U.S. patent application number 11/672423 was filed with the patent office on 2008-06-19 for evidence-based medicine supercharger.
This patent application is currently assigned to Accenture Global Services GMBH. Invention is credited to David H. Kil.
Application Number | 20080147440 11/672423 |
Document ID | / |
Family ID | 39204740 |
Filed Date | 2008-06-19 |
United States Patent
Application |
20080147440 |
Kind Code |
A1 |
Kil; David H. |
June 19, 2008 |
Evidence-Based Medicine Supercharger
Abstract
Apparatuses, computer media, and methods for supporting health
needs of a consumer by processing input data. An integrated health
management platform supports the management of healthcare by
obtaining multi-dimensional input data for a consumer, determining
a health-trajectory predictor from the multi-dimensional input
data, identifying a target of opportunity for the consumer in
accordance with the health-trajectory predictor, and offering the
target of opportunity for the consumer. An outcome study for a
medical treatment from a medical publication is detected. The
medical treatment is mapped to a diagnostic and procedural code and
a database for health data is accessed using the diagnostic and
procedural code. An outcome metric for the medical treatment with a
consumer group is associated, and a utility function is generated
from the plurality of outcome metrics, where the utility function
gauges an efficacy of at least one intervention channel for a
consumer.
Inventors: |
Kil; David H.; (Santa Clara,
CA) |
Correspondence
Address: |
BANNER & WITCOFF, LTD.;ATTORNEYS FOR CLIENT NO. 005222
10 S. WACKER DRIVE, 30TH FLOOR
CHICAGO
IL
60606
US
|
Assignee: |
Accenture Global Services
GMBH
Schaffhausen
CH
|
Family ID: |
39204740 |
Appl. No.: |
11/672423 |
Filed: |
February 7, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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11612763 |
Dec 19, 2006 |
|
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11672423 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
G16H 50/30 20180101;
G16H 20/00 20180101; G06Q 40/08 20130101; G16H 50/20 20180101; G06F
19/00 20130101 |
Class at
Publication: |
705/2 |
International
Class: |
G06Q 50/00 20060101
G06Q050/00 |
Claims
1. An apparatus for processing medical literature comprising: a
text mining module detecting an outcome study for a medical
treatment from a medical publication; a rule induction module
mapping the medical treatment to a diagnostic and procedural code;
a data analyzer configured to perform: (a) accessing a database for
health data using the diagnostic and procedural code, the health
data spanning previous treatment for a population of consumers; (b)
associating an outcome metric for the medical treatment with a
consumer group; and (c) repeating (a) and (b) to determine another
outcome metric for another consumer group; and a utility generator
generating a utility function from the plurality of outcome
metrics, the utility function gauging an efficacy of at least one
intervention channel for a consumer.
2. The apparatus of claim 1, the text mining module detecting
another outcome study for another medical treatment.
3. The apparatus of claim 1, the data analyzer determining the
consumer group by clustering a subset of the population from at
least one attribute of the population to form a cluster.
4. The apparatus of claim 3, the utility generator determining the
utility function that relates a utility score for a selected
intervention channel as applied to the cluster.
5. The apparatus of claim 4, the utility generator determining
another utility score for another intervention channel as applied
to another cluster.
6. The apparatus of claim 1, further comprising: a confirmation
interface presenting an abstract of the medical publication to a
user and receiving, from the user, a notification that includes a
confirmation of the abstract.
7. The apparatus of claim 6, the confirmation interface editing the
abstract that is presented to the user.
8. The apparatus of claim 3, the data analyzer comparing a cluster
dispersion measure of the cluster to a overall dispersion measure
of the population and accepting the cluster from a ratio of the
overall dispersion measure to the cluster dispersion measure.
9. The apparatus of claim 8, the data analyzer comparing the
cluster dispersion measure and overall dispersion measure for each
outcome distribution.
10. A method for processing medical literature comprising: (a)
detecting an outcome study for a medical treatment from a medical
publication; (b) mapping the medical treatment to a diagnostic and
procedural code; (c) accessing a database for health data using the
diagnostic and procedural code, the health data spanning previous
treatment for a population of consumers; (d) associating an outcome
metric for the medical treatment with a consumer group; and (e)
repeating (c) and (d) to determine another outcome metric for
another consumer group; and (f) generating a utility function from
the plurality of outcome metrics, the utility function gauging an
efficacy of at least one intervention channel for a consumer.
11. The method of claim 10, further comprising: (g) detecting
another outcome study for another medical treatment.
12. The method of claim 10, further comprising: (g) determining the
consumer group by clustering a subset of the population from at
least one attribute of the population to form a cluster.
13. The method of claim 12, further comprising: (h) determining the
utility function that relates a utility score for a selected
intervention channel as applied to the cluster.
14. The method of claim 13, further comprising: (i) determining
another utility score for another intervention channel as applied
to another cluster.
15. The method of claim 10, further comprising: (g) presenting an
abstract of the medical publication to a user receiving, from the
user, a notification that includes a confirmation of the
abstract.
16. The method of claim 15, further comprising: (h) editing the
abstract that is presented to the user.
17. The method of claim 12, further comprising: (h) comparing a
cluster dispersion measure of the cluster to a overall dispersion
measure of the population; and (i) accepting the cluster from a
ratio of the overall dispersion measure to the cluster dispersion
measure.
18. The method of claim 17, further comprising: (j) comparing the
cluster dispersion measure and overall dispersion measure for each
outcome distribution.
19. A computer-readable medium having computer-executable
instructions to perform: (a) detecting an outcome study for a
medical treatment from a medical publication; (b) mapping the
medical treatment to a diagnostic and procedural code; (c)
accessing a database for health data using the diagnostic and
procedural code, the health data spanning previous treatment for a
population of consumers; (d) associating an outcome metric for the
medical treatment with a consumer group; and (e) repeating (c) and
(d) to determine another outcome metric for another consumer group;
and (f) generating a utility function from the plurality of outcome
metrics, the utility function gauging an efficacy of at least one
intervention channel for a consumer.
20. The computer-readable medium of claim 19, further configured to
perform: (g) determining the consumer group by clustering a subset
of the population from at least one attribute of the population to
form a cluster.
21. The computer-readable medium of claim 20, further configured to
perform: (h) determining the utility function that relates a
utility score for a selected intervention channel as applied to the
cluster.
22. The computer-readable medium of claim 20, further configured to
perform: (h) comparing a cluster dispersion measure of the cluster
to a overall dispersion measure of the population; and (i)
accepting the cluster from a ratio of the overall dispersion
measure to the cluster dispersion measure.
Description
[0001] This application is a continuation of common-owned,
co-pending U.S. application Ser. No. 11/612,763 ("Integrated Health
Management Platform") filed on Dec. 19, 2006 naming David H. Kil,
the entire disclosure of which is hereby incorporated by
reference.
FIELD OF THE INVENTION
[0002] This invention relates generally to healthcare management.
More particularly, the invention provides apparatuses, computer
media, and methods for supporting health needs of a consumer by
processing input data.
BACKGROUND OF THE INVENTION
[0003] The U.S. healthcare industry is a $2T economy with the rate
of growth far exceeding that of general inflation. With the aging
global population, the current healthcare crisis is expected to
worsen, threatening the health of global economy. The existing
healthcare ecosystem is zero-sum. The recent pay-for-performance
(P4P) experiment by the National Health Services in the United
Kingdom resulted in mixed outcomes with incentive-based payments
far exceeding the budget with uncertain improvements in patient
health. On the other hand, a recent study on the sophistication of
healthcare consumers reveals that there is little correlation
between consumers' perception of care and the actual quality of
healthcare delivered as measured by RAND's 236 quality indicators.
