Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR PROVIDING A HEALTH DETERMINATION
SERVICE BASED ON USER KNOWLEDGE AND ACTIVITY
TECHNICAL FIELD
[0001] Examples described herein relate to a system and method for
providing a health determination service based on user knowledge and activity.
BACKGROUND
[0002] Online services exist which provide interactive gaming and social
environments for users. These services generally exist for amusement only.
[0003] There also exists a questionnaire, termed the Patient Activation
Measure ("PAM"), provided by Insignia Health under license from the State of
Oregon, which includes a static set of questions that are knowledge-based and
deemed correlative to health.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 illustrates a system for predicting a physiological or
mental
health of a user based on the user's knowledge level of health, according to
one or
more embodiments.
[0005] FIG. 2 illustrates an analysis system, according to an embodiment.
[0006] FIG. 3 illustrates an example of a data structure that can be
developed to link a question with a health outcome and a topic, according to
one
or more embodiments.
[0007] FIG. 4 illustrates an example method for predicting a health
outcome
of a user based in part on whether a user has independent knowledge of an
assertion relating to health.
[0008] FIG. 5 illustrates an example method for predicting a health
outcome
of a user based on a knowledge profile of a user.
[0009] FIG. 6A illustrates an example method for providing a health
related
service to a user based on a knowledge-predicted health outcome for a user.
[0010] FIG. 6B illustrates a health service sub-system 680, according to
an
embodiment.
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[0011] FIG. 7A illustrates an example method for providing a game-based
environment in which user responses enable prediction of health outcomes for
individual users.
[0012] FIG. 7B illustrates a knowledge-based recommendation engine,
according to one or more embodiments.
[0013] FIG. 7C illustrates an example method for choosing questions to
provide to a user based on data retrieved from an activity monitoring device.
[0014] FIG. 8A through 8H illustrate example interfaces for use with one
or
more embodiments described herein.
[0015] FIG. 9 is a block diagram that illustrates a computer system upon
which embodiments described herein may be implemented.
DETAILED DESCRIPTION
[0016] Some embodiments include a system and method for predicting a
health outcome of a user based on a determination of knowledge the user
possesses regarding issues of physiological or mental health.
[0017] Still further, in some embodiments, a system and method is provided
for providing a health service benefit to a user based on their predicted
health, as
determined from the user's knowledge of human health.
[0018] In one embodiment, a collection of assertions are stored in which
each
assertion pertains to human health. For each individual in a control
population of
persons, a value of a predetermined health parameter is determined which is
indicative of that person's health. For each assertion of the collection, a
correlative
health parameter is determined which is indicative of an association between
those individuals in the control population that have independent knowledge of
the
assertion and the value of the predetermined health parameter for persons of
the
control population. The collection of assertions can be stored by associating
each
assertion with the determined correlative health parameter for that assertion.
An
interface is provided for a user to indicate the user's independent knowledge
of
each assertion in at least a subset of assertions from the collection. A
health
outcome is predicted for the user based at least in part on the correlative
health
parameter of individual assertions in the subset of assertions.
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[0019] In still another embodiment, a health outcome of a user is
predicted
based on a knowledge profile determination of the user. In one embodiment, a
knowledge profile is determined for the user which reflects the user's
independent
knowledge of individual assertions in a collection of assertions. A
correlation is
determined as between a set of facets of the user's knowledge profile and a
corresponding set of facets of multiple individual person's knowledge profile.
The
knowledge profile can be determined for at least a set of assertions from the
collection of assertions. A health outcome is determined for each of the
multiple
individual persons. The health outcome of the user can then be predicted based
in
part on the correlation and the health outcome of each of the multiple
individuals.
[0020] In still another embodiment, a knowledge profile is determined for
the
user to reflect the user's independent knowledge of individual assertions in a
collection of assertions. Each assertion in the collection can be non-specific
to the
user or to any person of the population, but otherwise known to be correlative
to
human health. A determination is made as to a first correlation value as
between
the knowledge profile of the user and a knowledge profile of a control group
of
persons for whom one or more health outcomes are known. A first health outcome
is predicted for the user based on the first correlation value. A health
service
benefit is provided to the user based at least in part on the predicted health
outcome.
[0021] Still further, according to another embodiment, a human health
knowledge profile is determined for each user in a group of users, the human
health knowledge profile reflecting that user's independent knowledge about
assertions in a collection of assertions. Each assertion in the collection of
assertions may pertain to human health and is non-specific as to any user or
to
any person of the population. At least a first correlation value is determined
as
between a facet of the knowledge profile of individual users in the group of
users
and a corresponding facet of the knowledge profile of a control group of
persons
for whom one or more health outcomes are known. A subset of one or more users
is selected based on the first correlation value of each user of the subset
exceeding a threshold designation. A service or designation is provided for a
set
value to the one or more users of the subset, and not to other users of the
group.
The service or designation may be associated with a true per-user cost that is
not
equal to the set value, but which is variable and set to increases over time
when
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individual users in the subset suffer negative health consequences as a result
of a
naturally progressing medical condition. Still further, some embodiments
include a
system and method for providing a health service or benefit to a user. By way
of
examples a health service or benefit can include health insurance (including
primary or supplemental), life insurance, enrollment in a facility to receive
medical
attention, medical publications, as well as discounts or augmented services
thereof. In one embodiment, a collection of questions are stored, where each
question is based on a documented assertion pertaining to human health. Each
question in a first subset may be associated with a correlative health
parameter
that is based at least in part on (i) persons in a control population of that
have
independent knowledge of an assertion that is a basis of that question, and
(ii) a
value of a predetermined health parameter for each person in the control
population the value of the predetermined health parameter for each person
being
indicative of that person's health. Additionally, the second subset of the
questions
is associated with a null (i.e. non-existent) or neutral (i.e., not indicative
of
health) correlative health parameter. A corresponding set of questions is
displayed to the user from the collection for response for each user in the
set of
users. A response score is determined for each user in the set of users based
on a
correctness of their respective reply to each question in the corresponding
set of
questions. A health parameter value is determined for at least a health
outcome
based at least in part on the correlative health parameter of at least some
questions in the corresponding set of questions.
[0022] Still further, some embodiments include a system and method for
providing health recommendations to a user. In an embodiment, a plurality of
questions are provided to the user. The plurality of questions can include
multiple
questions for each of multiple health-related topics, so that individual
questions
are each associated with one or more of the multiple topics. A score is
determined
for the user in answering each question in the plurality of questions. The
score can
include topical scores for one or more of the multiple topics. Based on the
topical
score of at least a first topic, a set of recommendations can be identified
for the
user. The set of recommendations may include an action that the user can
perform to improve the user's mental or physiological health relating to the
topic.
[0023] According to some embodiments, contextual data is determined from
user activity, and more specifically, from health related activity recorded by
a user
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device. The user device can correspond to, for example, a mobile device that
the
user can carry on their person (e.g., mobile device in arm holster), or by a
wearable electronic device. By way of example, a wearable electronic device
can
include computerized devices that record movement, location, and/or a user's
biometric output (e.g., temperature or heart beat). Wearable electronic
devices
can have a variety of form factors, such as, for example, a bracelet, watch,
arm
band, glassware, hat, or garment. Depending on design or implementation, such
devices can operate independently or in communication with another computing
device (e.g., via Bluetooth or wireless connection to another mobile computing
device).
[0024] As used herein, an activity monitoring device includes any
electronic
device which the user can carry, such as a mobile computing device or wearable
electronic device, which tracks and records user activity in the context of
health.
The recorded activity can include data relating to user exercise, as well as
data
relating to everyday activities such as sleeping, walking, eating, or working
(e.g.,
sitting at desk). According to some embodiments, data generated by one or more
activity monitoring devices is retrieved, and questions displayed to the user
are
based on this retrieved data.
[0025] While examples such as described are implemented on computer
systems, empirical data has been derived to show health outcome prediction can
be correlated to user's knowledge. For example, examples have determined that
positive health outcome determinations made from evaluating user's answers
directly correlate to fewer hospital stays.
SYSTEM OVERVIEW
[0026] FIG. 1 illustrates a system for predicting a physiological or mental
health of a user based on the user's knowledge level of health, according to
one
or more embodiments. A system 100 as shown by an example of FIG. 1 can be
implemented using a combination of servers, or other computers which combine
to provide a network service for client computers operated by a user base.
While
an example of FIG. 1 illustrates the system 100 being implemented as a
combination of logical components, alternative implementations can readily
provide for functionality described to be integrated or discrete. Moreover,
specific
combinations of functionality and processes described can alternatively be
performed as sub-combinations or alternative combinations. Likewise, an
example
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of FIG. 1 illustrates use of multiple data stores, which can logically and/or
physically be implemented as a combined or integrated data structure (e.g.,
database), or alternatively, in distributed fashion such as shown.
