |
13/800421 |
Filed: |
March 13, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12982365 |
Dec 30, 2010 |
|
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13800421 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16H 10/60 20180101;
G16H 70/60 20180101; G06Q 30/0631 20130101 |
Class at
Publication: |
705/2 |
International
Class: |
G06Q 50/22 20060101
G06Q050/22 |
Claims
1. A physician referral system comprising: a data store to include
a plurality of disease profiles, each disease profile associated
with a patient condition and a hierarchical categorization of
physicians in a healthcare organization, the hierarchical
categorization of physicians including one or more characteristics
associated with each respective physician; a user interface to
accept a user request for a referral of a patient to a physician in
the healthcare organization; and a referral processor to: match the
patient to one of the plurality of disease profiles based on one or
more symptoms associated with the patient; if a match is made,
reduce a pool of possible physicians for referral based on the
matching disease profile in comparison with the hierarchical
categorization of physicians; if no match is made, reduce the pool
of possible physicians for referral based on one or more general
categories associated with the one or more symptoms associated with
the patient in comparison with the hierarchical categorization of
physicians; further reduce the pool of possible physicians for
referral based on the one or more characteristics associated with
each physician in the pool of possible physicians; and provide a
recommendation list of one or more possible physicians for referral
based on an output of the reductions.
2. The system of claim 1, wherein the one or more characteristics
associated with each physician are to be considered based on one or
more options selected by the user.
3. The system of claim 1, wherein the referral processor is to
enable a referral of the patient to a physician on the
recommendation list by the user with assistance via the user
interface.
4. The system of claim 1, wherein the one or more characteristics
associated with each physician include past physician outcomes,
physician cost of care delivery, physician efficiency, and
physician geographic location.
5. The system of claim 1, wherein one or more patient
characteristics including one or more of the patient's ability to
travel and the patient's ability to wait for treatment are to be
used to further reduce the pool of possible physicians for
referral.
6. The system of claim 1, wherein the one or more options selected
by the user are used to at least one of select or prioritize one of
more of the characteristics associated with each physician.
7. The system of claim 1, wherein the referral processor is to
further refine the recommendation list based on clinical best
practices retrieved from one or more knowledge bases.
8. A computer-implemented method of generating physician referral
recommendations for a patient, the method comprising: accepting a
user request for a referral of a patient to a physician in a
healthcare organization, physicians in the healthcare organization
organized according to a hierarchical categorization of physicians
in the healthcare organization, the hierarchical categorization of
physicians including one or more characteristics associated with
each respective physician; matching, using a processor, the patient
to one of a plurality of disease profiles based on one or more
symptoms associated with the patient; if a match is made, reducing,
using the processor, a pool of possible physicians for referral
based on the matching disease profile in comparison with the
hierarchical categorization of physicians; if no match is made,
reducing, using the processor, the pool of possible physicians for
referral based on one or more general categories associated with
the one or more symptoms associated with the patient in comparison
with the hierarchical categorization of physicians; further
reducing the pool of possible physicians for referral based on the
one or more characteristics associated with each physician in the
pool of possible physicians; and providing a recommendation list of
one or more possible physicians for referral based on an output of
the reductions.
9. The method of claim 8, wherein the one or more characteristics
associated with each physician are to be considered based on one or
more options selected by the user.
10. The method of claim 8, further comprising enabling a referral
of the patient to a physician on the recommendation list by the
user with assistance via a user interface.
11. The method of claim 8, wherein the one or more characteristics
associated with each physician include past physician outcomes,
physician cost of care delivery, physician efficiency, and
physician geographic location.
12. The method of claim 8, wherein one or more patient
characteristics including one or more of the patient's ability to
travel and the patient's ability to wait for treatment are to be
used to further reduce the pool of possible physicians for
referral.
13. The method of claim 8, wherein the one or more options selected
by the user are used to at least one of select or prioritize one of
more of the characteristics associated with each physician.
14. The method of claim 8, further comprising further refining the
recommendation list based on clinical best practices retrieved from
one or more knowledge bases.