Furthermore, given the high churn rate and the propensity of
employers to seek the lowest-cost health plan, payers are motivated
to focus primarily on reducing short-term cost and carving out the
cream-of-the-crop population, resulting in perverse benefit
design.
[0004] In healthcare, predictive models are used to improve
underwriting accuracies and to identify at-risk members for
clinical programs, such as various condition-centric disease
management programs. Unfortunately, predictive models typically use
year-1 payer claims data to predict year-2 cost. Some predictive
modeling vendors predict future inpatient or emergency-room
episodes since they represent high-cost events. The emphasis on
cost makes sense given that the impetus for predictive models came
from private and government payers struggling with rising
healthcare costs.
[0005] Evidence-based medicine (EBM) is an attempt to apply
scientific evidence to making care decisions for patients. A lot of
EBM guidelines are derived from medical journals, where teams of
researchers rely on randomized controlled trials and observational
studies to draw inferences on the efficacy of various treatments on
carefully selected patient populations. Pharmacovigilance or study
of adverse drug reactions is an example of EBM.
[0006] Current EBM vendors, such as Active Health Management, a
wholly owned subsidiary of Aetna, and Resolution Health, rely on a
team of physicians reading and codifying relevant medical journals.
The resulting EBM database is applied to population claims data
consisting of medical, Rx, and lab claims data in order to identify
patients not receiving proper EBM guidelines, i.e., with "gaps" in
treatment. Physicians of the identified patients are contacted
through faxes or telephone calls with instructions or
recommendations on how to close the gaps in treatment. A number of
shortcomings exist with the current EBM implementation. Many EBM
studies suffer from small sample size, thus making generalization
difficult and sometimes inaccurate. A corollary of the first
shortcoming is that most EBM studies are at a selected population
level and do not provide drilldown information at a sub-population
level. That is, if not everyone benefits from an EBM guideline, it
may be dangerous to apply the guideline to the entire study
population, which begs for a careful tradeoff between specificity
and sensitivity. Guidelines typically do a poor job of translating
study outcomes into metrics that end stakeholders care about. For
example, payers pay a particular attention to cost, which is not
the same as improving surrogate endpoints that are therapeutic in
nature with various time frames for healing or outcomes
improvement. Publication bias and conflicting results encourage ad
hoc decision making on the part of payers in the area of
utilization management, such as coverage denials and medical
necessity reviews. Furthermore, relying on published guidelines
discourages the use of autonomous or loosely guided search for
anomalies or precursors to adverse outcomes using a large of amount
of integrated data assets and intelligent search algorithms based
on machine learning.
[0007] Clearly, there is a desperate need for an integrated
solution for providing healthcare management.
BRIEF SUMMARY OF THE INVENTION
[0008] The present invention provides apparatuses, computer media,
and methods for supporting health needs of a consumer by processing
input data.
[0009] With one aspect of the invention, an integrated health
management platform supports the management of healthcare by
obtaining multi-dimensional input data for a consumer, determining
a health-trajectory predictor from the multi-dimensional input
data, identifying a target of opportunity for the consumer in
accordance with the health-trajectory predictor, and offering the
target of opportunity for the consumer. Multi-dimensional input
data may include claim data, consumer behavior marketing data,
self-reported data, and biometric data.
[0010] With another aspect of the invention a consumer is assigned
to a cluster or clusters based on the multi-dimensional input data.
A characteristic of the consumer may be inferred from a subset of
the multi-dimensional input data.
[0011] With another aspect of the invention, a cluster is
associated with a disease progression, where the cluster is
associated with at least one attribute of a consumer. A target of
opportunity is determined from the cluster and the disease
progression. An impact of the target of opportunity may be assessed
by delivering treatment to a consumer at an appropriate time.
[0012] With another aspect of the invention, a target of
opportunity is extracted from medical information using a set of
rules for the multi-dimensional input data.
[0013] With another aspect of the invention, a previous event that
occurred before a subsequent transition event is identified. A
correlation between the previous event and the subsequent
transition event is measured from historical data to assign
multidimensional strength or utility indicators to a discovered
rule.
[0014] With another aspect of the invention, an enrollment
healthcare selection for the consumer is recommended based on
multi-dimensional input data.
[0015] With another aspect of the invention, an outcome study for a
medical treatment from a medical publication is detected. The
medical treatment is mapped to a diagnostic and procedural code and
a database for health data is accessed using the diagnostic and
procedural code. An outcome metric for the medical treatment with a
consumer group is associated, and a utility function is generated
from the plurality of outcome metrics, where the utility function
gauges an efficacy of at least one intervention channel for a
consumer.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The present invention is illustrated by way of example and
not limited in the accompanying figures in which like reference
numerals indicate similar elements and in which:
[0017] FIG. 1 shows an architecture of an integrated health
management (IHM) platform in accordance with an embodiment of the
invention.
[0018] FIG. 1A shows a Service Oriented Architecture (SOA)
framework in accordance with an embodiment of the invention.
[0019] FIG. 2 shows a method of determining multimode
health-trajectory predictors in accordance with an embodiment of
the invention.
[0020] FIG. 3 shows a flow diagram for an evidence-based medicine
supercharger in accordance with an embodiment of the invention.
[0021] FIG. 4 shows a flowchart for an autonomous healthcare data
exploration system in accordance with an embodiment of the
invention.
[0022] FIG. 5 shows an illustrative conceptual example of the
optimal health benefit design in accordance with an embodiment of
the invention.
[0023] FIG. 6 shows an example of Markov modeling of assessing a
target of opportunity in accordance with an embodiment of the
invention.
[0024] FIG. 7 shows computer system 100 that supports an embodiment
of the invention.
DETAILED DESCRIPTION OF THE INVENTION
Integrated Health Management Platform in a Service Oriented
Architecture (SOA) Framework
[0025] FIG. 1 shows an architecture of integrated health management
(IHM) platform 100 in accordance with an embodiment of the
invention. IHM platform 100 creates for payers a virtuous circle of
integrated informatics leading to improved and real-time decision
making leading to healthier members leading to improved
profitability and cost savings leading to improved market share.
For consumers who must share an increasing burden of medical costs,
the execution of IHM platform may lead to improved health and
subsequent cost savings. In the following discussion, a consumer
may be an employee of a company or an individual choosing a
healthcare plan from a plurality of plans and consuming
products/services from various healthcare stakeholders. An
objective of the consumer is to maximize benefits associated with
good health by choosing a healthcare plan that is "best" for the
individual or his/her family and improving health through timely
preventive and proactive health actions.
[0026] The IHM platform consists of the following four components:
[0027] 1. Multimode health-trajectory predictors module 101:
Instead of focusing on predicting future cost alone using claims
data as most predictive models do now, multimode health-trajectory
predictors leverage claims data 153, self-reported data 151, and
consumer behavior marketing data 155, coupled with inference
engines 115, to provide a comprehensive set of future attributes
useful to assess the level of impact through various
consumer-engagement channels. Claims data 153 may include medical
claims, pharmacy claims, prior authorization, and lab results
(e.g., blood tests) for a consumer. Consumer-engagement channels
may encompass secure e-mails, Interactive Voice Recording (IVR)
calls, cellphone text messages, and nurse calls. Data Merge &
Cleaning 109 performs extract-transform-load (ETL) of disparate
data assets to form a consumer-centric view while cleaning data
prior to weak-signal transformation through digital signal
processing (DSP) and feature extraction 111. Disease clustering and
progression module 113 subsequently forms disease clusters and
estimates disease progression probabilities. Clustering
optimization & inference 115 performs clustering using
attributes that are meaningful from the perspective of predicting
future health trajectories and impactability with the inference
engine filling in unobserved variables using the instantiated
variables. A modular predictive model is developed for each
consumer cluster so that a collection of locally optimized
predictive models can provide a globally optimal performance 117.