[0027] Among other implementations, system 100 can be accessible to users
11 over a network 101, such as the World Wide Web, to mobile computing
devices (e.g., feature phones, tablets, etc.), personal computers (e.g.,
desktop
computers, laptops, etc.) and other user operated computing devices for
purpose
of interactively engaging individual users to determine their knowledge level
on
various health topics, and further for predicting the individual user's
physiological
or mental health based on their knowledge level of health. Among other
advantages, an example of FIG. 1 enables facets of physiological or mental
health
to be determined for a person, without need for obtaining user specific
medical
information or biological samples. For example, in one implementation, a
user's
health can be predicted without use of any user-specific medical question. In
a
variation, a user's health can be predicted based only on inputs of gender and
age. In another variation, data collected through activity monitoring devices
can
be used, alone or in combination with other inputs, to predict a user's
health.
[0028] As described in greater detail, system 100 generates fact-based
questions on various topics of health for purpose of (i) obtaining responses
from
users, and (ii) correlating some of those responses to physiological or mental
health determinations. One of the underlying assumptions of system 100 is that
the living habits and behaviors of people generally tends to have a measurable
impact on their physiological or mental health, particularly when the
assumption
is applied to a statistically significant sample of people (e.g., hundreds or
thousands of persons). Under a statistically significant sample, embodiments
described herein have recognized that a correlation can be made as between the
knowledge or awareness of individuals and their relative health outcome. More
generally, embodiments recognize that health-conscious individuals are
generally
more knowledgeable about health and also more healthy as compared to less
healthy people (e.g., individuals who suffer from obesity, heart disease,
etc.). In
fact, embodiments recognize that healthy individuals are significantly more
conscientious of maintaining healthy living habits and activities, and with
this
mindset, such individuals are far more knowledgeable about health than the
rest
of the population.
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[0029] With this recognition, embodiments described herein provide a system
for gauging how conscientious a given user is with respect to health, based on
the
user's awareness of information that is specific and health driven embodiments
further recognize that such. Such information, which in many cases may qualify
as trivia, nevertheless provides a mechanism for delineating those individuals
in
the population who are in fact conscientious about healthy living habits.
Furthermore, embodiments described herein programmatically correlate
knowledge of health to physiologic health of individuals amongst a
statistically
significant sample size of users.
[0030] In order to gauge knowledge, an embodiment of FIG. 1 maintains a
library of fact-based assertions on various subjects of human health, such as
nutrition, exercise, medicine, etc. In an example of FIG. 1, the assertions
are
presented to users in the form of questions, for which responses can provide
answers that are either correct or incorrect, and further enable evaluation of
knowledge based on whether correct or incorrect answers were given by the
users. While examples provide for assertions to be presented to users in the
form
of questions for purpose of validating their knowledge, other embodiments may
use alternative forms of interaction in order to gauge the user's awareness or
knowledge of a particular assertion. For example, the user may be provided a
statement that is presented as an answer, and the interaction required of the
user
can be for the user to generate a question that yields the particular answer.
In
this reverse format, the user's ability to generate the question, combined
with a
statement as the presented answer, serves as a mechanism for determining
whether the user has independent knowledge of the underlying assertion from
which the statement was originally presented.
[0031] Still further, as described in greater detail, some embodiments
utilize
a collection of assertions, of which only some have been determined to
correlate
to physiological or mental health. The user may have no knowledge of which
questions correlate to health, or that only some questions have direct
correlation
to health while others are being provided for alternative purposes (e.g.
amusement). In some cases, the user may even have no knowledge that some of
the assertions for which the user is responding to have any correlation to do
with
their actual physiological or mental health. Among other benefits, the use of
many
questions, in combination with questions that have been determined to
correlate
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to physiological or mental health, preclude some individuals from 'gaming' the
questions in a manner that masks their true knowledge level and awareness.
[0032] In more detail, system 100 includes a user interface 110, question
selection logic 120, response logic 130, and health scoring logic 140. The
question
selection 120 can receive or access questions 127 from a question library 152,
and the user interface 110 can present content based on the selected questions
127 to individual users in any one of a variety of computing environments that
stimulate the individual to provide purposeful responses that reflect the
user's
understanding and knowledge for a topic of the question. The questions 127 can
vary in their purpose. In one example, question library 152 includes (i) a
first set
of questions 127a which have been correlated to physiological or mental
health,
and (ii) a second set of questions 127b which have not been correlated to
physiological or mental health, but which may serve the alternative purpose of
providing trivia, factual information, and/or entertainment. Additionally, the
questions of library 152 can be assigned to topics and sub-topics. Still
further, the
questions of the library 152 can be associated with a difficulty score, based
on, for
example, a correction rate amongst a control group of persons who answered the
question.
[0033] When the user initiates a session, the user interface 110 may record
a
user ID 121 and session information 125. In implementation, the user interface
110 can authenticate the user, and provide credentials 139 for a user profile
store
138 in order to obtain profile data 137. The profile data 137 can identify,
for
example, any one or more of (i) the topic that the user was previously being
questioned on, (ii) a topic the user is interested in, (iii) identifiers to
questions
that the user as previously answered, and/or (iv) a determined knowledge level
135 of the user. With the profile data 137, the user interface 110 can
identify
parameters or other information for facilitating question selections for the
user. In
one example, the user interface 110 can use the profile information 137 to
specify
one or more topical parameters 123 and/or the knowledge level 135 of the user.
In turn, the question selection 120 can select questions 127 based on
parameters
113, which can be based on, for example, topic parameter 123, knowledge level
135, or user interest and/or preferences.
[0034] The profile data 137 can also include user-specific game data 119
(e.g., user's personalizations for gaming, historical performance on games,
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current game play state, etc.). Additionally, the profile data 137 can include
the
user's community or social network data 117 (user's personalizations for
community or social network application, social network content, etc.). The
user-
specific game data 119 and community or social network data 117 can, for
example, be loaded through the respective functional layers of the user
interface
110 when the user initiates a session with a service of system 100.
[0035] In addition to using profile data 137 to create parameters 113,
system
100 can also use device data 193, which can include indicators of a user's
overall
health and fitness levels, generated by activity monitoring devices 191 for
parameters 113, alone or in combination with profile data 137. Activity
monitoring
devices 191 include electronic devices (e.g., wearable electronic devices)
that can
be worn or held by users 11 in order to track data related to the users'
activity
levels and health parameters.
[0036] An activity monitoring device 191 can include resources such as
Global Positioning System (GPS), motion sensors, and/or sensors (e.g.,
heartbeat
monitor) to record and track user activity, as well as biometric information
of the
user in performing such activity. Additionally, the activity monitoring device
191
can include sensors such as an accelerometer or accelerometer set, a
gyroscope,
a magnetometer, an ambient light sensor, heart rate sensor, temperature sensor
and/or other sensors to measure facets of the user's body in performing an
activity. The activity monitoring device 191 records activity data 193, which
can
include statistics like pace, distance, elevation, route history, heartbeat,
body
temperature and/or other information relating to the user activity. The
activity
data 193 can include both (i) raw or measured data and (ii) derived or
computed
data based on measured or raw data and/or user input. Additional examples of
device data 193 include heart rate and heart rate trends, steps, distance
traveled,
floors climbed, calories burned (e.g., derived from distance, pace, and user
weight/gender), active minutes, sleep quality, blood sugar, and cholesterol
levels,
among others.
[0037] In some aspects, activity monitoring devices 191 can store their
data
in a device database 192, which can be managed by a computing platform (e.g.,
APPLE HEALTHKIT, manufactured by APPLE INC.). Such computing platforms can
allow for designated mobile applications to read and write data to the device
database 192 based on a set of permissions. For example, the permissions allow
a
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user to choose which applications have access to device data 193 in order to
protect privacy and prevent unauthorized access to potentially sensitive
information. In some implementations, system 100 may only use device data 193
if a user has specifically opted-in and given permission for the data to be
accessed
by the system.
[0038] The user interface 110 can be used to record responses 129 from
individual users. In one implementation, each question 127 can be communicated
to the user interface using a sequence in which the answer to the question is
also
packaged and presented to the user. Some conditional logic may also be
provided
with the question 127, so that, for example, if the user response is correct,
the
user is instantly notified and the next question is presented to the user.
However,
the conditional logic may render an alternative content in response to
incorrect
user response, specifically a panel or other information item which provides
information regarding the actual answer to the question presented. In this
way,
the user is made more knowledgeable.
