U.S. patent application number 13/906836 was filed with the patent office on 2014-12-04 for system and method for providing a disability insurance claim triage platform.
The applicant listed for this patent is Willie F. Gray, Amanda Sue Harper, Allison M. Scaia, Lisa Workin. Invention is credited to Willie F. Gray, Amanda Sue Harper, Allison M. Scaia, Lisa Workin.
Application Number | 20140358591 13/906836 |
Document ID | / |
Family ID | 51986137 |
Filed Date | 2014-12-04 |
United States Patent
Application |
20140358591 |
Kind Code |
A1 |
Gray; Willie F. ; et
al. |
December 4, 2014 |
SYSTEM AND METHOD FOR PROVIDING A DISABILITY INSURANCE CLAIM TRIAGE
PLATFORM
Abstract
According to some embodiments, a triage platform may receive
data indicative of a disability insurance claim submitted in
connection with a disability insurance policy, including at least
one claim characteristic. The triage platform may determine, based
on the claim characteristic, a claim segment to be associated with
the disability insurance claim. A claim handler may be assigned to
the disability insurance claim in accordance with the determined
claim segment. Information about the disability insurance claim may
then be automatically routed to the assigned claim handler.
Inventors: |
Gray; Willie F.; (North
Granby, CT) ; Harper; Amanda Sue; (Atlanta, GA)
; Scaia; Allison M.; (St. Louis, MO) ; Workin;
Lisa; (Eagan, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Gray; Willie F.
Harper; Amanda Sue
Scaia; Allison M.
Workin; Lisa |
North Granby
Atlanta
St. Louis
Eagan |
CT
GA
MO
MN |
US
US
US
US |
|
|
Family ID: |
51986137 |
Appl. No.: |
13/906836 |
Filed: |
May 31, 2013 |
Current U.S.
Class: |
705/4 |
Current CPC
Class: |
G06N 20/00 20190101;
G06Q 40/08 20130101; G06F 16/22 20190101 |
Class at
Publication: |
705/4 |
International
Class: |
G06Q 40/00 20120101
G06Q040/00 |
Claims
1. A system associated with a disability insurance policy, the
system comprising: a computer storage unit for receiving, storing,
and providing data indicative of a disability insurance claim
submitted in connection with the disability insurance policy,
including at least one claim characteristic; and a processor in
communication with the storage unit, wherein the processor is
configured for: determining, based on the claim characteristic, a
claim segment to be associated with the disability insurance claim,
assigning a claim handler to the disability insurance claim in
accordance with the determined claim segment, and automatically
routing information about the disability insurance claim to the
assigned claim handler.
2. The system of claim 1, wherein the disability insurance claim
comprises one of: (i) a long term disability insurance claim, (ii)
a short term disability insurance claim, or (iii) a workers'
compensation insurance claim.
3. The system of claim 1, wherein the disability insurance claim
comprises a long term disability claim and at least some of the
data indicative of the long term disability insurance claim is
based on information copied from a short term disability insurance
system.
4. The system of claim 1, wherein the processor is further
configured for: automatically transmitting information about the
disability insurance claim to at least one of: (i) an email server,
(ii) a workflow application, and (iii) a calendar application.
5. The system of claim 1, wherein the computer storage unit
receives the data indicative of the disability insurance claim via
at least one of: (i) a submitted paper claim, and (ii) a telephone
call center.
6. The system of claim 1, wherein the claim characteristic
comprises at least one of: (i) a date of birth, (ii) a date of
disability, (iii) a waiting period, (iv) diagnosis information, (v)
a claimant salary, (vi) an own occupation period, (vii) a job type,
(viii) a marriage status, (ix) a benefit percentage, (x) claimant
gender, and (xi) information from an attending physician.
7. The system of claim 1, wherein potential claim segments include
at least (i) a segment for higher complexity disability insurance
claims and (ii) a segment for lower complexity disability insurance
claims.
8. The system of claim 7, wherein the segment for higher complexity
disability insurance claims is determined when at least two of the
following conditions are true for the disability insurance claim:
(i) a subjective diagnosis is associated with the insurance claim,
(ii) an uncertain recovery profile is associated with the insurance
claim, and (iii) a pre-determined level of financial complexity is
associated with the insurance claim.
9. The system of claim 7, wherein the segment for lower complexity
disability insurance claims is determined when either of following
conditions is true for the disability insurance claim: (i) a
likelihood of recovery is below a first pre-determined threshold
value or (ii) the likelihood of recovery is above a second
pre-determined threshold value.
10. The system of claim 1, wherein the processor is further
configured for: outputting an indication of the determined claim
segment to a team leader.
11. The system of claim 1, wherein the processor is further
configured for: determining and outputting an indication of a
diagnosis description for the disability insurance claim.
12. The system of claim 1, wherein the processor is further
configured for: determining and outputting a recovery profile for
the disability insurance claim based on a predictive model.
13. The system of claim 12, wherein a plurality of recovery
profiles are determined and output, each associated with a
different recovery period.
14. The system of claim 1, wherein the processor is further
configured for: determining and outputting a test change outlook
for the disability insurance claim.
15. The system of claim 1, wherein the determination is based at
least in part on a predictive model trained with historical
disability insurance claim information.
16. The system of claim 15, wherein the predictive mode utilizes
high level diagnosis groupings.
17-18. (canceled)
19. A method associated with a long term disability insurance
policy, the method comprising: receiving at a triage platform data
indicative of a long term disability insurance claim submitted in
connection with the long term disability insurance policy,
including at least one claim characteristic; determining, by a
computer processor of the triage platform, based on the claim
characteristic, a claim segment to be associated with the long term
disability insurance claim; assigning, by the computer processor of
the triage platform, a claim handler to the long term disability
insurance claim in accordance with the determined claim segment;
and automatically routing, by the computer processor of the triage
platform, information about the long term disability insurance
claim to the assigned claim handler.