Finally, a set of health scores encompassing health scores,
behavior/lifestyle scores, engagement scores, impact scores,
data-conflict scores, cost scores, and clinical scores is output
119. [0028] 2. Targets-of-opportunity finder 103: Leveraging
consumer-understanding technologies, an evidence-based-medicine
(EBM) supercharger (shown as EBM supercharger 300 in FIG. 3), and
an autonomous insight crawler, one can identify targets of
opportunities in various consumer touch points. The four major
opportunities lie in clinical gaps 121, treatment adherence 123,
lifestyle/behavior 125, and psychosocial parameters 126. Impact
assessment is made based on the aggregate future impact of all the
identified targets of opportunities 127. [0029] 3.
Resource-allocation manager 105: Resource-allocation manager (RAM)
105 funnels the right members to the right consumer touch points at
the right time by maximizing multi-objective functions. Also
included in RAM 105 are consumer-understanding technologies and
iterative benefit design borrowing salient concepts from adaptive
conjoint analysis, predictive modeling, and Pareto multi-objective
optimization. Furthermore, mixing-in currently available
technologies into consumer touch points in conjunction with dynamic
progressive content tailoring allows one to go beyond the typical
nurse-based care model, which is inherently not scalable especially
with the projected worsening nurse shortage in the labor market.
(Resource-allocation manager 105 is Pareto efficient if no consumer
can be made better off without another consumer being made worse
off.) The fundamental idea here is building a multi-objective
constrained optimization engine 137 as a function of consumer,
intervention-channel, benefit-program profiles (129, 131, 135) and
utility functions 133 derived from the impact analysis engine.
[0030] 4. Impact-analysis engine 107: This module tells one what
works for which population segments, by how much, and why in a
drilldown mode. It facilitates the use of utility functions in the
framework of resource-allocation optimization as done in defense
battlefield resource management. The methodology employed uses
predictive modeling, combinatorial and stochastic feature
optimization with respect to outcomes, and propensity-score
shaping. After selecting candidate population for analysis 141, one
performs thorough matching in the two-dimensional space of
propensity and predictive scores 143 to create control and
intervention groups 145 for an "apple-to-apple comparison." One
then create rules of engagement for statistically significant
outcomes, which are further validated through focus-group study 149
and survey using the minimum number of necessary questions 148.
Validated rules 150 are stored in the master rules database for
production implementation. [0031] The four above components 101-107
complement one another and are ideally suited to assessing the
incremental benefits of bringing new data assets and business
processes into enterprise operations. In order to facilitate
integration into and compatibility with typical payer enterprise
applications, the IHM implementation (e.g., IMH platform 100)
adheres to an enhanced Service Oriented Architecture (SOA)
framework. A key idea here is maximizing synergy among business
process primitives, data models, and algorithm models so that one
can reduce latency between the generation of actionable knowledge
and its production implementation.
[0032] FIG. 1A shows Service Oriented Architecture (SOA) framework
160 of IHM platform 100 in accordance with an embodiment of the
invention. Framework 160 increases synergy in data models,
mathematical models, and business-process models that are important
in ensuring the success of IHM Platform 100. Inputs 161 consist of
data library 163, algorithm library 165, and business-process
libraries 167, which get updated with the latest discoveries. The
processing layer uses the building blocks of business processes and
algorithms tailored to underlying data models to produce
intermediate processing outputs as well as actionable insights that
feed to multimedia outputs for dissemination to the key
stakeholders.
[0033] Data library 163 includes Consumer Touch Points (CTP) 169,
Utilization Management (UM) 171, Underwriting Questionnaire (UWQ)
173, D&B: Dun & Bradstreet (D&B) database 175,
Electronic Medical Records (EMR) 177, and Health Risk Assessment
(HRA) database 179.
Multimode Health-Trajectory Predictors
[0034] FIG. 2 shows process 200 for determining multimode
health-trajectory predictors in accordance with an embodiment of
the invention. Instead of focusing on cost prediction, multimode
health-trajectory predictors attempt to understand current and
predict transitions in Bayesian relationships among the many
semi-orthogonal outcomes attributes so that one can maximize
positive impact through delivering the right intervention touch
points to the right consumers at the right time before adverse
transitions occur.
[0035] In healthcare, predictive models are used to improve
underwriting accuracies and to identify at-risk members for
clinical programs, such as various condition-centric disease
management programs. Typically, prior art predictive modes predict
year-2 cost using year-1 payer claims data. Some prior art
predictive modeling vendors predict future inpatient or
emergency-room episodes since they represent high-cost events. The
emphasis on cost makes sense given that the impetus for predictive
models came from private and government payers struggling with
rising healthcare costs.
[0036] Focusing on cost alone ignores the complex, multifaceted
nature of healthcare consumers. Knowing future cost with R-sq of
10-25% is different from being able to impact the future health
trajectory of each consumer. For example, it may be more beneficial
to touch John suffering from pre-diabetic conditions with body mass
index (BMI) of 32 than to intervene on behalf of Mark who has to go
through kidney dialysis three times a week because of end-stage
renal disease. From a cost perspective, Mark may be 20-40 times
more expensive. But from an impact perspective, John would be a
better candidate because his current conditions are more amenable
to actions that can be taken now to prevent unpleasant consequences
in the near future.
[0037] As a result of cost emphasis, prior art predictive models
extract a standard set of features from Rx and/or medical claims
data and apply linear regression or neural networks to predict
year-2 cost. Typical features include disease flags and year-1
summary cost and utilization statistics, such as average inpatient
cost per month, average Rx cost per month, # of physician visits
per month, etc. Some predictive models divide the population into
sub-groups using inputs from clinicians with the goal of designing
a model tailored to each sub-group (MedAI). However, it may be
quite difficult to design optimal clusters given the complexities
of and interplays among the many factors that determine future
health trajectories.
[0038] In order to address the shortcomings of the current
generation of predictive models, an embodiment of the invention
incorporates the following concepts: [0039] 1. Use of input data
such as claims data 251, self-reported data 255, consumer behavior
marketing (CBM) data 253, and biometric data 257 is augmented with
inference engine 209 to predict multiple semi-orthogonal attributes
with the goal of finding the best way to engage and motivate
healthcare consumers to create positive impact. Input data is
typically provided by electronic health record (EHR) database 203.
Not everyone will have all the data assets. Therefore, key unknown
variables need to be estimated using inference engine 209. [0040]
2. Flexible-dimension clustering process 211 creates an optimal set
of consumer clusters from an impact perspective instead of using
the same old disease hierarchy to create disease-centric consumer
clusters. [0041] 3. Adaptive hypermedia content creation 221
leverages a comprehensive understanding of consumer needs and how
to best provide a positive impact.
[0042] As shown in FIG. 2, inputs include: [0043] Claims data 251:
It is comprised of Rx/med/lab data, utilization-management (UM)
data including pre-authorization, Rx/med benefit data, program
touch-point data, Web log data, and limited member demographic
data. [0044] Consumer behavior marketing (CBM) data 253: This
externally purchasable data provides inferred behavior, lifestyle,
and attitudinal information on consumers from their demographic
data and credit history. [0045] Self-reported data 255: This
includes health risk assessment (HRA), ecological momentary
assessment (EMA), and experience sampling method (EMA) data
administered through multiple communication channels, such as the
Internet, cellphone, set top box, etc. [0046] Biometric data 257:
This encompasses data from wearable sensors (Bodymedia's
BodyBugg.TM., Nike+ shoe sensor, polar band) and attachable sensors
(glucometer, blood-pressure cuff, spirometer, etc.) transmitted
through wired or wireless networks.