15. A tangible computer readable storage medium including
executable program instructions which, when executed by a computer
processor, cause the computer to implement a method of generating
physician referral recommendations for a patient, the method
comprising: accepting a user request for a referral of a patient to
a physician in a healthcare organization, physicians in the
healthcare organization organized according to a hierarchical
categorization of physicians in the healthcare organization, the
hierarchical categorization of physicians including one or more
characteristics associated with each respective physician;
matching, using a processor, the patient to one of a plurality of
disease profiles based on one or more symptoms associated with the
patient; if a match is made, reducing, using the processor, a pool
of possible physicians for referral based on the matching disease
profile in comparison with the hierarchical categorization of
physicians; if no match is made, reducing, using the processor, the
pool of possible physicians for referral based on one or more
general categories associated with the one or more symptoms
associated with the patient in comparison with the hierarchical
categorization of physicians; further reducing the pool of possible
physicians for referral based on the one or more characteristics
associated with each physician in the pool of possible physicians;
and providing a recommendation list of one or more possible
physicians for referral based on an output of the reductions.
16. The computer readable storage medium of claim 15, wherein the
one or more characteristics associated with each physician are to
be considered based on one or more options selected by the
user.
17. The computer readable storage medium of claim 15, wherein the
method further comprises enabling a referral of the patient to a
physician on the recommendation list by the user with assistance
via a user interface.
18. The computer readable storage medium of claim 15, wherein the
one or more characteristics associated with each physician include
past physician outcomes, physician cost of care delivery, physician
efficiency, and physician geographic location.
19. The computer readable storage medium of claim 15, wherein one
or more patient characteristics including one or more of the
patient's ability to travel and the patient's ability to wait for
treatment are to be used to further reduce the pool of possible
physicians for referral.
20. The computer readable storage medium of claim 15, wherein the
one or more options selected by the user are used to at least one
of select or prioritize one of more of the characteristics
associated with each physician.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority to, as a
continuation of, U.S. patent application Ser. No. 12/982,365, filed
Dec. 30, 2010, and entitled "SYSTEM AND METHODS FOR REFERRING
PHYSICIANS BASED ON HIERARCHICAL DISEASE PROFILE MATCHING," which
is hereby incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0002] This disclosure relates generally to physician referrals
and, more particularly, to systems and methods for referring
physicians to patients based on profile matching.
BACKGROUND
[0003] Health Management Organizations (HMOs) and other Accountable
Care Organizations have a pool of possible physicians from which to
refer a patient. Physician referral systems enable the best
possible candidate physicians to be automatically and quickly
identified from this pool. A good physician referral system will
make the most of relevant profile data associated with the patient
as well as the physicians. Such data can be divided into different
categories that will enable health services to be delivered
efficiently at low cost.
BRIEF SUMMARY
[0004] Example systems, apparatus, and methods to generate a
recommendation list of physicians to a patient are disclosed. An
example system includes a data store to include a plurality of
disease profiles, each disease profile associated with a patient
condition, a user interface to accept a user request for a referral
of a patient to a physician, a referral processor to compare a
profile associated with the patient including a patient symptom to
the plurality of disease profiles to generate one or more physician
recommendations for referral, the referral processor to refine the
one or more physician recommendations based on one or more
characteristics associated with each of the one or more physician
recommendations, the referral processor to provide the refined one
or more physician recommendations to a user for review and
selection via the user interface.
[0005] An example method includes generating physician referral
recommendations for a patient, the method comprising categorizing
physicians in an organization by department and specialty,
classifying and assembling disease profiles for common diseases,
determining a disease profile for a patient, identifying a group of
one or more categorized physicians matching the disease profile of
the patient, refining the group of one or more categorized
physicians associated with the patient disease profile based on one
or more features associated with each physician in the group,
generating a recommendation of one or more physicians, and
providing the recommendation of one or more physicians to a user
for review and selection.
[0006] An example method comprises selecting a patient, matching
the patient to a patient condition profile, the patient condition
profile associated with a patient health condition, retrieving a
group of one or more physicians based on the patient condition
profile, refining the group of one or more physicians associated
with the patient condition profile based on one or more features
associated with each physician in the group, generating a
recommendation of one or more physicians, and providing the
recommendation of one or more physicians to a user for review and
selection.