20. The method of claim 19, wherein potential claim segments
include at least (i) a segment for higher complexity long term
disability insurance claims and (ii) a segment for lower complexity
long term disability insurance claims.
21. The method of claim 20, wherein the segment for higher
complexity long term disability insurance claims is determined when
at least two of the following conditions are true for the long term
disability insurance claim: (i) a subjective diagnosis is
associated with the insurance claim, (ii) an uncertain recovery
profile is associated with the insurance claim, and (iii) a
pre-determined level of financial complexity is associated with the
insurance claim.
22. The method of claim 20, wherein the segment for lower
complexity long term disability insurance claims is determined when
either of following conditions is true for the long term disability
insurance claim: (i) a likelihood of recovery is below a first
pre-determined threshold value or (ii) the likelihood of recovery
is above a second pre-determined threshold value.
23. A non-transitory, computer-readable medium storing instructions
adapted to be executed by a computer processor to perform a method
associated with a long term disability insurance policy, said
method comprising: receiving at a triage platform data indicative
of a long term disability insurance claim submitted in connection
with the long term disability insurance policy, including at least
one claim characteristic; determining, by a computer processor of
the triage platform based on the claim characteristic, a claim
segment to be associated with the long term disability insurance
claim; assigning a claim handler to the long term disability
insurance claim in accordance with the determined claim segment;
and automatically routing information about the long term
disability insurance claim to the assigned claim handler.
24. The medium of claim 23, wherein potential claim segments
include at least (i) a segment for higher complexity long term
disability insurance claims and (ii) a segment for lower complexity
long term disability insurance claims.
25. The medium of claim 24, wherein the segment for higher
complexity long term disability insurance claims is determined when
at least two of the following conditions are true for the long term
disability insurance claim: (i) a subjective diagnosis is
associated with the insurance claim, (ii) an uncertain recovery
profile is associated with the insurance claim, and (iii) a
pre-determined level of financial complexity is associated with the
insurance claim.
26. The medium of claim 24, wherein the segment for lower
complexity long term disability insurance claims is determined when
either of following conditions is true for the long term disability
insurance claim: (i) a likelihood of recovery is below a first
pre-determined threshold value or (ii) the likelihood of recovery
is above a second pre-determined threshold value.
Description
FIELD
[0001] The present invention relates to computer systems and more
particularly to computer systems that provide a disability
insurance claim triage platform.
BACKGROUND
[0002] An insurer may provide payments when claims are made in
connection with a disability insurance policy. For example, an
employee who becomes too ill to work might receive payments
associated with a long term disability insurance policy purchased
by his or her employer. Note that payments might continue until the
employee is able to return to work. The insurer may assign a claim
handler to communicate with the employee, the employer, and/or
medical service providers to facilitate the employee's return to
the workplace. Moreover, different claim handlers may have
different abilities and/or different workloads.
[0003] In one approach, newly received disability insurance claims
might be assigned to claim handlers in a random or round robin
manner. This, however, might lead to one claim handler having a
significantly more complex workload as compared to another claim
handler. To avoid such a result, particular types of disability
insurance claims might be more effectively assigned to particular
claim handlers. For example, a relatively complicated insurance
claim might be more efficiently processed by a claim handler who
handles a relatively small number of insurance claims and/or is
especially skilled when it comes to handling these types of
insurance claims.
[0004] Manually determining which claim handler should be assigned
to each individual insurance claim, however, can be time consuming
task, especially when there are a substantial number of claims to
be analyzed. For example, an insurer might receive tens of
thousands of new long term disability insurance claims each year
(which might represent a billion dollars of potential liability).
It would therefore be desirable to provide systems and methods to
facilitate the assignment of disability insurance claims to claim
handlers, in an automated, efficient, and accurate manner.
SUMMARY
[0005] According to some embodiments, systems, methods, apparatus,
computer program code and means may facilitate the assignment of
disability insurance claims to claim handlers. In some embodiments,
a triage platform may receive data indicative of a disability
insurance claim submitted in connection with a disability insurance
policy, including at least one claim characteristic. The triage
platform may determine, based on the claim characteristic, a claim
segment to be associated with the disability insurance claim. A
claim handler may be assigned to the disability insurance claim in
accordance with the determined claim segment. Information about the
disability insurance claim may then be automatically routed to the
assigned claim handler.
[0006] Some embodiments provide: means for receiving, at a triage
platform, data indicative of a disability insurance claim submitted
in connection with a disability insurance policy, including at
least one claim characteristic; means for determining, by a
computer processor of the triage platform based on the claim
characteristic, a claim segment to be associated with the
disability insurance claim; means for assigning a claim handler to
the disability insurance claim in accordance with the determined
claim segment; and means for automatically routing information
about the disability insurance claim to the assigned claim
handler.
[0007] A technical effect of some embodiments of the invention is
an improved and computerized method to facilitate the assignment of
disability insurance claims to claim handlers. With these and other
advantages and features that will become hereinafter apparent, a
more complete understanding of the nature of the invention can be
obtained by referring to the following detailed description and to
the drawings appended hereto.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is block diagram of a system according to some
embodiments of the present invention.
[0009] FIGS. 2 and 3 illustrate methods that might be performed in
accordance with some embodiments.
[0010] FIG. 4 is block diagram of a triage tool or platform
according to some embodiments of the present invention.
[0011] FIG. 5 is a tabular portion of a long term disability
insurance claim database according to some embodiments.
[0012] FIG. 6A illustrates a disability insurance claim system
input in accordance with some embodiments.
[0013] FIG. 6B illustrates a triage platform graphical user output
in accordance with some embodiments.