[0047] As shown in FIG. 2, processing includes: [0048] Mixer 201:
Not everyone will have all the data elements. Therefore, mixer 201
organizes incoming data into a schema appropriate for frame-based
dynamical data processing. Furthermore, it differentiates between 0
and an empty set .phi.. [0049] Preprocessing 205: This step
performs secondary data audit and consumer-centric data structure
generation. Primary data audit occurs during data creation in
enterprise data warehouse (EDW). [0050] 1) Data audit: Outliers are
normalized using multi-pass peak-shearing. Multiple debit/credit
entries and ghost claims are eliminated. It looks for potential
gender/age mismatch errors (grandmother or father giving birth to a
baby or a premature baby's neonatal claims being assigned to his or
her parents) using a look-up table. [0051] 2) Consumer-centric data
structure generation: For each consumer, we create an efficient
data structure from memory and processing perspectives. It is a
hierarchical structure encompassing the entire consumer touch-point
suite of channels. [0052] Transform 207: This step creates various
bandpass-filtered maps over time. For instance, International
Classification of Disease (ICD) 9/10 codes from medical claims and
National Drug Codes (NDC) from Rx claims are converted into
hierarchical condition-versus-time maps to facilitate the analysis
of disease progression and the creation of disease clusters.
Moreover, such a representation can help one to infer behavioral
patterns from linking discrete events or following medication
adherence for managing chronic conditions. A combination of ICD and
Current Procedure Terminology (CPT) codes is used to derive
Milliman & Robertson (M&R) categories over time, which is
useful in assessing the utilization of various service types
(inpatient, outpatient, emergency room, physician office visit,
etc.) over time. Biometric data is processed through a large number
of transformation algorithms, such as the fast Fourier transform,
wavelet transform, local cosine transform, ensemble interval
histogram, etc., in order to glean locally dynamic behaviors over
time. Due to the infrequent nature of HRA and CBM data (i.e.,
people do not change their behavior or lifestyle every hour),
locally dynamic behaviors serve as anchor points that vary much
more slowly so that one can investigate the cumulative effects of
linked local events over time on behavior change. The entire
transformation process is analogous to multi-rate signal
processing. At the end of transform, we extract a large number of
static and dynamic features from each transformation space, as well
as higher-order linked attributes spanning multiple transformation
spaces in order to glean insights into disease clustering, disease
progression, and their interplay with the consumer's psychosocial
behavioral traits. [0053] Inference engine 209: Knowing certain
unobserved traits can be quite useful in devising tailored
intervention strategies. Let x.sub.claims, x.sub.CBM, x.sub.SR, and
x.sub.bio represent the four data sets as previously discussed. If
knowing one's body mass index (BMI) is desirable, one first builds
modular predictive models from the sub-population that has BMI data
such that P(BMI|x.sub.claims), P(BMI|x.sub.CBM), etc. constitute a
feasible set of models for predicting BMI conditioned upon having
other data assets. This model can be in the form of Bayesian
networks, regression or classification algorithms leveraging
parametric and non-parametric learning algorithms. [0054]
Flexible-dimension clustering 211: This is an iterative process
leveraging multiple fitness functions and predictive models as part
of clustering. This step generates a set of clusters for each
outcomes variable such that the output dispersion compression is
maximized for improved prediction accuracy. [0055] 1) For each
outcomes variable, one performs feature optimization to find a
sufficient-statistics feature subset. [0056] 2) One performs
clustering using k-means, expectation-maximization (EM), and
Kohonen's self-organizing feature map. After clustering, there are
N.sub.C clusters for each outcomes variable. For each cluster, one
calculates the dispersion .sigma..sub.i, i=1, . . . , N.sub.C of
each of the outcomes distributions and compare it with the overall
dispersion .sigma..sub.T from the entire population. The
dispersion-compression ratio (DCR)
r.sub.i=.sigma..sub.T/.sigma..sub.i>.gamma., where .gamma.>1,
is a predetermined dispersion-compression threshold for accepting
the i.sup.th cluster based on its ability to compress the outcomes
distribution. One creates a set of samples that pass the DCR test.
[0057] 3) For the samples that do not pass the first DCR test,
repeat steps 1-2 until there is no sample left or the number of
remaining samples is less than the minimum sample size. [0058]
Automatic model calibration 215: In real-world problems, data
characteristics remain rarely stationary over time. With process
200, step 213 determines whether training is needed to update
process 200 for new medical developments. For example, introduction
of new medical technologies and drugs, changes in benefit plans and
fee-reimbursement schedules, changing demographics, and even
macroeconomic cycles can affect data characteristics. Built-into
the automatic model calibration algorithm 215 is a data-mismatch
estimator that keeps track of statistical parameterization of key
data assets over overlapping time frames and consumer clusters
after removing secular trends, e.g., medical-cost inflation. Model
parameters are updated and stored in model parameters database 217.
During model initialization and subsequent re-calibration, the
following takes place: [0059] 1) Perform preprocessing step 205,
transform step 207, inferring step 209, and flexible dimension
clustering step 211 [0060] 2) Feature optimization for each
consumer cluster and outcomes variable using combinatorial and
stochastic algorithms [0061] 3) Model performance tuning to find
the point of diminishing returns [0062] 4) Multiple-model combining
[0063] Multiple-model scoring 219: Once process 200 has been
trained, multiple-model scoring 219 is performed for input data
251-257. One generates the following health scores: [0064] 1)
Health scores as a function of current chronic conditions and
predicted disease progression [0065] 2) Behavior and lifestyle
scores computed heuristically as a function of reported, observed
(medication adherence, frequent ER visits, the level of interaction
with care-management nurses, etc.), and inferred behavior and
lifestyle attributes [0066] 3) Engagement scores as a function of
reported, observed, and inferred psychosocial and
collaborative-filtering parameters [0067] 4) Impact scores working
in concert with evidence-based-medicine (EBM) supercharger 300 and
utility functions associated with targets of opportunities and
derived from the impact-analysis engine [0068] 5) Conflict scores
as a function of discrepancies between reported and observed
behavioral/lifestyle factors and claims data [0069] 6) Cost scores
for multiple future time periods in chronic vs. acute categories
[0070] 7) Clinical utilization scores in terms of inpatient,
emergency room/urgent care centers, medication, etc. [0071]
Adaptive hypermedia content generation 221: This module generates a
tailored report of 1-2 pages succinctly summarizing current health
conditions, likely future states, targets of opportunities, action
plan, and benefits with drilldown menu.