[0007] An example apparatus includes a tangible computer readable
storage medium including executable program instructions which,
when executed by a computer processor, cause the computer to
implement a method of generating physician referral recommendations
for a patient. The example method includes generating physician
referral recommendations for a patient, the method comprising
categorizing physicians in an organization by department and
specialty, classifying and assembling disease profiles for common
diseases, determining a disease profile for a patient, identifying
a group of one or more categorized physicians matching the disease
profile of the patient, refining the group of one or more
categorized physicians associated with the patient disease profile
based on one or more features associated with each physician in the
group, generating a recommendation of one or more physicians, and
providing the recommendation of one or more physicians to a user
for review and selection.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 illustrates an exemplary physician referral system in
accordance with an embodiment of the present invention.
[0009] FIG. 2 illustrates a flowchart representative of an example
method of referring physicians to patients based on physician
categorization and disease profiles.
[0010] FIG. 3 illustrates a flowchart representative of an example
method of referring physicians to patients based on matching
physicians to a patient condition profile.
[0011] FIG. 4 is a schematic illustration of an example processor
platform that may be used and/or programmed to implement any or all
of the disclosed examples to refer physicians to patients.
[0012] The foregoing summary, as well as the following detailed
description of certain embodiments of the present invention, will
be better understood when read in conjunction with the appended
drawings. For the purpose of illustrating the invention, certain
embodiments are shown in the drawings. It should be understood,
however, that the present invention is not limited to the
arrangements and instrumentality shown in the attached
drawings.
DETAILED DESCRIPTION
[0013] Although the following discloses example systems and methods
including, among other components, software executed on hardware,
it should be noted that such methods and apparatus are merely
illustrative and should not be considered as limiting. For example,
it is contemplated that any or all of these hardware and software
components could be embodied exclusively in hardware, exclusively
in software, exclusively in firmware, or in any combination of
hardware, software, and/or firmware. Accordingly, while the
following describes example systems and methods, the examples
provided are not the only way to implement such methods and
systems.
[0014] When any of the appended claims are read to cover a purely
software and/or firmware implementation, at least one of the
elements in an at least one example is hereby expressly defined to
include a tangible medium such as a memory, DVD, CD, Blu-ray, etc.
storing the software and/or firmware.
[0015] Currently, the selection of physicians for referrals is made
manually using a variety of heuristics and ad-hoc methods. Since
outcomes and cost features are rarely available in highly
quantitative forms, it is thought that social networks and
scheduling are often the biggest drivers of referrals. However, as
the use of more electronic medical records (EMR) systems is
increasing, health care performance data is becoming more
available. Additionally, as different pay models are likely to be
developed, it will be more important for healthcare organizations
to be able to monitor, control and select physicians based on a
quantitative data. In certain examples, recommendation algorithms,
such as physician recommendation algorithms, allow users with an
efficient, automated way to sort through and analyze available data
to the benefit of users and patients.
[0016] Certain examples provide systems, apparatus, and methods for
"intelligent" physician referral resulting in better care at lower
cost to patient and provider. Cost can be represented as a function
of time to treat a patient, the number of appointments necessary,
the number of imaging required, tests, and post-treatment follow-up
appointments, for example. Physician referral algorithms can be a
differentiating feature for EMR products to help provide
improvement in care outcomes and efficiency, enhancing their value.
Ultimately, these systems and associated methods enable the best
candidate physicians to be automatically identified so that a
patient can more easily make informed decisions such that health
care is delivered more efficiently to the patient based on outcome
and cost.
[0017] Certain examples disclosed herein enable generation of a
recommendation list of physicians from which a patient can choose.
The list is generated by considering data related to the patient
and physician, including performance data for the physicians. Other
factors that are considered by the algorithm include the categories
of treatment the physicians usually engage in, as well as
additional features of the physician and a general patient
condition profile. Patient feedback, as well as expertise, can be
considered when generating and narrowing the list, for example.
Once the patient selects a physician from the list, the physician
can opt to provide care to the patient or not.
[0018] FIG. 1 illustrates an example physician referral system 100.