[0014] FIG. 7 is a long term disability insurance claim process
flow in accordance with some embodiments.
[0015] FIG. 8 is a partially functional block diagram that
illustrates aspects of a computer system provided in accordance
with some embodiments of the invention.
[0016] FIG. 9 is a block diagram that provides another
representation of aspects of the system of FIG. 8.
[0017] FIG. 10 is a flow chart illustrating how a predictive model
might be trained according to some embodiments.
[0018] FIG. 11 illustrates predictive model inputs according to
some embodiments.
DETAILED DESCRIPTION
[0019] An insurer may provide payments when claims are made in
connection with a "disability insurance" policy. As used herein,
the phrase "disability insurance" may refer to a form of long term
disability insurance that insures an employee's earned income
against the risk that a disability will prevent him or her from
performing core work functions. For example, the inability to lift
heavy objects or maintain focus (as with a psychological disorder),
an illness or other conditions may cause physical impairment and an
inability to work. Insurance payments generally continue until the
employee is able to return to work, and in many cases the insurer
will assign a claim handler to communicate with the employee, the
employer, and/or medical service providers to facilitate the
employee's return to the workplace. Note that embodiments may also
be associated with other types of disability insurance, including
workers' compensation insurance, short term disability insurance,
and/or flexible combinations of short and long term disability
insurance.
[0020] In some cases, different claim handlers will have different
abilities and/or different workloads. As a result, particular types
of disability insurance claims might be more effectively assigned
to particular claim handlers. Manually determining which claim
handler should be assigned to each individual insurance claim,
however, can be time consuming and difficult task, especially when
there are a substantial number of claims to be analyzed. It would
therefore be desirable to provide systems and methods to facilitate
the assignment of disability insurance claims to claim handlers.
FIG. 1 is block diagram of a system 100 according to some
embodiments of the present invention. In particular, the system 100
includes a triage platform 150 that receives information about
disability insurance claims (e.g., by receiving an electronic file
from a team leader, an employer, an employee, an insurance agent, a
medical service provider, or a data storage 110). According to some
embodiments, incoming telephone calls and/or documents from a
doctor may be used to create information in a claim system 120
which, in turn, can provide information to the triage platform 150.
In other embodiments, the triage platform 150 may retrieve
information from a data warehouse 130 (e.g., when the triage
platform 150 is associated with a long term disability insurance
system, some information may be copied from a short term disability
system data warehouse). In other embodiments, some or all of the
information about a disability claim may be received via an only
claim submission process. The triage platform may, according to
some embodiments, provide an automatic initial assessment of new
insurance claim to determine an appropriate claim segment based on
complexity and/or identify a particular claim handler 160 to
process the insurance claim. According to some embodiments,
recovery profile information may be generated and provided to claim
handler. For example, historical information may be used to
generate appropriate recovery profile information based on the
specific facts of the insurance claim being processed.
[0021] The triage platform 150 might be, for example, associated
with a Personal Computers (PC), laptop computer, an enterprise
server, a server farm, and/or a database or similar storage
devices. The triage platform 150 may, according to some
embodiments, be associated with an insurance provider.
[0022] According to some embodiments, an "automated" triage
platform 150 may facilitate the assignment of disability insurance
claims to claim handlers 160. For example, the triage platform 150
may automatically output a recommended claim segment for a received
insurance claim (e.g., to a team leader) which may then be used to
facilitate assignment of a claim handler 160. As used herein, the
term "automated" may refer to, for example, actions that can be
performed with little (or no) intervention by a human.
[0023] As used herein, devices, including those associated with the
triage platform 150 and any other device described herein, may
exchange information via any communication network which may be one
or more of a Local Area Network (LAN), a Metropolitan Area Network
(MAN), a Wide Area Network (WAN), a proprietary network, a Public
Switched Telephone Network (PSTN), a Wireless Application Protocol
(WAP) network, a Bluetooth network, a wireless LAN network, and/or
an Internet Protocol (IP) network such as the Internet, an
intranet, or an extranet. Note that any devices described herein
may communicate via one or more such communication networks.
[0024] The triage platform 150 may store information into and/or
retrieve information from the data storage 110. The data storage
110 might be associated with, for example, a client, an employer,
or insurance policy and might store data associated with past and
current disability insurance claims. The data storage 110 may be
locally stored or reside remote from the insurance claim triage
platform 150. As will be described further below, the data storage
110 may be used by the triage platform 150 to generate predictive
models. According to some embodiments, the triage platform 150
communicates a recommended claim segment, such as by transmitting
an electronic file to a claim handler 160, a client device, an
insurance agent or analyst platform, an email server, a workflow
management system, etc. In other embodiments, the triage platform
150 might output a claim segment indication to a team leader who
might select a claim handler based on that indication or override
the indication based on other factor associated with the disability
claim.
[0025] Although a single triage platform 150 is shown in FIG. 1,
any number of such devices may be included. Moreover, various
devices described herein might be combined according to embodiments
of the present invention. For example, in some embodiments, the
claim triage platform 150 and data storage 110 might be co-located
and/or may comprise a single apparatus.
[0026] FIG. 2 illustrates a method that might be performed by some
or all of the elements of the system 100 described with respect to
FIG. 1 according to some embodiments of the present invention. The
flow charts described herein do not imply a fixed order to the
steps, and embodiments of the present invention may be practiced in
any order that is practicable. Note that any of the methods
described herein may be performed by hardware, software, or any
combination of these approaches. For example, a computer-readable
storage medium may store thereon instructions that when executed by
a machine result in performance according to any of the embodiments
described herein.