[0039] The responses 129 can correspond to input that identifies, for
example, the user's answer to a particular question. The responses 129 can
identify the answer of the user, the question that was answered, and an
identifier
of the user. In some implementations, each question 127 can further be
associated with one or more subject matter topics. Response logic 130 can
process the responses 129 from the various users. In one implementation, an
initial determination of response logic 130 is whether the question identified
with
response 129 is pre-associated with a physiological or mental health
correlation,
or whether no such pre-association physiological or mental health correlation
exists for the question. In one implementation, the response logic 130 records
a
corresponding response entry 131 for each response, regardless of whether the
question of the response has pre-association with physiological or mental
health.
The response entry 131 can reflect whether the answer to the question is
correct,
as well as the true answer. In some implementations, the response entry 131
further links the question answered to topical designations for the question,
as
well as calibration or difficulty scoring.
[0040] Scoring logic 144 can process the answer of response entry 131 to
determine a score value 145 to associate with the particular record entry. The
score value 145 can be based in part on the difficulty level of the question,
which
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in some implementations, is provided as a calibration coefficient that is pre-
associated with the question. Thus, the mathematical process to tabulate
scoring
can include factors such as the number of questions the user correctly
answered,
the number of questions the user incorrectly answered, the difficulty
parameter
associated with each question, and/or secondary considerations such as the
time
it took for the user to provide the response and/or whether the user correctly
answered the question on the first try. The score value 145 can be stored with
the
response data store 118.
[0041] Additionally, scoring logic 144 can also tally one or more aggregate
or
overall scores for the user based on a historical record of responses. For
example,
the response data store 118 can maintain one or more aggregate or ongoing
subject matter topical scores (e.g., weight-lifting), as well as an overall
score for
the user. As described with other examples, scoring logic 144 can be used to
develop comparative scoring as between users, based on their overall
knowledge,
session performance, and/or topical subject matter knowledge.
[0042] The response logic 130 can optionally include a knowledge level
determination component 134. The knowledge level determination component
134 can determine from the response 129 the knowledge level 135 of the user.
Alternatively, the knowledge determination component 134 can determine the
knowledge level of the user from the difficulty parameter associated with the
question and/or with the score output, as provided by the scoring component
144.
The knowledge level determination component 134 can determine an overall
knowledge level or a topic-specific knowledge level 135. The determined
knowledge level(s) 135 can be stored as part of the user profile 138, so that
the
knowledge level of the user is communicated to the questions selection logic
120
when the user initiates a session with system 100.
[0043] For those selected questions which are identified as having a pre-
associated physiological or mental health correlation, the response logic 130
can
provide a corresponding health question record 133 which identifies, for
example,
the question, the answer provided, and/or whether the question was answered
correctly. The health question record 133 can also specify a topic or topics
of the
question.
[0044] According to some embodiments, the question identified with the
health question record 133 can be associated with a health parameter value
151.
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As described by other examples, the health parameter value 151 can be
determined as part of a correlative model that is developed using a control
population in order to provide a quantified correlation to physiological or
mental
health. A health scoring data store 150 can maintain a collection of health
parameter values 151 for individual questions. In one implementation, the
health
parameter values 151 reflect a predefined health outcome. Multiple health
outcomes can be considered, and each question of health question record 133
can
be associated with a particular health outcome. By way of examples, the
possible
health outcomes that have quantifiable correlations to the health parameter
values 151 include (i) health care cost for an individual in a given time
period, (ii)
number of medical facility visits by an individual in a given time period,
(iii)
number of prescriptions that the person takes in a given time period, and/or
(iv)
number of sick days that the person took. Other examples of health outcomes
include propensity for cancer (including cancer of different types), heart
disease,
diabetes, hypertension or other afflictions. The health outcomes can thus be
numerical and continuous in nature (e.g., health care cost) or categorical
(e.g.,
number of medical visits, prescriptions, sick days).
[0045] Accordingly, in one implementation, the health scoring component
140 utilizes health outcome logic 142 to generate a health outcome score 165
that is specific to a particular health outcome definition 155. The health
outcome
logic 142 can be implemented as a formula or model, and can take into account
parameters that include the health parameter value 151 determined an answered
question, the number of questions answered, the time of involvement, etc. In
one
implementation, the health parameter values 151 that can be combined or
tabulated can be determined from identifying the health questions 141 and
responses 143 of the user. Based on the question and response the health
correlative parameters 151 are retrieved.
[0046] In an embodiment, the health scoring component 140 uses the health
correlation parameter 151, as well as the question 141 and response 143 to
predict the health outcome 165 of the user. In determining the health outcome,
the health scoring component 140 can use a model or formula to determine the
health output score 165. For example, the health scoring component 140 can map
the user's input to a health score output which is then predictive for the
user. The
model used by the scoring component 140 to predict the health outcome score
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165 of the user can be the same model which determines the correlation of
questions to the particular health outcome definition. Examples of such models
is
provided with FIG. 2.
[0047] The health outcome score 165 can be generated and stored as part of
the user health data store 160. Additionally, the health outcome score 165 can
be
specific to a particular health outcome, and the type of value it reflects can
be
specific to the health outcome type. For example, one implementation provides
that for a health outcome that reflects health care cost for the individual,
the
health outcome score 165 can be a numeric indication of a specific cost or
range
of costs for the individual. The health outcome score 165 for the number of
medical facility visits, on the other hand, can be reflected by a category or
level
(e.g., 1 to 5 depending on amount).
[0048] In one implementation, the user health data store 160 is maintained
logically or physically separate from the question response data store 118 in
order
to preclude its viewability to users of the system 100. Each user can include
a
profile of health outcome scores with the user health data store 160, with
individual user profiles 141 which include scores for multiple different
health
outcomes. In some variations, a combined score or category may also be given
to
individual users as part of their health profile.
[0049] As described with other embodiments, the health outcome score(s)
165 of the user can be made available for health services, such as health
insurance services. For example, the premium, deductible or scope of coverage
provided as part of a health insurance package for a user can be determined
from
the health outcome score(s) 165. As another feature, health outcome score(s)
165 of the user can be used to determine if the user should receive a discount
for
health insurance, or alternatively receive an added benefit from health
related
services that are provided (or are to be provided) to the user.
[0050] According to one embodiment, a health service 190 sub-system can
utilize the health outcome scores 165 provided in the user health database 160
to
determine designations, qualifications or service level, in connection with a
health-related service. Examples of health related services 190 include health
insurance, life insurance, health service plans, memberships in health related
facilities (e.g., health spas, medical office), informational services (e.g.,
magazine
or journal subscriptions, electronic news). The benefit that can be provided
to the
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user includes the service itself, or alternatively a designation of health for
use
with such a service. For example, the user's predicted level of health can be
determined by the health outcome score(s) 165, and this can result in an
overall
health outcome determination (e.g., a ranking or classification), which in
turn can
be used to receive a discount for health related services (e.g., discount on
health
or life insurance premium, expanded coverage, etc.). An example of health
service sub-system is provided with an example of FIG. 6B.
[0051] In some implementations, the user interface 110 of system 100 can
include various layers or functional components for enhancing the
interactivity
level of the user. In one implementation, the user interface 110 includes a
gamification layer 112 and a community social network layer 114. The
gamification layer 112 includes logic, data, and content (collectively "game
data
103") for implementing a competitive environment for which the individual is
to
supply answers for questions 127. The game data 103 can be generated a gaming
engine 115, which can further personalize the gaming environment for the
specific
user. For example, the user identifier 121 can be used by a gaming engine 115
to
generate user-specific game data 103. The game data 103 can, for example,
include a competitive environment that is based on the knowledge level of the
user and/or topical interests of the user. An implementation that utilizes a
gamification layer 112 is described with FIG. 7A. The gamification layer 112
can
determine awards or credentials (e.g., skill level badges) for the user based
on
their performance. By way of example, the questions 127 presented through the
user interface 110 can be associated with a score value that accounts for
difficulty
(which may be determined from a calibration process, as detailed below),
response time, handicaps (e.g., the age of the user), etc.
[0052] The community social network layer 114 can operate using
community data 117, which can be generated from a community/social network
service 116. The community/social network service 116 can, for example,
provide
user-specific community (or social network) data based on the user identifier
121.
The community data 117 can provide content (e.g., user's health interest or
knowledge specialties) that is provided as part of the community social
network
layer 114.
[0053] The health parameter value 151 represents a correlative and
quantified measure as between human health and knowledge of a particular
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assertion. The granularity of the health parameter value 151 is applied to a
question as answered from an individual, but the determination of the value
can
be based on a correlative model applied to a control population of users. The
control population of users include those individuals who, for example,
voluntarily
provide real-world information about themselves, and more specifically, actual
health outcomes in a recent duration of time.