[0072] In accordance with an embodiment of the invention, process
200 provides outputs including: [0073] Consumer-centric metadata
for a comprehensive view (both tabular and scientific
visualization) of each consumer with appendages linking
consumer-centric metadata to various stakeholders to facilitate
stakeholder-centric data transformation [0074] Health scores [0075]
Adaptive hypermedia content tailored to each consumer
Evidence-Based Medicine Supercharger
[0076] FIG. 3 shows a flow diagram for evidence-based medicine
(EBM) supercharger 300 in accordance with an embodiment of the
invention. From an EBM guideline or a medical journal article 351,
evidence-based-medicine supercharger 300 generates a set of
multidimensional inferred and observed utility functions, which is
an essential ingredient in developing optimal resource allocation
strategies. The utility function can be multidimensional at
multiple levels of granularity in terms of patient or consumer
clusters, leading to an M.times.N matrix, where M and N represent
the number of utility components or objectives and the number of
consumer clusters, respectively._For example, consumer clusters
generated from the health-trajectory predictors may encompass the
following groups: (1) those who are generally healthy from a claims
perspective, but exhibit poor health habits in terms of high BMI
and "couch-potato" characteristics; (2) those who suffer from
chronic illnesses amenable from a lifestyle intervention, such as
diabetes and cardiovascular disease; (3) people who have multiple
co-morbid conditions, but one cannot find treatment-related claims
records (N=3). From a segmented drilldown impact analyses of three
intervention channels (Interactive Voice Response (IVR), health
behavior coaching, and case management (M=3)), one determines that
the most effective intervention channels for the three population
clusters are (IVR, health behavior coaching), (health behavior
coaching), (case management and health behavior coaching),
respectively. The utility function is a 3.times.3 matrix, where
each element x.sub.ij contains a utility score or return on
investment for the i.sup.th intervention channel applied to the
j.sup.th consumer cluster.
[0077] In accordance with an embodiment of the invention,
evidence-based-medicine supercharger 300 includes: [0078] Input
databases: [0079] 1) EBM database 317: It consists of EBM rules,
taxonomy for inducing rule parameters from medical journal,
population parameters, rule strength, mapping look-up tables that
map condition and drug names to ICD-9 and NDC, respectively, and
utility function. Population parameters encompass inclusion and
exclusion criteria. Rule strength is a function of publication rank
using a page-ranking algorithm, author prestige based on the number
of connections in the publication network, journal prestige based
on the number of circulation, sample size, percentage of total cost
affected, longitudinal duration, and the number of corroborating
articles. The EBM taxonomy facilitates efficient induction of
EBM-rule parameters from an exemplary journal abstract as shown in
the Appendix. More algorithmic details will be discussed in the
processing-algorithm subsection. [0080] 2) Electronic Health
Records (EHR) 203: This database contains claims data 251,
self-reported data 255, and consumer behavior marketing (CBM) data
253. [0081] Processing algorithms [0082] Text mining 301: The
Appendix shows a semi-structured abstract from an article published
in the New England Journal of Medicine. Instead of using a
bag-of-words or natural-language-processing feature vector and a
Naive Bayes classifier to rank an abstract, one simply detects
whether an abstract reports an outcomes study or not. This is a
much easier problem and defers the strength-of-evidence
classification until after integrated outcomes analysis. Next, one
uses a combination of key words, tf*idf text weights (in which the
importance of a word is based on its frequency of occurrence in a
document and normalized by its natural frequency of occurrence in a
corpus) with stemming and stop words, and distance measures from
key words to fill in the hierarchical tree EBM database fields in
the areas of: [0083] 1) Type of outcomes research [0084] 2) Patient
characteristics: size, dropout rate (if available), characteristics
in terms of inclusion and exclusion criteria, longitudinal
duration, and trigger criteria [0085] 3) Reported results The
distance measures are necessary to leverage lexical analysis to
understand higher-level relations and concepts between words in a
sentence or a paragraph. [0086] Automatic EBM rule induction 303:
Given the EBM database fields extracted from a medical journal, one
uses secondary look-up tables to map drug names, diagnoses, and
procedures onto NDC, ICD-9, CPT-4, and laboratory codes commonly
used in claims-payment systems. [0087] Human-Computer Interface
(HCI) for human confirmation 305: The induced EBM rule along with
the highlighted abstract is presented to a clinician for final
confirmation with or without edit. [0088] EBM population
identification 307: One identifies potential control and
intervention populations using the inclusion, exclusion, and
trigger criteria. The presence or absence of the trigger criteria
assigns a patient to the intervention or control group,
respectively, provided that the patient satisfies the inclusion and
exclusion criteria. [0089] Dual-space clustering 309: This step
creates meaningful consumer clusters that are homogeneous in the
optimized baseline-period-attribute-and-outcomes (y) vector space.
The baseline period equals the pre-intervention period of a fixed
duration [0090] 1) For each EBM guideline, one builds models that
predict various outcomes metrics. Associated with each predictive
model is an optimal feature subset (X.epsilon.R.sup.N, where N is
the optimal feature dimension) derived from a combination of
stochastic and combinatorial optimization algorithms. [0091] 2) In
the vector space spanned by X, one performs clustering using
k-means, expectation-maximization (EM), and Kohonen's
self-organizing feature map algorithms. After clustering, there are
N.sub.C clusters. For each cluster, one calculates the dispersion
.sigma..sub.i, i=1, . . . , N.sub.C of each of the outcomes
distributions and compare it with the overall dispersion
.sigma..sub.T from the entire population. The
dispersion-compression ratio (DCR)
r.sub.i=.sigma..sub.T/.sigma..sub.i>.gamma., where .gamma.>1,
is a predetermined dispersion-compression threshold for accepting
the i.sup.th cluster based on its ability to compress the outcomes
distribution for more precision in applying EBM from an outcomes
perspective. One creates a set of accepted samples for which
clusters in X are sufficiently precise for performing integrated
outcomes analysis. One selects the clustering algorithm that
provides the highest DCR. [0092] 3) For the remaining population
samples, perform feature optimization to derive a new optimal
feature subset X.sup.(k). Compress X.sup.(k) into X.sub.c
(dim(X.sub.c)<<dim(X.sup.(k))) using linear discriminant
analysis (LDA) and discretized outcomes metrics should they be
continuous. Next, perform clustering in the vector space spanned by
X.sub.c and y. Prior to clustering, normalize the vector space so
that mean and standard deviation of each component will be 0 and 1,
respectively. The standard deviation of y can be higher to reflect
its importance in determining clusters. Keep the clusters whose
DCRs>1. [0093] 4) For the remaining clusters, repeat step iii
until the number of remaining samples is below the minimum
threshold, i.e., (k).fwdarw.(k+1). The final remaining samples
represent the final cluster. [0094] Integrated outcomes analysis
313: For each cluster, perform case-controlled impact analysis
leveraging predictive and propensity-score models to account for
both regression to the mean and selection bias. A comprehensive set
of outcomes metrics encompasses both observed and inferred
variables. For the inferred variables, we estimate individual and
cluster prediction accuracies so that we can assess the level of
statistical significance as a function of cluster size and model
accuracy. [0095] Utility function generation 315: Finally we
generate a set of utility functions. [0096] 1) Two-dimensional
marginal utility functions over individual outcomes metrics and
population clusters [0097] 2) One-dimensional utility function over
a composite outcomes metric with weights [0098] 3) Pareto Frontier
set for multiple outcomes metrics based on a user-defined
multi-objective function
[0099] Outputs of evidence-based-medicine supercharger 300 include:
[0100] Utility functions tailored to each stakeholder, a composite
outcomes metric, or multi-objective optimization or
Pareto-efficient plots [0101] Outcomes metrics
Autonomous Healthcare Data Exploration System
[0102] FIG. 4 shows a flowchart for autonomous healthcare data
exploration system 400 in accordance with an embodiment of the
invention. Autonomous healthcare data exploration system 400
explores healthcare database to look for "interesting"
relationships autonomously using various signal processing and data
mining algorithms. There is often substantial hidden insight in
healthcare data that can be discovered. Autonomous data exploration
is sometimes associated with fraud detection. In healthcare, gaming
or exploitation of loopholes in fee-reimbursement policies can be a
serious problem, which has led to utilization management or medical
necessity review by payers. For example, one study reports that 39%
of physicians surveyed use at least one of the following three
gaming methods:
1. Exaggerating the severity of patients' conditions
2. Changing patients' billing diagnoses
3. Reporting signs or symptoms that patients didn't have
[0103] Fraud detection has been around for over two decades in a
myriad of forms. It typically looks for outliers or uses models
learned from labeled training data to identify suspicious
activities for human confirmation. The two most widely used areas
are in credit-card and financial industries. The U.S. Securities
and Exchange Commission (SEC) and research boutique firms pore
through tick-by-tick financial data to look for anomalous trading
patterns that can signal insider trading.