[0027] At 202, data indicative of a disability insurance claim
submitted in connection with a disability insurance policy may be
received. The data indicative of the disability insurance claim
might be received, for example, via a submitted paper claim or a
telephone call center. The received data may include at least one
claim "characteristic" associated with the insurance claim.
Examples of claim characteristics include, without limitation, an
employee's date of birth, a date of disability, a waiting period
(e.g., how long a claimant might need to be unable to work before
payments are provided), diagnosis information, a claimant salary,
an own occupation period (e.g., after which an employee might
return to a different type of job), a job type, a marriage status,
a benefit percentage, claimant gender, and/or information from an
attending physician.
[0028] At 204, a claim "segment" to be associated with the
disability insurance claim may be determined. For example,
potential claim segments might include a segment for higher
complexity disability insurance claims and a segment for lower
complexity disability insurance claims.
[0029] At 206, a claim handler is assigned to the disability
insurance claim in accordance with the determined claim segment.
Note that other information may also be considered when determining
a claim segment and/or claim handler. For example, the language
spoken by the claimant and/or the proficiency and/or specialized
abilities of a claim handler might be taken into account.
Information about the disability insurance claim may then be
automatically routed to the assigned claim handler at 208.
According to some embodiments, the system may further output an
indication of the determined claim segment to a team leader,
determine and output an indication of a diagnosis description for
the disability insurance claim, and/or determine and outputting a
recovery profile for the disability insurance claim. For example,
the system may transmit, to the assigned claim handler, recovery
profile comments generated based on historical results and the
details regarding the claim being handled. Note that a plurality of
recovery profiles might be provided, each associated with a
different recovery period.
[0030] The disability insurance claim might be associated with a
particular claim segment in accordance with any number of various
business logic rules. For example, FIG. 3 illustrates one method
that might be performed by some or all of the elements of the
system 100 described with respect to FIG. 1 according to some
embodiments of the present invention. In this example, the
insurance policy is a long term disability insurance policy and
three segments have been established: [0031] Segment One: for
claims which may require less intense intervention (which can be
assigned to claim handlers with relatively heavy workloads); [0032]
Segment Two: for typical claims which may have a possible return to
work in either the claimant's own occupation or an alternate
occupation; and [0033] Segment Three: for claims which may have
medical, occupational, and/or financial complexities which require
more in-depth investigation (which should be assigned to claim
handlers with relatively light workloads so they can devote more
time to each individual insurance claim or to claim handlers who
are skilled at handling complex claims).
[0034] At 302, data indicative of a long term disability insurance
claim submitted in connection with a long term disability insurance
policy may be received. At 304, the insurance claim is evaluated to
determine if the claim meets at least two of the following three
criteria: (1) is it a relatively subjective diagnosis (e.g.
clinical depression, Lyme disease), (2) is there an "uncertain"
recovery profile, and (3) is there a greater than average amount of
financial complexity associated with the claim. If at least two of
those criteria are present at 304, the insurance claim is
associated with segment three at 306 (for higher complexity long
term disability insurance claims).
[0035] If the insurance claim did not have at least two of the
criteria at 304, it is determined at 308 whether or not the
insurance claim's recovery profile satisfies either: (1) a
likelihood of recovery above a pre-determined threshold value
(e.g., a pregnant employee is very likely to return to work) or (2)
a likelihood of recovery below a pre-determined threshold (e.g., an
employee with late stage pancreatic cancer is very unlikely to
return to work). If either of these conditions are true at 308, the
insurance claim is associated with segment one at 310 (for lower
complexity long term disability insurance claims), otherwise the
insurance claim is associated with segment two at 312. The
determined segment might then be output (e.g., to a team leader)
and/or used to assign a claim handler to the insurance claim.
[0036] The embodiments described herein may be implemented using
any number of different hardware configurations. For example, FIG.
4 illustrates a triage platform 400 that may be, for example,
associated with the system 100 of FIG. 1. The triage platform 400
comprises a processor 410, such as one or more commercially
available Central Processing Units (CPUs) in the form of one-chip
microprocessors, coupled to a communication device 420 configured
to communicate via a communication network (not shown in FIG. 4).
The communication device 420 may be used to communicate, for
example, with one or more remote team leader and/or claim handler
devices. The triage platform 400 further includes an input device
440 (e.g., a mouse and/or keyboard to enter information about an
insurance claim and/or segmentation logic) and an output device 450
(e.g., to output a recommended segment and/or claim handler).
[0037] The processor 410 also communicates with a storage device
430. The storage device 430 may comprise any appropriate
information storage device, including combinations of magnetic
storage devices (e.g., a hard disk drive), optical storage devices,
mobile telephones, and/or semiconductor memory devices. The storage
device 430 stores a program 412 and/or a triage engine application
414 for controlling the processor 410. The processor 410 performs
instructions of the programs 412, 414, and thereby operates in
accordance with any of the embodiments described herein. For
example, the processor 410 may receive data indicative of a long
term disability insurance claim submitted in connection with a long
term disability insurance policy, including at least one claim
characteristic. The processor 410 may determine, based on the claim
characteristic, a claim segment to be associated with the long term
disability insurance claim. A claim handler may be assigned to the
long term disability insurance claim by the processor 410 in
accordance with the determined claim segment. Information about the
long term disability insurance claim may then be automatically
routed to the assigned claim handler by the processor 410.
[0038] The programs 412, 414 may be stored in a compressed,
uncompiled and/or encrypted format. The programs 412, 414 may
furthermore include other program elements, such as an operating
system, a database management system, and/or device drivers used by
the processor 410 to interface with peripheral devices.
[0039] As used herein, information may be "received" by or
"transmitted" to, for example: (i) the triage platform 400 from
another device; or (ii) a software application or module within the
triage platform 400 from another software application, module, or
any other source.