[0054] In more detail, system 100 can include a question analysis sub-
system 170 that includes functionality for determining correlations between
knowledge of individual questions and actual health outcomes. The sub-system
170 can implement and develop one or more correlative models 172, which can
analyze input questions 171 for purpose of determining correlations to health
outcomes. In particular, the correlative models can be implemented for purpose
of
determining health parameter values 149 that statistically reflect a
correlation as
between knowledge of individuals in the control population (shown with the
control population data store 180) for particular question and the respective
health outcomes for those individuals who answered the question (either
correctly
or incorrectly, depending on implementation). The health correlative values
151
can be specific to individual questions or cluster of questions. In one
implementation, different correlative models 172 can be used for different
types
of health outcomes. Different correlative models may compare a predicted value
with actual (or real-world) data provided for individuals (shown as verified
input
175). An example of question analysis sub-system is described in more detail
with
an example of FIG. 2.
[0055] While numerous examples provide for use of health correlative
scores,
other embodiments can also generate recommendations to users based on their
overall knowledge level, as determined by, for example, the user's score, or
topic-
specific scores. A response analysis 164 can retrieve scores 145 from the
response database 118, for example, and generate recommendations, content or
other output based on the user scores. An example of response analysis 164 is
illustrated with FIG. 7B, and accompanying examples thereof.
[0056] As an addition or alternative, the community social network layer
114
can provide forums such as message boards, ask an expert, or topical walls for
shared information and experiences. In one implementation, credentials that
the
user earns through the gamification layer 112 are carried onto the social
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environment of the community social network layer 114. For example, an 'expert
level' user may have credence when responding to questions of others, even to
a
point where the user can request payment or other consideration for providing
answers or information to other users.
[0057] FIG. 2 illustrates an analysis system, according to an embodiment.
In
particular, FIG. 2 illustrates an analysis system 200 for analyzing questions
(or
other forms of assertions) for purpose of determining whether knowledge of the
underlying assertions by subjects can be correlated to physiological or mental
health of the subjects. According to some embodiments, individual questions,
or
alternatively groups of questions, can be correlated to a quantifiable metric
that
statistically relates a subject's knowledge (or lack of knowledge) for an
underlying
assertion to a likelihood of a particular health outcome. The system 200 can
be
implemented as, for example, a sub-system of a physiological/mental predictive
system 100, such as shown with an example of FIG. 1.
[0058] In more detail, system 200 includes a question intake interface
210, a
fielder 220, a calibration component 230, and a correlative model
implementation
component 250. A question interface can receive questions 209 through, for
example, a manual interface (e.g., experts generate questions based on health
assertions). The questions 209 can be manually associated with one or more
topics relating to human health, such as topics relating to nutrition or
exercise, or
specific medical conditions. The granularity of the topics 211 can be
determined
by implementation. A question store 215 can be used to store a question 209
for
processing as the question is calibrated and/or correlated to human health.
[0059] The fielder 220 includes functionality to distribute the questions
209
to a control population of users through a population interface 222. For
example,
the fielder 220 can issue questions using the user interface 110 of an example
system of FIG. 1. For example, with further reference to an example of FIG. 1,
questions 209 can be issued through gameplay of user interface 110, and
responses from various users can be recorded. Some users, however, can be
designated as belonging to the control group. These users can correspond to
individuals for which data corresponding to ground truth data exists. For
example,
many users can be given an opportunity to volunteer real-world health
information. Such users can be asked questions such as "how many doctor visits
did you have last year" or "how many sick days did you have last year." Still
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further, some information like the user's health insurance cost can be
obtained
from a source such as the insurance companies. Accordingly, in one example
such
as shown by FIG. 1, members of the control group can supply responses 213 to
questions 209, presented through a game. At a separate time, either before or
after the questions 209 is presented to the subject, the subject can also be
given
the choice to provide actual data, shown as true user data 241. The true user
data 241 can represent an actual health outcome of a subject providing the
response 213. The true user data 241 can include information manually supplied
by the subject, as well as information provided by, for example, an insurance
carrier of the subject. Each response 213 from one of the subjects of the
control
population (e.g., those users of system 100 who opt-in to provide information)
can be linked to the question and to the identifier 205 of the subject.
Additionally,
the true user data 241 can be linked to the user identifier 205 of the subject
providing the response.
[0060] In addition, true user data 241 can be supplemented or replaced with
information gathered by activity monitoring devices 225 in order to create
more
accurate control data. Activity monitoring devices 225 can provide health data
226 from sensors, such as heart rate and heart rate trends, calories burned,
active minutes, sleep quality, blood sugar, or cholesterol levels. Location
data 227
can also be provided and includes where a user is located based on GPS data,
which can be used in conjunction with other databases to determine, for
example,
if a user is in a restaurant, grocery store, etc. Furthermore, time data 228
can be
used to track a user's schedule. In addition, a user can also choose questions
to
send to a friend through their activity monitoring devices 225 as friend data
229.
[0061] In some embodiments, some or all of these data gathered from
activity monitoring devices 225 can be used by fielder 220 to choose which
questions 209 are presented to a user. For example, if health data 226 shows
that
a user has high blood pressure, questions relating to how to lower blood
pressure
can be chosen. If the user is shown to have poor sleep quality, questions
about
tips to get better sleep can be chosen. If the user has just finished a
workout,
questions about post-workout recovery can be chosen. If a user is determined
to
be a new runner, questions about basic running knowledge can be chosen,
whereas if a user is an advanced runner, more advanced questions can be chosen
instead.
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[0062] Location data 227 and time data 228 can also be used by fielder 220
to interpret a user's schedule and choose appropriate schedule-related
questions.
For example, if the data show that a user commutes via a long subway ride
every
weekday, questions about exercise ideas for long commuters can be shown. If a
user is detected in a restaurant, questions regarding healthy food choices can
be
shown, and if a user is in a grocery store, questions about vegetables,
organic
food, and nutrition can be shown.
[0063] The calibration component 230 can analyze the questions 209 under
process to determine a difficulty level 265 of the question. For example, the
calibration component 230 can query 231 the intake store 215 for a tally of
the
number of responses which were correct and incorrect. The percentage of
individuals who correctly answer a question can provide a basis for
determining a
difficulty level of the question. The difficulty level 265 can be stored with
the
question for subsequent use.
[0064] The correlation model 250 operates to determine a correlation
between knowledge by a subject for an underlying assertion of a question and
the
subject's health. In one implementation, the correlation model 250 implements
one or multiple models for purpose of determining different parametric values
that statistically correlate to different health outcome definitions (e.g.,
amount of
healthcare or healthcare cost an individual requires, the number of medical
facility
visits, propensity for heart disease, cancer, hypertension or diabetes, etc.).
The
correlation model 250 can receive, as model input 255, (i) a question
identifier
261, (ii) identification of a set of individuals in the control group who
answered
the question 209, including identification of the answer each person provided
to
the question 209, and (iii) true user data 241 for each person in the set of
individuals that answered the question. The particular model selected compares
an expected result to a true result by (i) assigning the person to an expected
result, corresponding to a particular health parameter value, based on their
answer to a question, then (ii) using the true user data 241 to compare a true
health outcome (reflecting real-world data of the individual supplying the
answer)
to the expected result.
[0065] The expected result can initially start as a hypothetical or
neutral
value, indicating a likelihood that a given person has or does not have a
particular
health outcome based on the answer the person provided to the question. The
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expected result can further include different values depending on whether the
user provided a correct answer or incorrect answer, as well as which incorrect
answer the user provided. The initial correlation can correspond to a
coefficient
(e.g., a value between 0 and 1) that is set by, for example, an expectation as
to
whether the underlying assertion of the question is information that is
indicative
of health-conscious behavior (e.g., rubbing one's eyes can make a person
susceptible to common cold) or information that is indicative of poor health-
conscious behavior (e.g., specific nutritional information about a donut).
From the
initial value, the correlation can become positive, negative or made neutral
based
on the expected/actual comparison for persons in the set. As more individuals
are
added to the set, the correlation can be made more valid or certain. The
determined correlation from the correlation model 250 can be identified as
correlative health parameter 251. The correlative health parameter 251 can be
specific to a particular health outcome 253. The correlative health parameter
251
can, for example, correspond to a parametric value, such as a weight or
coefficient, which can be aggregated, modeled and/or combined with other
parametric values to make a health outcome determination.
[0066] The particular model 255 implemented by correlation model 250 can
depend on the nature of the health outcome that the assertion is to apply to.
For
a health outcome definition in which the health parameter value is continuous
(e.g., monetary cost for health care in a given period, weight or body mass
index), a linear regression model can be used. Some health outcome definitions
can utilize health parameter values which are tiered or categorical. For
example,
the number of medical facility visits can be defined into tiered values, such
as:
0=no medical facility visits, 1= 1-2 medical facility visits in a year, 2=3-5
medical
facility visits in a year, or 4=5 or more medical facility visits in a year.