[0104] Just to illustrate the difficulty of transitioning
commercial antifraud solutions to healthcare, the U.S. Government
Accountability Office reports that instead of adopting commercially
available antifraud software to Medicare use, the Health Care
Financing Administration (HCFA) chose to enter into a multi-year
agreement with the Los Alamos National Laboratory, citing numerous
difficulties with adopting commercial software. Unfortunately, no
such software--commercial or custom-built--is in widespread use
today.
[0105] The focus on fraud pits one stakeholder against another when
outright fraud is relatively rare, and a soft form of exploiting
system loopholes is more common in healthcare. Therefore, there is
a need for a more sophisticated and less demeaning system focused
on learning hidden causal relations between treatment and health
outcomes (both positive and negative) so as to gain the widest
possible acceptance from all the stakeholders.
[0106] FIG. 4 shows the flowchart of autonomous healthcare data
exploration system 400, which leverages multimode health-trajectory
predictors along with a consumer-centric database 401. Autonomous
healthcare data exploration system 400 includes the following
components: [0107] Inputs [0108] Consumer-centric database (CCDB)
401 consisting of membership, benefit-plan history,
consumer-touch-point history, claims, self-reported, consumer
behavior marketing, provider, and evidence-based medicine data
[0109] Autonomous knowledge database, which is empty in the
beginning, but will be populated with new and iteratively refined
knowledge [0110] Processing [0111] Projection 403: This step
creates multiple projections of CCDB 401 over time so that one has
a complete view of all that's happening to each consumer
conditioned upon slowly-changing lifestyle, behavior, and
psychographic parameters. [0112] Overlapped frame feature
extraction 405: From each time frame of each projection space, one
extracts an appropriate number of summarization and dynamic
features so that we can track their trajectories over time. [0113]
Multimode health-trajectory predictors 407: Predictors 407 predict
future states of one's health around disease progression,
engagement, and impact. [0114] Past-future dynamic clustering 409:
Clustering is performed on the vector space spanned by the current
set of features and predicted attributes. In one embodiment of such
a system, the current set of features encompasses the
parameterization of current disease conditions, utilization of
medical resources, and lifestyle/health behavior. Predicted
attributes may include disease progression, the level of
impactability, and future cost. The key idea is to cluster
consumers based on both where they are today and where they are
likely to transition to in the future. [0115] Anomalous cluster
detection and merging 411: Within each homogeneous cluster, one
looks for outliers in joint and marginal spaces. Depending on the
outlier-population size derived from each cluster, one merges
outliers from multiple similar clusters to improve statistical
power and significance. [0116] Outcomes analysis 413: For each
outlier cluster, one looks for attributes with commonality and
differences between outliers and normal cases. This search for
common and uncommon attributes facilitates case-controlled outcomes
analysis with drilldown along with the understanding of factors
responsible for differences in outcomes. [0117] Causal pathway
analysis 415: For each anomaly case identified, one uses a
structural learning algorithm to induce a Bayesian network
structure. Next, one ensures that causal parameters between control
and test groups move in a logical way. [0118] GUI for human
confirmation 419: Each discovered knowledge is presented to a human
expert for final confirmation and inclusion into the autonomous
knowledge discovery database 417. [0119] Outputs provided by
autonomous healthcare data exploration system 400 include: [0120]
Extracted knowledge
Intelligent Health Benefit Design System
[0121] FIG. 5 shows an illustrative conceptual example of the
optimal health benefit design in accordance with an embodiment of
the invention. An intelligent benefit design system leverages ideas
from consumer-understanding technologies, predictive modeling,
impact analysis, and multi-objective optimization to design an
individually tailored benefit product that balances the conflicting
needs of moral hazard and social insurance by finding the
acceptable ratio of profitability to subsidization for each product
or plan configuration in a product bundle.
[0122] Element 515 in FIG. 5 shows a simplified two-dimensional
efficient frontier in the two-dimensional space of premium and
out-of-pocket (OOP) cost with an indifference curve. That is,
higher premiums are generally associated with lower OOP costs and
vice versa. An insurance company starts out with an initial set of
product bundles 501. If the company introduces a new product for
which no prior enrollment data is available, then the product
enrollment distribution is estimated using adaptive conjoint
learning and prediction 503. On the other hand, if product changes
are evolutionary, then one can use prior enrollment data to develop
and deploy predictive models to estimate the new product enrollment
distribution given an initial set of product attributes 505. As
part of designing an adaptive conjoint analysis (ACA)
questionnaire, one leverages consumer marketing database or
demographic database from the U.S. Census Bureau so that the
questionnaire can be tailored to each consumer 507, 509.
[0123] The fundamental idea is to iterate the process of adjusting
product attributes, estimating product enrollment distributions,
and calculating economic parameters (projected profit/loss as well
as the level of subsidization inherent in a medical insurance
product) of each product bundle so that we achieve an acceptable
trade off between social insurance and moral hazard. That is, while
the young and healthy are supposed to subsidize the cost of
insurance for the old and sick, there needs to be an element of
personal responsibility in benefit design so that people with poor
health habits and beneficiary mentality do not abuse the entire
healthcare system to the detriment of all 511, 513. In short,
benefit design must deal effectively with risk factors that can be
mitigated within socially acceptable means. The plot labeled 517
shows the relationship between individual prediction accuracy
measured in R-sq or R.sup.2 and group prediction accuracy measured
in predictive ratio (PR) mean (.mu.) and standard deviation
(.sigma.). Individual predictive accuracy becomes less important as
group size increases as in employer or group underwriting. However,
in clinical settings and predicting benefit enrollment, where
adverse selection can occur frequently, individual predictive
accuracy is of paramount importance.
[0124] In healthcare, benefit design, according to prior art, is
typically carried out by linking historical utilization and cost
data to various benefit parameters, such as co-pay, deductible,
co-insurance, maximum out-of-pocket, limits on Health Savings
Account/Flexible Spending Account (HSA/FSA), etc. Then a loading
factor (margin) is computed for each plan design, which sets the
premium for the plan. Depending on the premium differential between
plans, subsidization factors are calculated such that a plan
attractive to predominantly the healthy (high-deductible plans) may
subsidize the cost of another plan that appeals primarily to the
sick so that the concept of social insurance can be preserved in
plan design.
[0125] An important consideration in benefit design is risk
management. If benefit parameters are particularly attractive to a
certain segment of population whose medical needs differ
significantly from those of the general population, then such a
plan has a high likelihood of attracting a biased population, which
can lead to unexpected profit or loss depending on the direction of
the bias. Unfortunately for health insurance companies, this
phenomenon of biased population (called anti- or adverse selection)
is not uncommon. The result is a cookie-cutter benefit design with
a small number of selections so that the law of large numbers
dominates the field.
[0126] More recently under the banner of consumer-directed health
plan (CDHP), many payers started introducing high-deductible,
low-premium plans. The theory of the case for CDHP is that
high-deductible plans with some form of medical savings account
will turn beneficiary-mentality patients into sophisticated
healthcare consumers. Unlike other consumer industries, healthcare
consumers may have hard time correlating actual high-quality care
with a perceived one of at least based on RAND's quality metrics.