[0040] In some embodiments (such as shown in FIG. 4), the storage
device 430 further stores insurance claim data 500, a claim handler
data 460 (e.g., indicating a handlers workload, experience, special
expertise, etc.), and segment rules 470. An example of a database
that may be used in connection with the triage platform 400 will
now be described in detail with respect to FIG. 5. Note that the
database described herein is only one example, and additional
and/or different information may be stored therein. Moreover,
various databases might be split or combined in accordance with any
of the embodiments described herein. For example, the claim handler
data 460 and/or segment rules 470 might be combined and/or linked
to each other within the triage engine application 414.
[0041] Referring to FIG. 5, a table is shown that represents the
claim handler database 500 that may be stored at the triage
platform 400 according to some embodiments. The table may include,
for example, entries identifying insurance claims submitted under a
particular long term disability insurance policy or a number of
different policies. The table may also define fields 502, 504, 506,
508, 510 for each of the entries. The fields 502, 504, 506, 508,
510 may, according to some embodiments, specify: a claim identifier
502, a disability description 504, a date of birth 506, a
determined segment 508, and an assigned claim handler identifier
510. The claim handler database 500 may be created and updated, for
example, based on information electrically received and/or manually
entered into the system by a team leader.
[0042] The claim identifier 502 may be, for example, a unique
alphanumeric code identifying a claim submitted in connection with
a long term disability insurance policy. The disability description
504 may indicate a diagnosis associated with the claim identifier
502 and the data of birth 506 may indicate when he or she was born.
Although two claim characteristics 504, 506 are illustrated in FIG.
5 for clarity, note that an actual implementation may evaluate many
other factors.
[0043] The determined segment 508 might be associated with an
automatically determined level of complexity assessed by a triage
platform. For example, claim "C.sub.--10001" was determined to be a
segment one insurance claim because a pregnancy has a fairly
predictable return to work recovery profile (and may therefore be
assigned to a claim handler with a relatively heavy workload). The
determined segment 508 may then be used by a team leader and/or the
triage platform to establish an assigned claim handler identifier
510. For example, claim handler "H.sub.--101" might have a
relatively heavy workload while claim handler "H.sub.--103" has a
relatively light workload (and can therefore devote more time to
each individual long term disability insurance claim).
[0044] By way of example, a team leader may enter key
characteristic variables for a newly received long term disability
insurance claim. Note that in the case of a long term disability
claim, some or all of this information might be automatically
populated based on corresponding data elements of a prior
associated short term disability claim. Responsive to this entered
information, the triage platform may provide a recommended claim
segment based on the diagnosis, recovery profile, and/or the
financial complexity of the claim. The triage platform may also
output a diagnosis description pulled from the Official Disability
Guidelines definition, and one or more recovery profiles. These
factors may then be used by the team leader to an appropriate
analyst resource. In addition, the recovery profile information may
be used to help the team leader understand the appropriate next
follow-up actions for the insurance claim as well as provide
guidance that might need to be forwarded to the claim handler or
analyst. According to some embodiments, a team leader might review
and/or override a claim segment that was automatically determined
by the triage platform.
[0045] FIG. 6A illustrates a disability insurance claim system
input 602 in accordance with some embodiments. The input 602 may
include, for example, text-based answers to questions 612 and/or
selections from pull down menus 622 to provide information about
claimants and/or claimant conditions. According to some
embodiments, answers to some questions might result in one or more
follow-up questions being automatically determined by the system
and presented on the input 602. Information provided via the input
602 may, according to some embodiments, help determine an
appropriate segment and/or claim handler for a disability
claim.
[0046] FIG. 6B illustrates a triage platform graphical user output
600 that might be displayed to a team leader in accordance with
some embodiments. The output 600 includes the recommended segment
610 (e.g., based on the determined segment 508 in the insurance
claim database 500) along with the reasons why that particular
segment was determined (e.g., based on the diagnosis, the recovery
profile, and/or financial complexity of the claim). According to
some embodiments, the output 600 further includes a diagnosis
description for the insurance claim. The diagnosis description
might, for example, be pulled from the Official Disability
Guidelines definitions.
[0047] The output 600 also includes 6 month and 24 month recovery
recommendations 620 for the insurance claim. The predicted 24 month
Return To Work ("RTW") recovery rate might be determined, for
example, using the employee's particular medical condition and
other factors entered by the team leader. Note that the recovery
recommendations 620, as well as the other information provided on
the output, may be generated by multiple predictive models (e.g.,
different models associated with different time periods may output
suggested text).
[0048] In addition, the output 600 includes an overall recovery
profile 630. For example, a behavioral health recovery rate might
be determined based on the medical condition (e.g., clinical
depression or bi-polar disorder), the elimination period, marriage
status, benefit percentage, salary, and/or gender of the
claimant.
[0049] The output 600 further includes information about
occupational risks, test change outlooks, and other considerations
640 that may be relevant to a claim handler. The test change
outlook might represent an overall likelihood that a claimant, who
has reached a test change point, will pass a test change
requirement meeting an "any occupation" definition of disability.
The occupational risk information might be based on, for example,
job class, claimant age, diagnosis and/or salary.
[0050] A graphical representation 650 of the recovery profiles
and/or other data in comparison to an average long term disability
claim may also be provided in the output 600. For example, the
likelihood of the claimant recovering in 6 months in contrast with
the baseline (or average) for claimants within the first 6 months
might be displayed along with the likelihood of the claimant
recovering in 6 to 24 months in contrast with the baseline (or
average) for claimants within 6 to 24 months of when the claim was
submitted.
