Automated health data acquisition, processing and communication system

1. A method for providing health scores of a plurality of individuals, comprising:

receiving, by at least one processor, health data and extrinsic data as parameters for computation of a health score of a first individual;

verifying, by at least one processor, integrity of at least one of the received health data and the received extrinsic data by confirming at least one of the received health data and the received extrinsic data are not less than a minimum value and not more than a maximum value;

determining, by at least one processor based on the received health data and the received extrinsic data, a likelihood of at least one health risk;

combining the received health data and extrinsic data using an algorithm being implemented as code executing in at least one processor so as to compute a first health score of the first individual, wherein the algorithm causes parameters comprising one portion of the received health data and extrinsic data to interact with parameters comprising another portion of the received health data and extrinsic data, and further wherein the algorithm accounts for the determined likelihood of at least one health risk in the computed first health score;

applying, by at least one processor, artificial intelligence to the received health data and extrinsic data to learn statistical lifestyle data of the first individual;

outputting, by at least one processor to an interface operated by the first individual, the first health score of the first individual;

determining, by at least one processor at least in part using the determined statistical lifestyle data of the first individual, a plurality of modifiable risk factors that each respectively has potential impact to alter the first health score of the first user;

calculating, by at least one processor, a second health score of the first individual as a function of the first health score and a respective one of the plurality of modifiable risk factors;

determining, by at least one processor, a difference between the second health score and the first health score;

selectively outputting to the interface, by at least one processor, the second health score and, as a function of the determined difference between the second health score and the first health score, information associated with the respective one of the plurality of modifiable risk factors;

providing, by at least one processor to at least one second computing device, as a function of information received from the first individual, an access token that provides access control rights by others to the first health score of the first individual;

in response to receiving, by at least one processor, the access token from the at least one second computing device, providing the first health score of the first individual to the at least one second computing device; and

in response to receiving, by at least one processor, at least one other access token which was previously transmitted to the at least one second computing device, providing at least one health score respectively associated with at least one other of the plurality of individuals and the at least one other access token,

wherein the first health score of the first individual is available to at least one other computing device, as a function of the access token.

2. The method of claim 1 , wherein the first health score is presented in the interface as falling in one of a plurality of predefined bands, wherein the bands comprise a relative scale in comparison to fixed percentile criteria.

3. The method of claim 1 , further comprising simulating, by at least one processor, score evolution using a trend in the extrinsic data.

4. The method of claim 1 , further comprising:

providing, by at least one processor, a feedback loop engine that includes at least one of a user challenge, a group challenge and facts.

5. A system for providing health scores of a plurality of individuals, comprising:

a communication unit operable to receive health data and extrinsic data as parameters for computation of the health scores;

a memory arranged to store the received data;

a processor arranged to execute code stored on non-transitory processor readable media to:

verify integrity of at least one of the received health data and the received extrinsic data by confirming at least one of the received health data and the received extrinsic data are not less than a minimum value and not more than a maximum value;

determine, based on the received health data and the received extrinsic data, a likelihood of at least one health risk;

combine the received health data and extrinsic data using an algorithm being implemented as code executing in the processor so as to compute a first health score of a first individual, wherein the algorithm causes parameters comprising one portion of the received health data and extrinsic data interact with parameters comprising another portion of the data and further wherein the algorithm accounts for the determined likelihood of at least one health risk in the computed first health score;

provide a feedback loop module configured to receive information and to learn statistical lifestyle data of the first individual using artificial intelligence, wherein the feedback loop module is configured for scheduling, processing and delivering notifications including health-related information over various channels;

output the first health score of the first individual to an interface of a computing device operated by the first individual;

determine, at least in part using the determined statistical lifestyle data of the first individual, a plurality of modifiable risk factors that each respectively has potential impact to alter the first health score of the first user;

calculate a second health score of the first individual as a function of the first health score and a respective one of the plurality of modifiable risk factors;

determine a difference between the second health score and the first health score;

selectively output to the interface, the second health score and, as a function of a difference between the second health score and the first health score, information associated with the respective one of the plurality of modifiable risk;

provide, to at least one second computing device as a function of information received from the first individual, an access token that provides access control rights to the first health score of the first individual to others;

provide the first health score of the first individual in response to receiving the access token; and

receive from at least one second computing device in response to receiving at least one other previously transmitted access token, at least one health score respectively associated with at least one other individual of the plurality of individuals and the at least one other previously received access token,

wherein the first health score of the first individual is available to at least one other computing device, as a function of the access token.

6. The system of claim 5 , wherein the health score is presented in the interface as falling in one of a plurality of predefined bands, wherein the bands comprise a relative scale in comparison to fixed percentile criteria.

7. The system of claim 5 , wherein the processor is further arranged to execute additional code to configure the processor to simulate score evolution using a trend in the extrinsic data.

8. The method of claim 1 , further comprising determining by at least one processor relationships of data objects associated with a plurality of health scores of a plurality of individuals, wherein the plurality of health scores are output to the interface of the computing device operated by the first individual as a function of a social graph.

9. The method of claim 8 , wherein each of the plurality of health scores is respectively provided by the at least one second computing device as a function of a respective token.

10. The method of claim 1 , further comprising transmitting, by at least one processor, to the first individual over a delivery channel chosen by the first individual, a notification associated with the health data or the extrinsic data.

11. The method of claim 1 , wherein the at least one health risk includes at least one of a vascular risk, a predecessor risk and a modulator risk.

12. The method of claim 1 , further comprising deriving at least one model from at least one study to determine the at least one health risk.

13. The method of claim 12 , further comprising modifying the at least one model for consistency and to predict at least one health-related event within a period of time.

14. The method of claim 1 , further comprising combining a plurality of models, each model derived from at least one study, to derive the at least one health risk.

15. The method of claim 14 , wherein the plurality of models are combined using probabilistic logic.

16. The method of claim 12 , further comprising validating, by at least one processor, the first health score over time.

17. The method of claim 1 , wherein at least one of the received health data or the received extrinsic data is further verified by at least metadata.

18. The method of claim 1 , further comprising:

applying, by at least one processor, information from at least one medical study to at least one data model to derive at least one other health risk; and

determining, as a function of the at least one health risk and the at least one other health risk, a single estimate of a health risk event.

19. The system of claim 5 , wherein the processor is further arranged to execute additional code to configure the processor to derive at least one model from at least one study to determine the at least one health risk, and to modify the at least one model for consistency and to predict at least one health-related event within a period of time.

20. The system of claim 5 , wherein the processor is further arranged to execute additional code to configure the processor to combine a plurality of models, each model derived from at least one study, to derive the at least one health risk.