Furthermore, the initial thrust of CDHP was to attract the
cream-of-the-crop population from employers offering plans from
multiple payers. That is, nimble new-to-the-market payers
introduced CDHP products to employers desperate to cut soaring
health benefit costs. The end result was that dinosaur payers were
saddled with the undesirable segment of the population, hurting
their bottom line.
[0127] Studies suggest that while the young and healthy are
potential winners of CDHP, their opportunities for savings are
limited because of restrictions in plan design, such as portability
and investment. Results of post-CDHP health-resource utilizations
and costs suggest mixed results with no clear trend. Perhaps mixed
results are not surprising given the ambiguity of the theory of the
case.
[0128] Perhaps the biggest shortcoming of the current health plan
design is that few incorporate innovative design parameters, such
as consumer-engagement strategies, incentives for lifestyle
changes, and fun aspects in linking validated
evidence-based-medicine guidelines, nutrition and exercise to
health. Our design approach leverages the estimation of a
consumer-preference function and projected utility functions
derived from the impact analysis engine to move away from a
cookie-cutter design and towards a tailored plan design that
impacts health behavior change.
[0129] For new product launch 501, one first proceeds with adaptive
conjoint questionnaire (ACQ) 503 that is designed to minimize the
number of questions leveraging predictive questionnaire
construction. From ACQ responses, one can estimate a consumer
preference function at an individual level. From a pool of initial
product bundles with preset features, one estimates the overall
enrollment distribution for a group (i.e., an employer). From the
overall enrollment distribution and the outputs of multimode
health-trajectory predictors, one computes profit/loss for each
product and generate a three-dimensional picture of profit/loss and
compressed two-dimensional objectives (i.e., minimize premium and
out-of-pocket or OOP expense) as shown in relationships 515 and
517. This picture will provide visual insights to facilitate the
understanding of Pareto-efficient design parameters, which can lead
to the reconfiguration of product features. This process of
enrollment prediction and product reconfiguration is iterative
until the incremental change in product-feature reconfiguration is
below an acceptable threshold.
[0130] After the product launch, one starts with a fresh data set,
which represents the actual product selection behavior by
consumers. Unlike in conjoint analysis, one does not have
information on exactly which products consumers traded off before
making product-selection decisions. One has the following
information on consumers and their product-selection behavior:
1. Demographics and behavior marketing (x.sub.demo, x.sub.cbm)
2. Prior product selection (x.sub.pps), which doesn't exist for new
consumers
3. Current product selection
[0131] The task at hand is to estimate a revised consumer
preference function using real data. Let y and w denote the
product-selection behavior and product features, respectively.
Then, the estimation task is as follows:
y=f(x.sub.demo,x.sub.cbm,x.sub.pps,w,D(w,y)),
where D(w,y) is a distance function between w and y, and f(.cndot.)
can be estimated using parametric or nonparametric learning
algorithms. Any differences between the conjoint and real-data
models are stored in a database for continuous model adaptation and
learning. More complex design with incentives requires utility
functions associated with incentives from the impact-analysis
engine. After estimating the consumer-preference function, there is
a secondary step of identifying intervention opportunities given
the characteristics of consumers choosing each product bundle.
Based on utility functions and the outputs of the multimode
health-trajectory predictors, the remaining task is to design an
incentive program within each product bundle that will encourage
high-risk members to participate in the program.
[0132] FIG. 6 shows an example of Markov modeling of assessing a
target of opportunity in accordance with an embodiment of the
invention. Markov model 600 shows a disease progression related to
diabetes. Markov model 600 shows the probability of transitioning
from one disease state to another disease state based on whether
the consumer obtains a prescribed treatment. Additionally, disease
states may depend on observed behavioral/lifestyle factors
including the attributes of the consumer. Attributes may include
the category of life style (e.g., "coach-potato") and level of
education of the consumer. The type of treatment and the efficacy
of the treatment may depend on the consumer's attributes.
[0133] With state 601, a consumer, who is a "couch-potato," is
determined to be a pre-diabetic. As determined by intervention
opportunity finder 103 (as shown in FIG. 1), there is a probability
p.sub.1a 609a of the consumer becoming a diabetic (state 603)
without any treatment and a probability p.sub.1 609b if the
consumer received a prescribed treatment (treatment_1). For
example, EBM supercharger 300 may determine that the consumer can
substantially reduce the probability of becoming a diabetic with a
proper diet and exercise regime under the supervision of a
dietician and/or exercise coach.
[0134] When the consumer becomes a diabetic, there is a probability
of developing coronary artery disease (corresponding to state 605).
The corresponding treatment_2 (as determined by EBM supercharger)
may be more radical than treatments. For example, treatment_1 may
include one or more prescribed drugs that are typically more costly
than providing a dietician and/or exercise coach. (In general, as a
disease progresses, the associated costs increase.) The probability
of a diabetic developing coronary arterial disease without
treatment is p.sub.2a 611a and p.sub.2 611b with treatment.
[0135] In accordance with Markov model 600, once a consumer has
developed coronary arterial disease, the consumer may further
develop renal failure and/or congestive heart failure (state 607).
The probability of developing renal failure/congestive failure is
p.sub.3a 113a without treatment is and p.sub.3 611b with
treatment.
[0136] Markov model 600 may include states based on different
attributes of a consumer. For example, state 615 is associated with
the consumer having a physically active life style. Consequently,
the transition probability of disease progression is typically
smaller than a consumer having has a sedentary lifestyle
(corresponding to state 601, in which a consumer is classified as a
"coach-potato).
Exemplary Scenario
[0137] Sarah is a 45-year-old mother of two children, overweight,
pre-diabetic, being treated for hypertension and hyperlipidemia. At
work, she needs to enroll in a health benefit plan since her
employer switched to a new payer, Global Health. In accordance with
an embodiment of the invention, the following scenario that a
consumer experiences. [0138] Enrollment: Sarah is first given a
combination of Predictive Health Risk Assessment (PHRA)
interspersed with Adaptive Conjoint Analysis (ACA) questions. Even
without single claims, PHRA calculates future health trajectories
and guides Sarah through the benefit selection process based on an
adaptive questionnaire tree designed to minimize the number of
questions while maximizing predictive accuracy. She ends up
selecting an HMO plan with various incentives for staying healthy.
Impact analysis engine provided ROI's associated with incentives
for consumers who fit Sarah's profile. She is given an instant
analysis of her current health, likely health trajectories, and
what she can do to prevent unpleasant outcomes. An interactive goal
setting wraps up her first-day consumer experience with GH. Health
trajectory predictors are based on PHRA/ACA questions, in which the
optimal benefit design is part of resource allocation management
(RAM). (With prior art, Sarah is typically given a list of
traditional HMO, PPO, and Indemnity plans with a limited number of
choices in deductibles, co-pays, and premium with health savings
accounts.) [0139] At-risk member identification: By virtue of PHRA,
Sarah has already been identified as an at-risk member who can
benefit from intervention. PHRA lists diabetes as a major risk
factor given her current conditions, BMI, and lifestyle parameters
inferred from external consumer behavior data obtained from
Experian for a specific purpose of improving health guidance, not
premium setting. Given her status, she gets a VAT call tailored to
her situation, along with a two-page feedback/action plan letter
based on her responses to the PHRA questionnaire all during the
first week as part of a welcoming package. The Integrated Health
Management Platform supports this function with health trajectory
predictors, intervention opportunity finder; and RAM. (With prior
art, since Sarah is a new member, GH must wait for claims data to
accumulate before running a predictive model that predicts 12-month
future cost. Because of claims lag, the typical wait time is 6
months.) [0140] Maintenance: Based on earlier communications, Sarah
understands what to do. She takes PHRA frequently to report her
progress and to see if her health scores are improving. Upon
meeting her first goal of losing 10 lbs in 4 weeks and improving
her health scores by 10%, GH sends her a USB pedometer. Now she
uses it to keep track of her activity level daily, uploading to her
personal Web portal at GH activity data, which provides additional
data points to the IHM Platform in order to improve guidance for
Sarah. Meanwhile the IHM Platform is exploring healthcare database
autonomously, looking for patterns that precede low-to-high or
high-to-low transitions so that it can update its knowledge
database. Furthermore, it is constantly monitoring the relationship
between intervention and outcomes to ensure that every member gets
the best possible touch points to maximize population health using
both high-tech and human interventions. The multimode
health-trajectory predictors perform predictions both on a regular
basis and asynchronously (event-driven). All IHM components work
seamlessly to make this happen. (With prior art, not knowing the
full extent of her risk factors, she may live her life as she
normally does. One day, she feels chest pain and goes to ER. Upon
examination, they find out that she needs heart bypass. Further
blood test shows her blood glucose level at 175 mg/dl, which makes
her a diabetic, further complicating her recovery. About 3 months
after her bypass surgery, GH finally has her claims data in an
electronic data warehouse. The indigenous PM now flags her as a
high-risk member--a clear case of regression to the mean and fixing
the door after a cow has already left. A nurse calls her to inquire
if anything can be done to help her.)