[0051] FIG. 7 is a long term disability insurance claim process
flow 700 in accordance with some embodiments. After a new long term
disability insurance claim is received by a team leader at 710,
information about the claim may be entered into a triage platform
at 712. The triage outputs at 714 may be stored into data storage
at 740 for later analysis and/or retrieval. The triage outputs at
714 are evaluated at 716 to determine if the insurance claim needs
to be re-assigned to another team leader (e.g., because of special
considerations regarding the claim or the leader). If the insurance
claim does not need to be re-assigned, the leader assigns the claim
at 718 and it is forwarded to an analyst at 730 for further
processing. If the insurance claim needs to be re-assigned, it is
received by the alternate team leader at 720, who may determine the
claim segment at 722 and assign the long term disability claim at
724.
[0052] Thus, embodiments may provide group benefit claims with
automated long term disability segmentation allowing improved
claim, medical, and/or financial management of insurance claims.
According to some embodiments, the application of specific claim
variables, including diagnosis and other claim characteristics, as
well insurer's historical claim recovery information may let claims
be aligned within three segments based on claim management
complexity. Note however, that two, four or more segments might be
provided instead. An ability analyst and/or claim handler may be
aligned by segment and receive claims best suited for his or her
skill set, which may further result in improved claim management.
That is, long term disability segmentation may result in optimized
outcomes for long term disability insurance claims through better
application of skilled resources to the right claims. The triage
platform may assist team leaders with claim assessment (and claim
assignment to an appropriate resource) by providing a recommended
claim segment and assisting with the creation of an appropriate
claim management plan.
[0053] According to some embodiments, a determination of an
appropriate claim segment may be based at least in part on a
predictive model trained with historical long term disability
insurance claim information. For example, triage platform
segmentation might be aided by data modeling, input from an
insurer's claim subject matter experts, and analysis of historical
claim experience. The following are some variables that might be
used by a predictive model to help identify a correct claim segment
for a long term disability claim: [0054] Date of Birth (age, with
older claimants perhaps requiring more time to return to work),
[0055] Date of Disability, [0056] Waiting Period, [0057] Diagnosis,
[0058] Salary, [0059] Own Occupation Period (period of being
disabled from claimant's own occupation as compared to any
occupation), [0060] And Job Type (e.g., Sedentary or
Non-sedentary), [0061] Spouse, [0062] Benefit Percentage, and
[0063] Gender. According to some embodiments, the predictive mode
utilizes high level diagnosis groupings. For example, by analyzing
an insurer's historical data for high level diagnostic groups
(e.g., cancer, respiratory, musculoskeletal, circulatory, etc.),
predictive models may be created for each group to identify the
variables most likely to impact duration and claim outcomes.
Factors that may contribute to the complexity of claim management,
such as salary and definition of disability, might also be
considered. Note that different diagnosis groupings may be
associated with different sets and/or weights of relevant factors.
For example, depending on the high level diagnosis grouping (e.g.,
cancer, respiratory illness), different variables may be
significant and/or relevant and the weightings of common variables
may be different.
[0064] In general, and for the purposes of introducing concepts of
embodiments of the present invention, a computer system may
incorporate a "predictive model." As used herein, the phrase
"predictive model" might refer to, for example, any of a class of
algorithms that are used to understand relative factors
contributing to an outcome, estimate unknown outcomes, discover
trends, and/or make other estimations based on a data set of
factors collected across prior trials. Note that a predictive model
might refer to, but is not limited to, methods such as ordinary
least squares regression, logistic regression, decision trees,
neural networks, generalized linear models, and/or Bayesian models.
The predictive model is trained with historical claim transaction
data, and is applied to current claim transactions to determine how
the current claim transactions should be handled by a long term
disability insurance program. Both the historical claim transaction
data and data representing the current claim transactions might
include, according to some embodiments, indeterminate data or
information extracted therefrom. For example, such data/information
may come from narrative and/or medical text notes associated with a
claim file.
[0065] Features of some embodiments associated with a predictive
model will now be described by first referring to FIG. 8. FIG. 8 is
a partially functional block diagram that illustrates aspects of a
computer system 800 provided in accordance with some embodiments of
the invention. For present purposes it will be assumed that the
computer system 800 is operated by an insurance company (not
separately shown) for the purpose of referring certain claims to
long term disability insurance claim handlers as appropriate.
[0066] The computer system 800 includes a data storage module 802.
In terms of its hardware the data storage module 802 may be
conventional, and may be composed, for example, by one or more
magnetic hard disk drives. A function performed by the data storage
module 802 in the computer system 800 is to receive, store and
provide access to both historical claim transaction data (reference
numeral 804) and current claim transaction data (reference numeral
806). As described in more detail below, the historical claim
transaction data 804 is employed to train a predictive model to
provide an output that indicates how a claim should by handled by a
long term disability insurance program (e.g., segment assignments),
and the current claim transaction data 806 is thereafter analyzed
by the predictive model. Moreover, as time goes by, and results
become known from processing current claim transactions, at least
some of the current claim transactions may be used to perform
further training of the predictive model. Consequently, the
predictive model may thereby adapt itself to changing patterns of
long term disability insurance claims.
[0067] Either the historical claim transaction data 804 or the
current claim transaction data 806 might include, according to some
embodiments, determinate and indeterminate data. As used herein and
in the appended claims, "determinate data" refers to verifiable
facts such as the date of birth, age or name of a claimant or name
of another individual or of a business or other entity; a type of
injury, accident, sickness, or pregnancy status; a medical
diagnosis; a date of loss, or date of report of claim, or policy
date or other date; a time of day; a day of the week; a vehicle
identification number, a geographic location; and a policy
number.
[0068] As used herein and in the appended claims, "indeterminate
data" refers to data or other information that is not in a
predetermined format and/or location in a data record or data form.