Similar
tiered values can be used for health outcomes such as sick days. For such
health
outcomes, an ordinal logistic regression model can be used. In variations, a
multinomial or polynomial model can be used for tiered categories,
particularly
those health outcomes which define tiers which are not naturally ordered. Each
question can be assigned to a particular health outcome, so that the health
parameter value is specific to the determination of the health outcome.
[0067] Numerous other machine-learning models can be used in both
developing correlative health parameters, and determining health outcomes
based
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on correlative health parameters. By way of example, such machine-learning
models can include random forest, neural network and/or gradient boosting
models.
[0068] In some embodiments, the determination of the health parameter
values 251 can be tuned to reflect determinations that are for use with a
model in
which no user-specific information is known. In one implementation, the
control
population can be associated with classification parameters, such as age group
(e.g., over 50, under 50), gender, weight, race, geographic location or
setting,
and/or presence of certain medical conditions such as diabetes. An individual
question can be associated with multiple correlative health parameter values
251,
including health parameter values that reflect the general control population,
as
well as a health parameter value that is specific to a class or sub-class
(e.g.,
females over 50).
[0069] According to some embodiments, a combination of question and
correlative health values 251 can map to one of multiple possible health
outcomes. Thus, in one implementation, a question can have a correlative
health
value as it applies to a single health outcome.
[0070] Other implementations provide tor the determination of health
parameter values 251 which are correlative to health of a user based on a
model
in which a classification (e.g., gender or age) or set of classifications
(e.g., gender
and age) are known about the person answering the question. Depending on
implementation, the classifications of users can include (i) unknown users,
for
which no information is known, (ii) users for which some basic health-relevant
characteristic is known, such as age, gender, or combination thereof, (iii)
users
for which multiple relevant facets of health is known, such as their weight
and/or
height, as well as, as gender and age. One implementation provides for the
determination of correlative health parameters 251 which are determined
specific
for different classifications of the user, based on applying models as
described to
segments of the control population which have the relevant classification.
Thus, in
some variations, the correlative health parameter values 251 can be made
specific to specific classes of persons, so that the evaluation of health for
the user
is made in reference to the user's class. For example, in some embodiments,
the
questions can be fielded for individuals who categorize themselves by gender,
age, weight, and/or presence of certain medical conditions such as diabetes.
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[0071] System 200 can be implemented on a control group that is dynamic,
meaning individuals can be added to the control group continuously over time.
As
mentioned, a larger control group can provide more valid results. In an
interactive
gaming environment, such as described with an example of FIG. 1, additional
persons can be added to the control group continuously through invitation or
opt-
in features. For example, the user-interface 110 can prompt individuals to
volunteer for questions that reflect actual medically relevant information.
This
mechanism can provide a way to expand the control group with the addition of
users for whom true user data 241 can be provided. The control group can also
be
managed based on criteria, such as gender and age, so that it accurately
reflects
a desired population segment.
[0072] With the determination of the health parameter values 251, the
questions can be deemed processed, in which case the questions can be included
in a library or collection of questions and marked as being correlative to
health. In
one implementation, a library build process 260 links processed questions 259
with the question identifier 261, topical identifier, the difficulty level 265
and the
correlative health parameter 251 (or multiple values). The difficulty level
265 can
be used to determine which individuals receive the question based on user
level.
[0073] While an example of FIG. 2 provides for processing of questions
which
are deemed correlative to health, a fielding and calibration process can be
used to
determine difficulty of all questions, including those questions which have no
determined correlation to health. For example, any question can be associated
with the topic 211 and fielded to the control population as described, and
further
evaluated for difficulty level 265 based on, for example, the percentage of
individuals of the control group who correctly answered the question.
[0074] FIG. 3 illustrates an example of a data structure that can be
developed to link a question with a health outcome and a topic, according to
one
or more embodiments. While an example of FIG. 3 illustrates the data structure
300 as being logically integrated, variations can provide for distributed data
structures which associate or link parameters as described. With reference to
an
example of FIG. 1, the data structure 300 of an example of FIG. 3 can, for
example, be provided with the question library 152, and include information
provided with the health scoring database 150.
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[0075] In more detail, data structure 300 associates individual questions
by
question identifier 301 to one of multiple possible correlative health
parameters
303, and one or more topics 305. Other information or parameters that can be
conveyed with the data structure 300 include a difficulty level, which can be
determined, for example, through an output of the calibration component 230
(see FIG. 2). For a given implementation, the correlative health parameter can
relate to a particular health outcome. Multiple health outcomes can be defined
for
a future time interval, including health care cost, medical facility visits,
sick days,
and number of prescriptions. Other examples of health outcomes include blood
sugar level, weight or body fat (e.g., BMI), cholesterol level, depression or
anxiety
disorder, and/or longevity. In one embodiment, each question associated to
only
one health outcome, and is further assigned a correlative health parameter
value
that reflects a correlative measure between knowledge of the underlying
assertion
and a corresponding health outcome. In one implementation, a system of FIG. 2
determines health parameter values for each defined health outcome, and the
health parameter value selected for a question is that which has the strongest
correlation. If no correlative health determination has been made for a
question,
then the health parameter values for such questions can be shown as null.
[0076] As further shown by an example of FIG. 3, each question can be
linked with multiple topics based on, for example, manual input. The
determined
difficulty can also be expressed as a parameter, such as a number between 0
and
1. The difficulty level can be independent of the topic assignment for the
question-thus, meaning the difficulty level of a question can be provided as
being
the same regardless of the assigned topic being considered.
METHODOLOGY
[0077] FIG. 4 illustrates an example method for predicting a health outcome
of a user based in part on whether a user has independent knowledge of an
assertion relating to health. FIG. 5 illustrates an example method for
predicting a
health outcome of a user based on a knowledge profile of a user. In describing
example methods of FIG. 4 and FIG. 5, reference may be made to elements of
FIG. 1, FIG. 2 or FIG. 3 for purpose of illustrating a suitable component for
performing a step or sub-step being described.
[0078] With reference to an example of FIG. 4, a collection of assertions
relating to human health can be stored and processed for use with a population
of
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users (410). In one implementation, the assertions can be formatted as
questions
for which the answer from the user indicates whether the user has knowledge of
the assertion (412).
[0079] For the control population, a health parameter value is determined
for
individuals of the control population (420). The health parameter value can
reference actual or real-world data which serves as an indicator of
physiological or
mental health of a user. In one implementation, the determination of the
health
parameter value can be based on input of a user. For example, in an
interactive
gaming environment of FIG. 1, some users can opt-in to provide requested
health-specific input, such as the number of sick days taken in the prior
month or
year. In some embodiments, the health parameter value is based on a defined
health outcome (422), or combinations of health outcomes. By way of example,
the health outcome can correspond to an estimated health care cost for an
individual (424), a number of medical center visits for an individual in a
given
duration of time (425), a number of prescriptions for the individual in the
given
time frame (426), and a number of sick days an individual incurred in the
given
duration of time (428).
[0080] For each assertion, a correlative health parameter is determined
(430). Generally, the correlative health parameter corresponds to a parametric
measure that quantifiably links knowledge of an assertion to human health. The
health parameter value 151 (FIG. 1), 251 (FIG. 2), as described with other
examples, provides an example based on use of a control group (432).
[0081] The establishment of questions with associated correlative health
parameters can be done through implementation of a model, with ground truth
data provided by select users from a larger user base of respondents. Once the
correlative health parameters are established for individual questions, the
questions can be fielded to the user base. The responses from the user can be
used to determine the user's independent knowledge level of a particular
assertion (440).
[0082] The correlative health parameters for the individual questions
answered by the user can be determined and modeled into a value for a
particular
health outcome (450). For each user, the correlative health parameters of the
answered questions pertaining to a particular health outcome can serve as
inputs
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in order to determine a predicted health outcome for the user (460). Multiple
health outcomes can be determined in this manner.
[0083] With reference to FIG. 5, a knowledge profile of a user can be
determined, relating to a particular health outcome (510). The knowledge
profile
can reflect answers to individual questions, or answers to clusters or groups
of
questions. The knowledge profile can be determined based on a selected
definition. In one implementation, the knowledge profile is specific to a
question,
and reflects whether a user correctly answer the question. In a variation, the
knowledge profile is specific to a question, and reflects which question the
user
answered. Still further, the knowledge profile can reflect the user's answers
in
aggregate form, such as in a cluster of questions (e.g., 3 to 10 questions),
reflecting facets such as the number of questions the user correctly answered
in
the cluster, or the number of answers provided which were deemed more wrong
than others.