The example physician referral system 100 of FIG. 1 includes
physician retrieval 110, physician feature selection 120, physician
categorization 130, a referral processor 140, a user interface 150,
a data store of disease profiles 160, and patient condition
matching 170. To maintain a repository of common diseases that an
organization is likely to see, the example physician referral
system 100 contains a data store of disease and/or other patient
condition profiles 160. While a profile in the data store 160 can
include one or more of a disease and/or other patient complaint,
condition, disorder, demographic, etc., the profiles will be
referred to herein as disease profiles. However, it is understood
that, in various examples, the profiles can include more than a
disease recitation or description.
[0019] For example, a pre-diabetic disease profile is created for
patients that are considered to be pre-diabetic based on two
factors such as weight and blood glucose levels, and another
disease profile for pre-diabetic is based on three or more factors
including weight, hypertension and family history. The example data
store of disease profiles 160 aggregates these profiles into a
database. To categorize physicians into areas that they are most
likely to able to treat well, the example physician referral system
100 includes physician categorization 130. Examples of physician
categorization 130 include internal medicine, endocrinology, and/or
cardiology. To assign a patient to a particular disease profile,
the example physician referral system 100 includes patient
condition matching 170. The example patient condition matching 170
enables a patient, when selected, to receive a referral to a
physician, based on matching, within a level of tolerance, the
patient to the closest disease profile within the data store of
disease profiles 160. To retrieve the physician(s) matching the
profile, the example physician referral system 100 includes
physician retrieval 110. Upon patient condition matching 170, the
example physician retrieval 110 is used to reduce the pool of
possible physicians to those who also match the patient's disease
profile. If there is no match, then physician categorization 130 is
further used to select a physician category, going as deep into the
hierarchy of categories as possible based on the symptoms.
[0020] To further refine the list of possible physicians, the
example physician referral system 100 includes physician feature
selection 120. An example physician feature selection 120 examines
other characteristics of the physician such as past performance,
outcome, cost, and efficiency of treating patients. Other
characteristics of physician feature selection 120 include
availability and geographic location. Physician feature selection
120 can also be combined with features of the patient, such as
their willingness to wait or travel. To finally generate the list
of physicians, the example physician referral system 100 includes a
referral processor 140. The example referral processor 140 uses one
or more of the above named factors, in addition to pre-selected
options specified by a user, to generate the recommendation list of
candidate physicians from which to refer the patient.
[0021] To enable the user to interact with the system, the example
physician referral system 100 includes a user interface 150. The
example user interface 150 enables a health care staff member to
provide the list to the patient. If there is no recommendation at
all, then the referral would be made by traditional means.
[0022] As used herein, the term tangible computer-readable medium
is expressly defined to include any type of computer-readable
medium and to expressly exclude propagating signals. Example
computer-readable medium include, but are not limited to, a
volatile and/or non-volatile memory, a volatile and/or non-volatile
memory device, a compact disc (CD), a digital versatile disc (DVD),
a floppy disk, a read-only memory (ROM), a random-access memory
(RAM), a programmable ROM (PROM), an electronically-programmable
ROM (EPROM), an electronically-erasable PROM (EEPROM), an optical
storage disk, an optical storage device, magnetic storage disk, a
magnetic storage device, a cache, and/or any other storage media in
which information is stored for any duration (e.g., for extended
time periods, permanently, brief instances, for temporarily
buffering, and/or for caching of the information) and which can be
accessed by a processor, a computer and/or other machine having a
processor, such as the example processor platform P100 discussed
below in connection with FIG. 4. As used herein, the term
non-transitory computer-readable medium is expressly defined to
include any type of computer-readable medium and to exclude
propagating signals.