Computer Implementation
[0141] FIG. 7 shows computer system 1 that supports an integrated
health management platform (e.g., IHM platform 100 as shown in FIG.
1) in accordance with an embodiment of the invention. Elements of
the present invention may be implemented with computer systems,
such as the system 1. Computer system 1 includes a central
processor 10, a system memory 12 and a system bus 14 that couples
various system components including the system memory 12 to the
central processor unit 10. System bus 14 may be any of several
types of bus structures including a memory bus or memory
controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. The structure of system memory 12 is
well known to those skilled in the art and may include a basic
input/output system (BIOS) stored in a read only memory (ROM) and
one or more program modules such as operating systems, application
programs and program data stored in random access memory (RAM).
[0142] Computer 1 may also include a variety of interface units and
drives for reading and writing data. In particular, computer 1
includes a hard disk interface 16 and a removable memory interface
20 respectively coupling a hard disk drive 18 and a removable
memory drive 22 to system bus 14. Examples of removable memory
drives include magnetic disk drives and optical disk drives. The
drives and their associated computer-readable media, such as a
floppy disk 24 provide nonvolatile storage of computer readable
instructions, data structures, program modules and other data for
computer 1. A single hard disk drive 18 and a single removable
memory drive 22 are shown for illustration purposes only and with
the understanding that computer 1 may include several of such
drives. Furthermore, computer 1 may include drives for interfacing
with other types of computer readable media.
[0143] A user can interact with computer 1 with a variety of input
devices. FIG. 7 shows a serial port interface 26 coupling a
keyboard 28 and a pointing device 30 to system bus 14. Pointing
device 28 may be implemented with a mouse, track ball, pen device,
or similar device. Of course one or more other input devices (not
shown) such as a joystick, game pad, satellite dish, scanner, touch
sensitive screen or the like may be connected to computer 1.
[0144] Computer 1 may include additional interfaces for connecting
devices to system bus 14. FIG. 7 shows a universal serial bus (USB)
interface 32 coupling a video or digital camera 34 to system bus
14. An IEEE 1394 interface 36 may be used to couple additional
devices to computer 1. Furthermore, interface 36 may configured to
operate with particular manufacture interfaces such as FireWire
developed by Apple Computer and i.Link developed by Sony. Input
devices may also be coupled to system bus 114 through a parallel
port, a game port, a PCI board or any other interface used to
couple and input device to a computer.
[0145] Computer 1 also includes a video adapter 40 coupling a
display device 42 to system bus 14. Display device 42 may include a
cathode ray tube (CRT), liquid crystal display (LCD), field
emission display (FED), plasma display or any other device that
produces an image that is viewable by the user. Additional output
devices, such as a printing device (not shown), may be connected to
computer 1.
[0146] Sound can be recorded and reproduced with a microphone 44
and a speaker 66. A sound card 48 may be used to couple microphone
44 and speaker 46 to system bus 14. One skilled in the art will
appreciate that the device connections shown in FIG. 7 are for
illustration purposes only and that several of the peripheral
devices could be coupled to system bus 14 via alternative
interfaces. For example, video camera 34 could be connected to IEEE
1394 interface 36 and pointing device 30 could be connected to USB
interface 32.
[0147] Computer 1 can operate in a networked environment using
logical connections to one or more remote computers or other
devices, such as a server, a router, a network personal computer, a
peer device or other common network node, a wireless telephone or
wireless personal digital assistant. Computer 1 includes a network
interface 50 that couples system bus 14 to a local area network
(LAN) 52. Networking environments are commonplace in offices,
enterprise-wide computer networks and home computer systems.
[0148] A wide area network (WAN) 54, such as the Internet, can also
be accessed by computer 1. FIG. 7 shows a modem unit 56 connected
to serial port interface 26 and to WAN 54. Modem unit 56 may be
located within or external to computer 1 and may be any type of
conventional modem such as a cable modem or a satellite modem. LAN
52 may also be used to connect to WAN 54. FIG. 7 shows a router 58
that may connect LAN 52 to WAN 54 in a conventional manner.
[0149] It will be appreciated that the network connections shown
are exemplary and other ways of establishing a communications link
between the computers can be used. The existence of any of various
well-known protocols, such as TCP/IP, Frame Relay, Ethernet, FTP,
HTTP and the like, is presumed, and computer 1 can be operated in a
client-server configuration to permit a user to retrieve web pages
from a web-based server. Furthermore, any of various conventional
web browsers can be used to display and manipulate data on web
pages.
[0150] The operation of computer 1 can be controlled by a variety
of different program modules. Examples of program modules are
routines, programs, objects, components, and data structures that
perform particular tasks or implement particular abstract data
types. The present invention may also be practiced with other
computer system configurations, including hand-held devices,
multiprocessor systems, microprocessor-based or programmable
consumer electronics, network PCS, minicomputers, mainframe
computers, personal digital assistants and the like. Furthermore,
the invention may also be practiced in distributed computing
environments where tasks are performed by remote processing devices
that are linked through a communications network. In a distributed
computing environment, program modules may be located in both local
and remote memory storage devices.
[0151] In an embodiment of the invention, central processor unit 10
determines health trajectory predictors from HRA data 151, claims
data 153, and CBM data 155 (as shown in FIG. 1), which are obtained
through LAN 152 and WAN 154. Central processor unit 10 may also
provide the functionalities of intervention opportunity finder 103,
resource allocation manager 105, and impact analysis engine 107.
Consequently, central processor unit 10 may provide a target of
opportunity for a consumer from evidence-based medicine (EBM)
guidelines or medical journals 351 (as shown in FIG. 351). EBM
guidelines (corresponding to EBM database 317) and electronic
health records (EHR) (corresponding to EHR database 203) may be
retrieved from hard disk drive 18.
[0152] As can be appreciated by one skilled in the art, a computer
system with an associated computer-readable medium containing
instructions for controlling the computer system may be utilized to
implement the exemplary embodiments that are disclosed herein. The
computer system may include at least one computer such as a
microprocessor, a cluster of microprocessors, a mainframe, and
networked workstations.
[0153] While the invention has been described with respect to
specific examples including presently preferred modes of carrying
out the invention, those skilled in the art will appreciate that
there are numerous variations and permutations of the above
described systems and techniques that fall within the spirit and
scope of the invention as set forth in the appended claims.
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