[0084] A facet of the knowledge profile can be compared to corresponding
facets of knowledge profiles from individuals of a control group (520). In one
implementation, the user's answer to a particular set of questions can be
individually compared to an answer to the same set of questions from one or
multiple persons of the control group. In variations, the user's answer to a
cluster
of questions can be compared to answers provided by a subset of the control
group for the same cluster of questions, with the comparison being made for
the
cluster of questions as a whole. Still further, the user's answers can be
compared
to answers provided by a subset of the control group which provided the same
exact answers for the cluster of questions.
[0085] A health outcome can be determined for individuals of the control
group (530). As mentioned with other examples, the health outcome can be
defined as a healthy living style characteristic that is indicative of human
health.
The health outcome that is determined for a person of the control group can
reflect real-world information about that person (532). In one implementation,
individuals of the control group can volunteer their personal health outcome
information (534). For example, the information can be provided in exchange
for
some benefit to the person of the control group. In other examples, personal
health outcome information for persons in the control group can be determined
using data from activity monitoring devices. In variations, the health
outcomes
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information for persons of the control group can be determined from sources
such
as health care or insurance providers (536).
[0086] The health outcome of a user can be predicted based in part on a
correlation between the health outcomes of individuals in the control
population
and the compared facets of the knowledge profile between the user and persons
of the control group (540). Thus for example, a user's answer to individual
questions can be compared to the answers provided for the same questions by
those members of the control group. As an addition or alternative, a user's
answers to a cluster of questions can be compared to answers provided to the
same cluster of questions for individuals of the control group, with, for
example,
the comparison being based on matching the user with a subset of persons of
the
control group based on a percentage of correct or incorrect answers provided.
[0087] HEALTH SERVICE METHODOLOGY AND SUB-SYSTEM
[0088] FIG. 6A illustrates an example method for providing a health related
service to a user based on a knowledge-predicted health outcome for a user. In
describing example method of FIG. 6A, reference may be made to elements of
FIG. 1, FIG. 2 or FIG. 3 for purpose of illustrating a suitable component for
performing a step or sub-step being described.
[0089] With reference to FIG. 6A, a health knowledge profile is determined
from each of multiple users (610) with regard to assertions relating to health
(e.g., physiological or mental health). As mentioned with other examples, the
health knowledge profile can reflect individual answers to questions, those
questions which were answered correctly or incorrectly, specific answers
provided
to specific questions (e.g., such as incorrect answers), and/or percentages of
questions answered from a defined cluster of questions.
[0090] Additionally, as mentioned with other examples, a value of a health
correlation parameter can be determined as between the user and a subset of
persons in the control group (620). With reference to an example of FIG. 1,
the
health value parameter 151 can, for example, be determined by the health
scoring component 140. In determining the health correlation parameter, a
given
facet of the users' knowledge profile can be compared to that of relevant
persons
in the control group (622). By way of example, the comparison can be on a
question by question basis, or alternatively, on a cluster basis (e.g.,
compare set
of 5 answers, etc.). Actual health outcomes can be known for members of the
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control group, and the identified correlative health parameters can be based
in
part on the known health parameters of individuals in the control group. The
correlative health parameter can thus be pre-determined for the control group,
and based on real-world information about members of the control group.
[0091] Based on the correlation values, a health outcome determination is
provided for the user (630). As shown with an example of FIG. 3, the
correlation
values can be specific to pre-determined health outcomes. Further with
reference
to an example of FIG. 1, given a set of health parameter values 151 for a
particular health outcome, the health scoring component 140 can make a health
outcome determination. The determination of the health outcome can be in the
form of a score, so that it gives a relative measure of the particular health
outcome as compared to other individuals in the general population. The health
outcome determination can correspond to a health outcome score 165, or
alternatively, to a combination of health outcome scores. For example,
multiple
health outcome scores can be determined for the user, and the scores can be
combined to form an aggregate health outcome determination.
[0092] Based on the health outcome determination, a health service benefit
can be provided to the user (640). The service or designation can be one made
for a set value, wherein the service or designation is associated with a true
per-
user cost that is not equal to the set value, but which is variable and set to
increases over time when individual users in the subset suffer negative health
consequences as a result of a naturally progressing medical condition.
[0093] The health service benefit can correspond to a variety of direct
and
indirect service related benefits. In one implementation, those users with a
health
outcome determination that exceeds a particular threshold can receive a
designation (642). The designation can correspond to a service or credential
provided to only select users of, for example, a network service provided with
system 100 (644). For example, those users which receive a health outcome
determination that places them within the top 10 percentile of all users may
receive a certification, which in turn enables them to receiving discounts
with their
healthcare provider, health insurance, or related health service activities
(e.g.,
discount with nutrition store, athletic gym membership, life insurance, etc.).
Alternatively, the designation can entitle the subset of users to receive a
service,
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such as primary health insurance, supplemental accidental insurance, life
insurance, or other membership service (whether health related or not).
[0094] In variations, the health outcome determination provides a basis for
predicting a user's health, and this basis can in turn be used to determine
health
related services for the user (646). For example, health insurance, life
insurance,
and/or accidental health insurance can be provided to the user with scope and
cost determined by the health outcome determination. For example, the cost of
the premium or deductible to the individual user can be based on the health
outcome determination (648). By way of example, an insurance service can be
provided to users of system 100, and those users with better health outcome
determinations can be provided discounts to their premiums or deductibles, or
alternatively given greater scope of coverage as compared to counterparts
users
who have lesser health outcome determinations.
[0095] FIG. 6B illustrates a health service sub-system 680, according to an
embodiment. A health service sub-system 680 can be implemented with or as
part of, for example, system 100. In variations, the health service sub-system
680 can be provided as a separate system which interfaces with the system 100.
Additionally, the health service sub-system 680 provides an example of a
system
on which an example of FIG. 6 can be implemented.
[0096] With reference to FIG. 6B, a health service sub-system 680 includes
a
system interface 682, a customer data store 684, and service determination
logic
686. The health service sub-system 680 can also include a service customer
interface 688, such as a web page or application page, which a service
customer
accesses to provide input for defining the health service offered, as well
specific
logic or parameters for the service determination logic 686. The service
customer
input 685 can, for example, include text data definition of the service
offered
(e.g., terms of health or life insurance), as well a supplemental content for
viewing by users of system 100. This input can be stored in the service data
store
684.
[0097] In some variations, the service customer input 685 can further input
parameters 683 and other logic (e.g., rules) for the service determination
logic
686. The parameters 683 and rules can, for example, including definition of
the
qualifications needed for users to (i) receive the service, (ii) receive a
particular
facet or tier of the service, and/or (iii) receive the service or tier
according to a
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particular price structure. For example, the service can include tiers of
benefits, or
multi-tiered cost structure, and each tier can be provided to users based on
qualifications, such as one or more of (i) a threshold health outcome score or
set
of scores, (ii) a threshold combination of health outcome score, and/or (iii)
other
health outcome determination.
[0098] The system interface 682 can interface with the user health database
680 in order to determine the health outcome scores 689 of a given user or
user-
base. In a variation, the system interface 682 can communicate with a push or
trigger component on the system 100 which in turn retrieves and pushes
specified
health outcome scores to the system interface 682. In some embodiments, end-
users are precluded from handling health outcome data. The output of health
determination logic 686 can correspond to a notification 691, which can
specify
the results of the health determination logic 686. These results can be
communicated to either the user or to a provider of the health service
benefit.
GAME PLAY
[0099] Numerous embodiments described use of game play and logic as a
mechanism to increase use response and participation. More user response and
participation can have numerous benefits, including (i) increasing the size of
the
control group, by finding more qualified volunteers who are willing to provide
real-
world health information for purpose of developing health correlations to
questions, (ii) more predictive correlations based on larger statistical
sample, and
(iii) data points from users, enabling better prediction of individual user
health.
Additionally, the use of game logic provides a mechanism to hide health
correlative questions from public inspection, thereby precluding users from
"gaming" the questions (e.g., studying) for purpose of receiving a good health
score.
[0100] FIG. 7A illustrates an example method for providing a game-based
environment in which user responses enable prediction of health outcomes for
individual users. In describing an example method of FIG. 7A, reference may be
made to elements of FIG. 1, FIG. 2 or FIG. 3 for purpose of illustrating a
suitable
component for performing a step or sub-step being described.
[0101] With reference to an example of FIG. 7A, a set of questions can be
stored, were at least some of the questions are based on assertions that are
core
relative to health (702). For example, questions can be stored in the question
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library 152, after being processed using a system such as described with an
example of FIG. 2. The stored questions can include both (i) health
correlative
questions, which are used in determining a health outcome score or
determination
for the user (704); and (ii) non-health correlative questions. While the
latter
questions may pertain to health, those questions have either not been
determined
to be correlative or health, or those questions have little relevance to
awareness
for health, and thus correlative to actual human health (706). As mentioned
with
other examples, a gaming environment can be implemented in which the
questions are provided as trivia, so that users receive entertainment benefit
from
participating in answering questions.