[0023] While an example physician referral system 100 is
illustrated in FIG. 1, one or more of the elements, processes
and/or devices illustrated in FIG. 1 may be combined, divided,
re-arranged, omitted, eliminated and/or implemented in any other
way. Further, the example physician retrieval 110, the example
physician feature selection 120, the example physician
categorization 130, the example referral processor 140, the example
user interface 150, the example data store of disease profiles 160,
and/or the example patient condition matching 170 may be
implemented by hardware, software, firmware and/or any combination
of hardware, software and/or firmware. Thus, for example, any of
example physician retrieval 110, the example physician feature
selection 120, the example physician categorization 130, the
example referral processor 140, the example user interface 150, the
example data store of disease profiles 160, and/or the example
patient condition matching 170 could be implemented by the example
processor platform P100 of FIG. 4 and/or one or more circuit(s),
programmable processor(s), application specific integrated
circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or
field programmable logic device(s) (FPLD(s)), field-programmable
gate array(s) (FPGA(s)), fuses, etc. When any apparatus claim of
this patent incorporating one or more of these elements is read to
cover a purely software and/or firmware implementation, at least
one of the example physician retrieval 110, the example physician
feature selection 120, the example physician categorization 130,
the example referral processor 140, the example user interface 150,
the example data store of disease profiles 160, and/or the example
patient condition matching 170 are hereby expressly defined to
include a tangible article of manufacture such as a tangible
computer-readable medium storing the firmware and/or software.
Further still, any of the example physician retrieval 110, the
example physician feature selection 120, the example physician
categorization 130, the example referral processor 140, the example
user interface 150, the example data store of disease profiles 160,
and/or the example patient condition matching 170 may include one
or more elements, processes and/or devices in addition to, or
instead of, those illustrated in FIG. 1, and/or may include more
than one of any or all of the illustrated elements, processes and
devices.
[0024] FIG. 2 is a flowchart representing an example process that
may be embodied as machine-accessible instructions and executed by,
for example, one or more processors to generate a list of candidate
physicians. A processor, a controller and/or any other suitable
processing device may be used, configured and/or programmed to
perform the example processes of FIG. 2. For example, the process
of FIG. 2 may be embodied in coded instructions stored on a
tangible computer-readable medium. Machine-readable instructions
comprise, for example, instructions that cause a processor, a
computer and/or a machine having a processor to perform one or more
particular processes. Alternatively, some or all of the components
of the example process of FIG. 2 may be implemented using any
combination(s) of ASIC(s), PLD(s), FPLD(s), FPGA(s), fuses,
discrete logic, hardware, firmware, etc. Also, some or all of the
components of the example process of FIG. 2 may be implemented
manually or as any combination of any of the foregoing techniques,
for example, any combination of firmware, software, discrete logic
and/or hardware. Further, many other methods of implementing the
example operations of FIG. 2 may be employed. For example, the
order of execution of the blocks may be changed, and/or one or more
of the blocks described may be changed, eliminated, sub-divided, or
combined. Additionally, the blocks of any or all of the components
of the example process of FIG. 2 may be carried out sequentially
and/or carried out in parallel by, for example, separate processing
threads, processors, devices, discrete logic, circuits, etc.
[0025] The example process 200 of FIG. 2 begins with physicians
being categorized in an organization by department and specialty
(block 210). The categorization is hierarchical going from general
categories down to specialists. Example categories can be, for
example, pediatrics, radiology, etc. The example process 200 of
FIG. 2 classifies and assembles disease profiles (block 220). The
disease profiles are created for the most common types of diseases
an organization sees. A patient is selected for referral (block
230) and is one of many patients looking for services. For the
patient that is selected, the example process of FIG. 2 determines
his/her disease profile (block 240). Disease profiles for diabetics
can include factors such as weight, blood glucose levels,
hypertension, family history, etc. The process 200 of FIG. 2
determines whether there are any physicians who match that profile
(250). If there is a match, those physicians are identified (block
270). If not, then the example process 200 looks to the general
pool of physicians and selects a physician category for potential
candidates (block 260). In both cases, additional physician
features are considered (block 280) such as past performance,
availability, geographic location, and/or price. The example
process 200 of FIG. 2 generates a recommendation list of physicians
for the patient (block 290). The user (e.g., a patient,
administrator, referring physician, etc.) can select a physician
from the recommendation list (block 295).
[0026] In certain examples, selection of a physician initiates an
email, message, and/or appointment scheduling with the selected
physician and/or associated administrator. In certain examples, a
physician may be automatically selected for the patient. In such
examples, the automatic selection may be overridden by a user
(e.g., a physician). In certain examples, the user is guided
through a referral process once a physician is selected from the
recommendation list. The selected physician is sent a referral
request that he/she can accept or deny. If the physician accepts,
then that physician starts the appointment/scheduling process with
the patient, for example.