Examples of indeterminate data include narrative speech or text,
information in descriptive notes fields and signal characteristics
in audible voice data files. Indeterminate data extracted from
medical notes might be associated with, for example, a prior
injury, alcohol related co-morbidity information, drug related
co-morbidity information, tobacco related co-morbidity information,
arthritis related co-morbidity information, diabetes related
co-morbidity information, and/or obesity related co-morbidity
information.
[0069] The determinate data may come from one or more determinate
data sources 808 that are included in the computer system 800 and
are coupled to the data storage module 802. The determinate data
may include "hard" data like the claimant's name, date of birth,
social security number, policy number, address; the date of loss;
the date the claim was reported, etc. One possible source of the
determinate data may be the insurance company's policy database
(not separately indicated). Another possible source of determinate
data may be from data entry by the insurance company's claims
intake administrative personnel.
[0070] The indeterminate data may originate from one or more
indeterminate data sources 810, and may be extracted from raw files
or the like by one or more indeterminate data capture modules 812.
Both the indeterminate data source(s) 810 and the indeterminate
data capture module(s) 812 may be included in the computer system
800 and coupled directly or indirectly to the data storage module
802. Examples of the indeterminate data source(s) 810 may include
data storage facilities for document images, for text files (e.g.,
claim handlers' notes) and digitized recorded voice files (e.g.,
claimants' oral statements, witness interviews, claim handlers'
oral notes, etc.). Examples of the indeterminate data capture
module(s) 812 may include one or more optical character readers, a
speech recognition device (i.e., speech-to-text conversion), a
computer or computers programmed to perform natural language
processing, a computer or computers programmed to identify and
extract information from narrative text files, a computer or
computers programmed to detect key words in text files, and a
computer or computers programmed to detect indeterminate data
regarding an individual. For example, claim handlers' opinions may
be extracted from their narrative text file notes.
[0071] The computer system 800 also may include a computer
processor 814. The computer processor 814 may include one or more
conventional microprocessors and may operate to execute programmed
instructions to provide functionality as described herein. Among
other functions, the computer processor 814 may store and retrieve
historical claim transaction data 804 and current claim transaction
data 806 in and from the data storage module 802. Thus the computer
processor 814 may be coupled to the data storage module 802.
[0072] The computer system 800 may further include a program memory
816 that is coupled to the computer processor 814. The program
memory 816 may include one or more fixed storage devices, such as
one or more hard disk drives, and one or more volatile storage
devices, such as RAM (random access memory). The program memory 816
may be at least partially integrated with the data storage module
802. The program memory 816 may store one or more application
programs, an operating system, device drivers, etc., all of which
may contain program instruction steps for execution by the computer
processor 814.
[0073] The computer system 800 further includes a predictive model
component 818. In certain practical embodiments of the computer
system 800, the predictive model component 818 may effectively be
implemented via the computer processor 814, one or more application
programs stored in the program memory 816, and data stored as a
result of training operations based on the historical claim
transaction data 804 (and possibly also data resulting from
training with current claims that have been processed). In some
embodiments, data arising from model training may be stored in the
data storage module 802, or in a separate data store (not
separately shown). A function of the predictive model component 818
may be to determine an appropriate complexity segment for current
claim transactions. The predictive model component may be directly
or indirectly coupled to the data storage module 802.
[0074] The predictive model component 818 may operate generally in
accordance with conventional principles for predictive models,
except, as noted herein, for at least some of the types of data to
which the predictive model component is applied. Those who are
skilled in the art are generally familiar with programming of
predictive models. It is within the abilities of those who are
skilled in the art, if guided by the teachings of this disclosure,
to program a predictive model to operate as described herein.
[0075] Still further, the computer system 800 includes a model
training component 820. The model training component 820 may be
coupled to the computer processor 814 (directly or indirectly) and
may have the function of training the predictive model component
818 based on the historical claim transaction data 804. (As will be
understood from previous discussion, the model training component
820 may further train the predictive model component 818 as further
relevant claim transaction data becomes available.) The model
training component 820 may be embodied at least in part by the
computer processor 814 and one or more application programs stored
in the program memory 816. Thus the training of the predictive
model component 818 by the model training component 820 may occur
in accordance with program instructions stored in the program
memory 816 and executed by the computer processor 814.
[0076] In addition, the computer system 800 may include an output
device 822. The output device 822 may be coupled to the computer
processor 814. A function of the output device 822 may be to
provide an output that is indicative of (as determined by the
trained predictive model component 818) particular claim segments
and/or claim handlers for the current claim transactions. The
output may be generated by the computer processor 814 in accordance
with program instructions stored in the program memory 816 and
executed by the computer processor 814. More specifically, the
output may be generated by the computer processor 814 in response
to applying the data for the current claim transaction to the
trained predictive model component 818. The output may, for
example, be a true/false flag or a number within a predetermined
range of numbers. In some embodiments, the output device may be
implemented by a suitable program or program module executed by the
computer processor 814 in response to operation of the predictive
model component 818.
[0077] Still further, the computer system 800 may include a routing
module 824. The routing module 824 may be implemented in some
embodiments by a software module executed by the computer processor
814. The routing module 824 may have the function of directing
workflow based on the output from the output device. Thus the
routing module 824 may be coupled, at least functionally, to the
output device 822. In some embodiments, for example, the routing
module may direct workflow by referring, to a long term disability
insurance program claim handler 826, current claim transactions
analyzed by the predictive model component 818 and found to be
associated with a particular claim segment. In particular, these
current claim transactions may be referred to case manager 828 who
is associated with the long term disability insurance program claim
handler 826. The long term disability insurance program claim
handler 826 may be a part of the insurance company that operates
the computer system 800, and the case manager 828 might be an
employee of the insurance company.