[0102] Still further, as described with other examples, the health
correlative
questions can be processed to determine a health correlative parameter (710).
For example, question analysis subsystem 200 can be used to determine a health
correlative parameter 151 for a given question. Still further, as described
with
other examples, the health correlative parameter can be based on persons in
the
control population who have knowledge (or knowledge deficit thereof) of an
assertion underlying the particular question (712).
[0103] In order to encourage participation and development of accurate
health outcome scores and determinations, a gaming environment can be
established in which users are asked questions in a competitive or semi-
competitive context (720). An example of a gaming environment is shown with
environments depicted through interfaces of FIG. 8A through 8H.
[0104] The user responses to trivia questions are recorded, with those
responses including both scores related to health correlative questions (730)
and
scores related to all questions (or alternatively to non-health correlative
scores)
(732). As described with an example of FIG. 6, the health correlative
questions
can be scored for purpose of determining health services to the user (740).
This
score may be hidden or unknown to the user, and determine independently of the
overall gaming score.
[0105] Conversely, the overall gaming score can be published in a social or
gaming environment, to provide the user with credentials in the community of
the
service provided through system 100 (742). For example, the user can use the
latter gaming score to achieve credentials that give the user authority on
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message board discussions, and question and answer forums of the community
platform.
[0106] In some variations, the gaming score can also provide a mechanism
to provide health base recommendations to the user (744). For example, the
user's knowledge base can be evaluated based on topical subjects, and the
user's
deficiency or strengths respect to specific topics of health can be used to
infer
physiological or mental information about the user.
[0107] FIG. 7B illustrates a knowledge-based recommendation engine,
according to one or more embodiments. With reference to FIG. 1 and FIG. 7B,
for
example, the response analysis component 164 can include recommendation
engine 780. The recommendation engine 780 can use information about the
user's knowledge in order to generate recommendations 785, which can include
content that communicates to the user specific actions, lifestyle choices, or
areas
of growth (for knowledge or lifestyle), for purpose of growth.
[0108] In one implementation, the recommendations 785 can be based on
the determinations of the user's strength or weakness with regards to specific
topics of health. The recommendation engine 780 can include processes 782
which retrieve the user's topical scores 781, and then correlate the topical
scores
with recommendation logic 790. The recommendation logic 790 can include rules
791, 793 for selecting recommendations for the user based on different topical
scores and criteria. For example, the recommendation logic 790 can include
rules
for suggesting recommendations to users for specific topics when the user's
score
for the topic is below a threshold. By way of example, a topic can be defined
for
cardiac health, and anytime a user's topical score for cardiac health is below
a
threshold, a set of recommendations 785 for improving the user's cardiac
health
can be generated and communicated to the user. Likewise, if the user's
knowledge is strong in a particular topic, that can also be interpreted as
interest,
and the recommendation logic 790 can utilize the score to suggest
recommendations that are of an advanced level. For example, if the user scores
high in the topic of weight lifting, then the recommendation provided to the
user
can include specific techniques or recommendations based on questions that
have
the highest difficulty level (as determined from, for example, a calibration
component 230 of FIG. 2).
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[0109] In some implementations, activity monitoring devices 770 can provide
device data 774, which can include indicators of a user's overall health and
fitness
levels, to the recommendation logic 790. These devices can include GPS
receivers
to record statistics like pace, distance, elevation, route history and workout
summaries. In addition, they can include sensors such as accelerometers, a
gyroscope, a compass, an ambient light sensor, heart rate sensor, among other
features capable of tracking and recording health and fitness parameters.
Examples of device data 774 include heart rate and heart rate trends, steps,
distance traveled, floors climbed, calories burned, active minutes, sleep
quality,
blood sugar, and cholesterol levels, among others. Recommendation logic 790
can
then use device 774, alone or in combination with rules 791,793 and topical
scores 781, in order to create the recommendation set 785. For example, if
topical scores 781 show that a user has poor knowledge of cholesterol but
device
data 774 indicates that the user's cholesterol levels are satisfactory,
recommendation logic 790 may choose not to recommend cholesterol-related
questions.
[0110] In a variation, the set of recommendations 785 generated for any one
topic can be associated linked with questions or sub-topics of questions. A
recommendation filter 792 can filter the recommendations 785, so as to weed
out
those recommendations the user likely knows based on their correctly answered
questions.
[0111] Still further, the recommendation logic 790 can include combination
rules, which select recommendations 785 for the user based on criterion
provided
by the user's topical score in two or more topics. The combination rules can
identify subject matter relevancy between topics, so that the user's knowledge
of
one topic will benefit another or vice versa. In one implementation, when the
user's topical score of one topic exceeds a threshold, and the topical score
of
another topic is below a threshold, then the recommendation may be provided
that assumes user activity or interest in one topic to assist the user's
knowledge
or lifestyle with regards to the second topic. For example, the user may have
scored high in the topic of weight-lifting, but scored low in nutrition or
sleep. The
recommendation provided to the user may identify the recommended hours for
the user to sleep in order to add muscle mass.
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[0112] By way of another example, if the user is strong on a subject such
as
weight training, but poor in nutrition, then the recommendation engine can
suggest (i) that the user develop his knowledge on nutrition, (ii) identify
nutritional information related to training in order to provide
recommendations.
Recommendations can include, for example, what the user should eat when
training, how such nutritional intake can affect performance in training,
recommendations for the user to confirm with a nutritionist, and expected
results
that can be achieved through proper diet and weight training. Such an example
illustrates recommendations that can be made based on the user being strong in
his or her knowledge base for one topic and weak in another topic. In such
scenarios, the relationship between the two topics can be determined in order
to
generate programmatically actions and subtopics of learning which may be of
interest or benefit to the user.
[0113] Similar recommendations can be determined and linked to user's
topical scores based on different threshold determinations. In one
implementation, if the user scores low on two topics related by subject
matter,
the user's recommendation may be selected on the assumption that the user
suffers from health consequences related to a physiological or mental problem
related to the topics.
[0114] Still further, analysis of the topical determinations can also be
used to
infer characteristics about the respondents, without any mathematical
correlation
being made to the control population. For example, an individual who scores
poorly in both nutrition and exercise can be inferred to be obese, potentially
diabetic, and/or suffer from other health related issues such as depression.
Based
on such analysis, the recommendation engine can suggest areas of growth for
the
user's knowledge. The recommendation engine 780 can also provide
recommended actions, such as publishing a diet to the user for weight loss,
suggesting the user visits a psychiatrist (on a sound assumption that the user
is
depressed), suggesting the user sees a nutritionist and/or personal trainer
(on the
side assumption that the user is overweight), or recommend that the user have
his blood sugar checked for diabetes and or high cholesterol. Such actions can
follow when the user scores poorly on knowledge in topics that have synergy or
relation to one another when considered for physiological or mental health.
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[0115] FIG. 7C illustrates an example method for choosing questions to
provide to a user based on data retrieved from activity monitoring devices. In
describing an example method of FIG. 7A, reference may be made to elements of
FIG. 1, FIG. 2 or FIG. 3 for purpose of illustrating a suitable component for
performing a step or sub-step being described.
[0116] With reference to an example of FIG. 7A, a set of questions can be
stored, were at least some of the questions are based on assertions that are
core
relative to health (750). For example, questions can be stored in the question
library 152, after being processed using a system such as described with an
example of FIG. 2. The stored questions can include both (i) health
correlative
questions, which are used in determining a health outcome score or
determination
for the user (752); and (ii) non-health correlative questions. While the
latter
questions may pertain to health, those questions have either not been
determined
to be correlative for health, or those questions have little relevance to
awareness
for health, and thus correlative to actual human health (754). As mentioned
with
other examples, a gaming environment can be implemented in which the
questions are provided as trivia, so that users receive entertainment benefit
from
participating in answering questions.
[0117] Still further, as described with other examples, the health
correlative
questions can be processed to determine a health correlative parameter (755).
For example, question analysis subsystem 200 can be used to determine a health
correlative parameter 151 for a given question. Still further, as described
with
other examples, the health correlative parameter can be based on persons in
the
control population who have knowledge (or knowledge deficit thereof) of an
assertion underlying the particular question (760).
[0118] In some aspects, in order to contextually choose questions,
question
selection 120 can retrieve data generated by activity monitoring devices 191
(762). This can include direct indicators of health such as heart rate, blood
sugar,
and cholesterol levels (763) as well as information regarding exercise such as
steps taken per day, calories burned, and average activity levels (764). These
devices typically include sensors such as accelerometers, a gyroscope,
compass,
GPS, and a light sensor, among others, that can be used to calculate certain
health parameters like quality of sleep and distance traveled per day (765).