[0027] FIG. 3 is a flowchart representing an example process that
may be embodied as machine-accessible instructions and executed by,
for example, one or more processors to generate a list of candidate
physicians. A processor, a controller and/or any other suitable
processing device may be used, configured and/or programmed to
perform the example processes of FIG. 3. For example, the process
of FIG. 3 may be embodied in coded instructions stored on a
tangible computer-readable medium. Machine-readable instructions
comprise, for example, instructions that cause a processor, a
computer and/or a machine having a processor to perform one or more
particular processes. Alternatively, some or all of the components
of the example process of FIG. 3 may be implemented using any
combination(s) of ASIC(s), PLD(s), FPLD(s), FPGA(s), fuses,
discrete logic, hardware, firmware, etc. Also, some or all of the
components of the example process of FIG. 3 may be implemented
manually or as any combination of any of the foregoing techniques,
for example, any combination of firmware, software, discrete logic
and/or hardware. Further, many other methods of implementing the
example operations of FIG. 3 may be employed. For example, the
order of execution of the blocks may be changed, and/or one or more
of the blocks described may be changed, eliminated, sub-divided, or
combined. Additionally, the blocks of any or all of the components
of the example process of FIG. 3 may be carried out sequentially
and/or carried out in parallel by, for example, separate processing
threads, processors, devices, discrete logic, circuits, etc.
[0028] The example process 300 of FIG. 3 generally looks at
physicians based on disease profiles and physician features, but
not necessarily physician categories since a physician may have
achieved good results and gotten good feedback outside of his/her
specialty area. Physicians within an organization can be associated
with a disease profile, but may not necessarily be categorized by
department. The example process 300 of FIG. 3 begins by gathering
the disease profile of the patient (block 310) as well as the
demographic information of the patient (block 320). Based on both
sets of information, the example process 300 of FIG. 3 assigns a
general condition profile to the patient (block 330). With this
information, the example process 300 of FIG. 3 develops physician
cohorts (block 340). In addition to looking at the general patient
condition profile, the example process of FIG. 3 assesses the cost
of delivery (block 350). Physicians are matched to the patient
based on clinical indicators and the patient profile (block 360).
In this process 300, a physician is judged based on the patient
condition profiles. For instance, a physician may be a
cardiologist, but if he/she sees a lot of diabetics and maintains
quality very well, then he/she may be selected. The example process
300 helps identify the best provider(s) for the patient without
putting him/her into a category. If the physician accepts the
referral when the patient chooses him/her (block 370), then the
process 300 of FIG. 3 begins an appointment/scheduling process
(block 390). If not, then another physician is selected from the
recommendation list (block 380).
[0029] FIG. 4 is a block diagram of an example processor platform
P100 capable of executing the example process of FIG. 2 and the
example process of FIG. 3 to generate a recommendation list of
referrals for patients to select from. The example processor
platform P100 can be, for example, a computer, a workstation, a
server and/or any other type of computing device containing a
processor.
[0030] The processor platform P100 of the instant example includes
at least one programmable processor P105. For example, the
processor P105 can be implemented by one or more Intel.RTM.
microprocessors from the Pentium.RTM. family, the Itanium.RTM.
family or the XScale.RTM. family. Of course, other processors from
other processor families and/or manufacturers are also appropriate.
The processor P105 executes coded instructions P110 and/or P112
present in main memory of the processor P105 (e.g., within a
volatile memory P115 and/or a non-volatile memory P120) and/or in a
storage device P150. The processor P105 may execute, among other
things, the example machine-accessible instructions of FIGS. 2-3 to
generate a recommendation list of physicians for patients to select
from. Thus, the coded instructions P110, P112 may include the
example instructions of FIGS. 2-3.
[0031] The processor P105 is in communication with the main memory
including the non-volatile memory P110 and the volatile memory
P115, and the storage device P150 via a bus P125. The volatile
memory P115 may be implemented by Synchronous Dynamic Random Access
Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic
Random Access Memory (RDRAM) and/or any other type of RAM device.
The non-volatile memory P110 may be implemented by flash memory
and/or any other desired type of memory device. Access to the
memory P115 and the memory P120 may be controlled by a memory
controller.