[0078] FIG. 9 is another block diagram that presents a computer
system 900 in a somewhat more expansive or comprehensive fashion
(and/or in a more hardware-oriented fashion). The computer system
900, as depicted in FIG. 9, includes a "triage platform" 901 given
that a function of the triage platform 901 is to automatically and
selectively assess newly received long term disability insurance
claims for the insurance company. As seen from FIG. 9, the computer
system 900 may further include a conventional data communication
network 902 to which the triage platform 901 is coupled.
[0079] FIG. 9 also shows, as parts of computer system 900, data
input device(s) 904 and data source(s) 906, the latter (and
possibly also the former) being coupled to the data communication
network 902. The data input device(s) 904 and the data source(s)
906 may collectively include the devices 808, 810 and 812 discussed
above with reference to FIG. 8. More generally, the data input
device(s) 904 and the data source(s) 906 may encompass any and all
devices conventionally used, or hereafter proposed for use, in
gathering, inputting, receiving and/or storing information for
insurance company claim files.
[0080] Still further, FIG. 9 shows, as parts of the computer system
900, personal computers 908 assigned for use by physicians (who may
be associated with the insurance company's long term disability
insurance program) and personal computers 910 assigned for use by
case managers (who might also be associated with team leaders
and/or claim handlers the long term disability insurance program).
The personal computers 908, 910 are coupled to the data
communication network 902.
[0081] Also included in the computer system 900, and coupled to the
data communication network 902, is an electronic mail server
computer 912. The electronic mail server computer 912 provides a
capability for electronic mail messages to be exchanged among the
other devices coupled to the data communication network 902. Thus
the electronic mail server computer 912 may be part of an
electronic mail system included in the computer system 900. The
computer system 900 may also be considered to include further
personal computers (not shown), including, e.g., computers which
are assigned to individual claim handlers or other employees of the
insurance company.
[0082] According to some embodiments, the triage platform 901 uses
a predictive model to facilitate a provisioning of claim handlers.
Note that the predictive model might be designed and/or trained in
a number of different ways. For example, FIG. 10 is a flow chart
illustrating how a predictive model might be created according to
some embodiments. At 1002, data to be input to the predictive model
may be analyzed, scrubbed, and/or cleaned. This process might
involve a broad review of the relevant variables that may be
included in the sample data. Variables might be examined for the
presence of erroneous values, such as incorrect data types or
values that don't make sense. Observations with such "noisy" data
or missing data may be removed from the sample. Similarly, any data
points that represent outliers are also managed.
[0083] At 1004, a data reduction process might be performed. This
might occur, for example, between variables in the data sample
and/or within specific variables. According to some embodiments,
certain variables may be associated with one another and the number
of these variables may be reduced. For example, it might be noted
that injuries to the left shoulder generally have values similar to
injuries to the right shoulder. Within certain variables, the raw
values may represent a level of information that is too granular.
These raw values might then be categorized to reduce the
granularity. A goal of the data reduction process may be to reduce
the dimensionality of the data by extracting factors or clusters
that may account for the variability in the data.
[0084] At 1006, any necessary data transformations may be
performed. Transformations of dependent and/or independent
variables in statistical models can be useful for improving
interpretability, model fit, and/or adherence to modeling
assumptions. Some common methods may include normalizations of
variables to reduce the potential effects of scale and dummy coding
or other numeric transformations of character variables.
[0085] Once these steps are complete, the predictive model may be
developed at 1008. Depending on the nature of the desired
prediction, various modeling techniques may be utilized and
compared. The list of independent variables may be narrowed down
using statistical methods as well as business judgment. Lastly, the
model coefficients and/or weights may be calculated and the model
algorithm may be completed. For example, it might be determined
that back injuries require a high degree of management (and thus,
according to some embodiments, a back injury might be weighted more
as compared to a shoulder injury and thus be more likely to end up
in a segment associated with claim handlers with light
workloads).
[0086] Note that many different types of data might be used to
create, evaluate, and/or use a predictive model. For example, FIG.
11 is a block diagram of a system 1100 illustrating inputs to a
predictive model 1110 according to some embodiments. In this
example, the predictive model 1110 might receive information about
prior long term disability insurance claims 1120 (e.g., historical
data). Moreover, the predictive model 1110 might receive monetary
information about claims 1130 (e.g., a total amount of payments
made in connection with a claim) and/or demographic information
1140 (e.g., the age or sex of a claimant). According to some
embodiments, claim notes 1150 are input to the predictive model
1110 (e.g., and keywords may be extracted from the notes 1150).
Other types of information that might be provided to the predictive
model 1110 include medical bill information 1160 (e.g., including
information about medical care that was provided to a claimant),
disability details 1170 (e.g., which part or parts of the body have
been injured), and employment data 1180 (e.g., an employee's salary
or how long an employee has worked for an employer).
[0087] The predictive model 1110, in various implementation, may
include one or more of neural networks, Bayesian networks (such as
Hidden Markov models), expert systems, decision trees, collections
of decision trees, support vector machines, or other systems known
in the art for addressing problems with large numbers of variables.
Preferably, the predictive model(s) are trained on prior data and
outcomes known to the insurance company. The specific data and
outcomes analyzed vary depending on the desired functionality of
the particular predictive model 1110. The particular data
parameters selected for analysis in the training process are
determined by using regression analysis and/or other statistical
techniques known in the art for identifying relevant variables in
multivariable systems. The parameters can be selected from any of
the structured data parameters stored in the present system,
whether the parameters were input into the system originally in a
structured format or whether they were extracted from previously
unstructured text.
[0088] The present invention has been described in terms of several
embodiments solely for the purpose of illustration. Persons skilled
in the art will recognize from this description that the invention
is not limited to the embodiments described, but may be practiced
with modifications and alterations limited only by the spirit and
scope of the appended claims.
* * * * *