In
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addition, GPS and clock data can be combined with other health and fitness
data
in order to determine a user's schedule and where the user is located (766).
[0119] After retrieving the activity monitoring device data, question
selection
120 can choose questions taking into account the data (768). For example, if
health data shows that a user has high blood pressure, questions relating to
how
to lower blood pressure can be chosen. If the user is shown to have poor sleep
quality, questions about tips to get better sleep can be chosen. If the user
has
just finished a workout, questions about post-workout recovery can be chosen.
If
a user is determined to be a new runner, questions about basic running
knowledge can be chosen, whereas if a user is an advanced runner, more
advanced questions can be chosen instead.
[0120] Location data and time data can also be used to interpret a user's
schedule and choose appropriate schedule-related questions. For example, if
the
data show that a user commutes via a long subway ride every weekday, questions
about exercise ideas for long commuters can be shown. If a user is detected in
a
restaurant, questions regarding healthy food choices can be shown, and if a
user
is in a grocery store, questions about vegetables, organic food, and nutrition
can
be shown.
[0121] In order to encourage participation and development of accurate
health outcome scores and determinations, a gaming environment can be
established in which users are asked questions in a competitive or semi-
competitive context (720). An example of a gaming environment is shown with
environments depicted through interfaces of FIG. 8A through 8H.
EXAMPLE INTERFACES
[0122] FIG. 8A through 8H illustrate example interfaces for use with one or
more embodiments described herein. Interfaces such as described with FIGS. 8A
through 8H can be implemented using, for example, a system such as described
with an example of FIG. 1. Accordingly, reference may be made to elements of
FIG. 1 for purpose of illustrating suitable components for implementing an
interface as described.
[0123] In FIG. 8A, in interface 800 provides a topical selection 804 for a
user
(e.g., nutrition). The interface 800 can be displayed with information from
the
user's profile 138, such as their game score 802 (e.g., provided as game data
119
of the user's profile, in an example of FIG. 1) and badges or certifications
805.
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[0124] The panel 810 of FIG. 8B illustrates a question 812, in the form of
trivia. A set of answers 814 can be provided to the user, from which the user
can
make selection of in order to affect his or her score.
[0125] FIG. 8C illustrates a panel 820 that provides feedback 825 to the
user
as to the correctness of the answer, as well as supplemental information
regarding the correct answer and/or assertion underlying the question. In FIG.
8D, once the user provides the answer, the user can be provided an additional
panel 830, displaying the underlying assertion 832 behind the question. Other
information, such as the percentage of individuals who answer the question
correctly can be displayed to the user. This feature 834 can also reflect the
difficulty level of the question.
[0126] FIG. 8E illustrates a panel 840 on which a menu of options is
provided. The user can select from the menu of options. As shown, the
functionality provided includes gaming (e.g., leader board) and community
interaction (e.g., discussions), in a gaming and social environment such as
described with an example of FIG. 1. Additionally, the menu of options can
include
a health report feature 842 that can display, for example, recommendations as
determined from an example of FIG. 7.
[0127] FIG. 8F illustrates a panel 850 that provides a gaming summary for
the user, displaying the user's overall score 852, as well as badges are
honors
marking 854 achievements in the number of questions the user answered etc.
[0128] FIG. 8G illustrates a panel 860 on which a leaderboard 862 is
provided. The leaderboard can be topic specific and/or categorized by user
level.
[0129] FIG. 8H illustrates the panel 870 for enabling social interaction,
gaming and knowledge base forums through a system such as described with an
example of FIG. 1. Among other social interaction functions, one or more
knowledge base "twins" can be identified to the user. The twins can correspond
to
an individual who closely shares one or more of (i) knowledge profile about
health, or certain topics of health with the user, and/or (ii) similar or same
health
outcome values or determinations. As an addition or variation, the twin can
also
include similar demographic profile, such as having the same gender, age
and/or
race. Identify twins can be shown to each other as a mechanism for building
social interaction and shared experiences, particularly as to distributing
health-
based knowledge, information and services.
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COMPUTER SYSTEM
[0130] One or more embodiments described herein provide that methods,
techniques and actions performed by a computing device are performed
programmatically, or as a computer-implemented method. Programmatically
means through the use of code, or computer-executable instructions. A
programmatically performed step may or may not be automatic.
[0131] One or more embodiments described herein may be implemented
using programmatic modules or components. A programmatic module or
component may include a program, a subroutine, a portion of a program, or a
software or a hardware component capable of performing one or more stated
tasks or functions. As used herein, a module or component can exist on a
hardware component independently of other modules or components.
Alternatively, a module or component can be a shared element or process of
other
modules, programs or machines.
[0132] Furthermore, one or more embodiments described herein may be
implemented through instructions that are executable by one or more
processors.
These instructions may be carried on a computer-readable medium. Machines
shown or described with figures below provide examples of processing resources
and computer-readable mediums on which instructions for implementing
embodiments of the invention can be carried and/or executed. In particular,
the
numerous machines shown with embodiments of the invention include
processor(s) and various forms of memory for holding data and instructions.
Examples of computer-readable mediums include permanent memory storage
devices, such as hard drives on personal computers or servers. Other examples
of
computer storage mediums include portable storage units, such as CD or DVD
units, flash or solid state memory (such as carried on many cell phones and
consumer electronic devices) and magnetic memory. Computers, terminals,
network enabled devices (e.g., mobile devices such as cell phones) are all
examples of machines and devices that utilize processors, memory, and
instructions stored on computer-readable mediums. Additionally, embodiments
may be implemented in the form of computer-programs, or a computer usable
carrier medium capable of carrying such a program.
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[0133] FIG. 9 is a block diagram that illustrates a computer system upon
which embodiments described herein may be implemented. For example, in the
context of FIG. 1, FIG. 2, FIG. 6B and FIG. 7B, a network service or system
can
be implemented using one or more computer systems such as described by FIG.
9. Still further, methods such as described with FIG. 4, FIG. 5, FIG. 6A and
FIG.
7A can be implemented using a computer system such as described with an
example of FIG. 9.
[0134] In an embodiment, computer system 900 includes processor 904,
memory 906 (including non-transitory memory), storage device, and
communication interface 918. Computer system 900 includes at least one
processor 904 for processing information. Computer system 900 also includes a
memory 906, such as a random access memory (RAM) or other dynamic storage
device, for storing information and instructions to be executed by processor
904.
The memory 906 also may be used for storing temporary variables or other
intermediate information during execution of instructions to be executed by
processor 904. Computer system 900 may also include a read only memory
(ROM) or other static storage device for storing static information and
instructions
for processor 904. The communication interface 918 may enable the computer
system 900 to communicate with one or more networks through use of the
network link 920 (wireless or wireline).
[0135] In one implementation, memory 906 may store instructions for
implementing functionality such as described with example systems or sub-
systems of FIG. 1, FIG. 2, FIG. 6B or FIG. 7B, or implemented through example
methods such as described with FIG. 4, FIG. 5, FIG. 6A or FIG. 7A. Likewise,
the
processor 904 may execute the instructions in providing functionality as
described
with example systems or sub-systems of FIG. 1, FIG. 2, FIG. 6B or FIG. 7B, or
performing operations as described with example methods of FIG. 4, FIG. 5,
FIG.
6A or FIG. 7A.
[0136] Embodiments described herein are related to the use of computer
system 900 for implementing functionality as described herein. The memory 906,
for example, can store a question library 931 (see, e.g., also question
library 152
of FIG. 1), including values for health correlative parameters 933 (see e.g.,
also
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health correlative parameters 151 of FIG. 1) of the some questions. The memory
906 can also store instructions 941 for determining a health score, in order
to
determine one or more correlative health parameters for a user, in connection
with the user's participation of responding to questions in an interactive
community or game environment.
[0137] According to one embodiment, functionality such as described herein
can be performed by computer system 900 in response to processor 904
executing one or more sequences of one or more instructions contained in the
memory 906. Such instructions may be read into memory 906 from another
machine-readable medium, such as through a non-transitory storage device.
Execution of the sequences of instructions contained in memory 906 causes
processor 904 to perform the process steps described herein. In alternative
embodiments, hard-wired circuitry may be used in place of or in combination
with
software instructions to implement embodiments described herein. Thus,
embodiments described are not limited to any specific combination of hardware
circuitry and software.
[0138] Although illustrative embodiments have been described in detail
herein with reference to the accompanying drawings, variations to specific
embodiments and details are encompassed by this disclosure. It is intended
that
the scope of embodiments described herein be defined by claims and their
equivalents. Furthermore, it is contemplated that a particular feature
described,
either individually or as part of an embodiment, can be combined with other
individually described features, or parts of other embodiments. Thus, absence
of
describing combinations should not preclude the inventor(s) from claiming
rights
to such combinations.
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