[0032] The processor platform P100 also includes an interface
circuit P130. Any type of interface standard, such as an external
memory interface, serial port, general-purpose input/output, as an
Ethernet interface, a universal serial bus (USB), and/or a PCI
express interface, etc, may implement the interface circuit
P130.
[0033] The interface circuit P130 may also includes one or more
communication device(s) P112 such as a network interface card to
facilitate exchange of data, packets, and/or routing information
with other nodes, servers, devices and/or routers of a network.
[0034] In some examples, the processor platform P100 also includes
one or more mass storage devices P150 to store software and/or
data. Examples of such storage devices P150 include a floppy disk
drive, a hard disk drive, a solid-state hard disk drive, a CD
drive, a DVD drive and/or any other solid-state, magnetic and/or
optical storage device. The example storage devices P150 may be
used to, for example, store the example coded instructions of FIGS.
2-3.
[0035] Thus, certain examples provide a way to generate a
recommended list of physicians to a patient. This is done
automatically and quickly and is meant to provide the best list of
possible physicians matching the condition of the patient. The list
is based on a number of factors including convenience and location
and helps enable a user generating the recommendation list to
navigate forward to narrow a recommendation list and/or backward to
broaden the list. Certain examples transform a pool of possible
physicians into a refined ranking of physicians. Physicians that
are not eliminated by the constraining information, rules,
preferences, etc., can be ranked and sorted. Additionally, in
certain examples, a pareto-front can be calculated to return the
physician(s) that have the most attractive values per feature
across all features. A physician that is very efficient in treating
previous patients can be farther away than another physician, but
ultimately ranked higher because of their past performance. All
features/parameters can be combined to return a ranked list of
physicians, which has the ability to be further sorted
independently by an end user.
[0036] In certain examples, clinical decisioning is provided to
collect information and best practices regarding how clinicians
have treated patients in the past and apply that information to a
current patient. An organization requesting a referring can specify
one or more parameters/criteria to frame a referral request. For
example, an organization might say that they want to refer patients
to physicians who treat patients faster using fewer resources. In
certain examples, patients with similarities can be grouped to make
more efficient use of expertise, resources, etc. By tailoring
priorities and/or other parameters, as well as focusing on
profiles, categories, and available information to narrow a list of
referral physicians, an organization can be provide with more
control of outcomes and cost and better management of resources,
for example.
[0037] In certain examples, based on preference and information
captured regarding physicians, diseases, cost, and/or other data, a
healthcare organization can be provided with a list of
recommendations for referral. Data can be gathered from one or more
data sources including clinical knowledge bases, clinical
information systems, patient and/or colleague feedback,
review/processing of clinical outcomes data, financial and/or
schedule analysis, etc. For an accountable care organization,
rather than referrals being given to patients based on physician
relationships, certain examples provide greater efficiency by
matching patient to physician based on these indicators, not based
on friends and/or other personal relationships that may run counter
to diagnosis and treatment efficiency and effectiveness goals.
Certain examples help a referring physician to deliver a better
referral based not only on quality but also on a finer grain of
detail regarding physician suitability for a particular physician.
For example, a physician may have a history of working well with
poor diabetics and not with wealthy suburban housewives. A
physician can be evaluated based on patient profile. For example,
the physician may be a cardiologist, but if he sees many diabetics
and maintains a high quality of treatment, then he may be selected.
In this way, a best care provider for a patient can be identified
without putting the care provider into a category.
[0038] In certain examples, available data is analyzed to identify
and establish profiles and then develop cohorts based on the
profiles that would then include the profiles. Specific type(s) of
cohorts can be examined based on relevance (e.g., disease state,
primary physician referral cost, etc.) to identify and narrow a
referral recommendation list. Additionally, medical literature,
common morbidity profiles, etc., can be referenced in comparison
with patient condition and physician information to develop a
referral list. Physician(s) can be matched with patients based on
clinical indicators as well as demographics and/or other factors,
for example.
[0039] Although certain example methods, apparatus and articles of
manufacture have been described herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all systems, methods, apparatus and articles of manufacture
fairly falling within the scope of the claims of this patent.
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