System and method for determining health care risk costs using a digital communication network

Abstract

Methods for determining health care risk costs using a digital communication network (DCN) are provided. The method includes providing the (DCN) for a plurality of members of a population to communicate between each other. The method also includes analyzing communications in the DCN between the plurality of members and determining one or more network characteristics of the communications based on the analysis of the communications. The method further includes calculating a cost of assuming risks for a health care cost in the population based on at least the one or more characteristics.

Description

Attorney Docket No. 774223: SEQ-019PCSYSTEM AND METHOD FOR DETERMINING HEALTH CARE RISK COSTS USING A DIGITAL COMMUNICATION NETWORKCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 750,109, filed on January 27, 2025, which application is incorporated herein by reference in its entirety.BACKGROUND

[0002] Various embodiments relate generally to health care systems, methods, devices and computer programs and, more specifically, relate to determining health care risk costs using a digital communication network.

[0003] This section is intended to provide a background or context. The description may include concepts that may be pursued, but have not necessarily been previously conceived or pursued. Unless indicated otherwise, what is described in this section is not deemed prior art to the description and claims and is not admitted to be prior art by inclusion in this section.

[0004] Determining a cost of assuming risk for a healthcare cost of a population commonly uses phenotype, prior utilization history, and population size and demographics. However, certain types of healthcare risks (e.g., obesity, mental health, willingness to engage, metabolic syndrome, etc.) may be difficult to determine based on conventional factors. Other factors as determined through social relationships may aid in determining the costs associated with certain types of healthcare risks. For example, computers have changed the way people interact and develop and maintain social relationships. Digital communication networks, which can include social media where individuals can interact with others online, connect many with a community. The online community can be utilized to analyze and group common traits or aspects of the individuals within the community and to determine social relationships between different users.

[0005] Thus, what is needed is a way to determine cost(s) of health care risks associated with a population using the digital communications network.SUMMARY

[0006] Example aspects of the present disclosure include:Attorney Docket No. 774223: SEQ-019PC

[0007] A method for determining an assumed risk cost in a population according to at least one embodiment of the present disclosure comprises providing a digital communication network (DCN) for a plurality of members of the population to communicate between each other; analyzing communications in the DCN between the plurality of members; identifying an individual member of the plurality of members based on the analysis of the communications; determining one or more network characteristics based on the analysis of the communications, the one or more network characteristics comprising: characteristics of the individual member, characteristics of other members of the plurality of members that the individual member is in communication with, and a strength and a direction of the communications between the individual member and the other members; and determining the assumed risk cost for a health care cost in the population based on at least the one or more network characteristics.

[0008] Any of the aspects herein, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.

[0009] Any of the aspects herein, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member.

[0010] Any of the aspects herein, wherein the one or more phenotypical traits include one or more behavioral traits.

[0011] Any of the aspects herein, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising: determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the assumed risk cost for a health care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members, wherein one or more phenotypical traits of the other members is weighted based on the strength and direction of the communications between the individual member and the other members.

[0012] Any of the aspects herein, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

[0013] Any of the aspects herein, wherein the analyzed communications only include forward communications.

[0014] Any of the aspects herein, wherein the DCN is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.Attorney Docket No. 774223: SEQ-019PC

[0015] Any of the aspects herein, wherein the biometric is derived from at least one of: a wearable device, a biometric sample, and answers to a survey or psychometric instrument.

[0016] Any of the aspects herein, wherein the wearable device is an accelerometer, a HR monitor, a HRV monitor, a continuous glucose monitor, a continuous ketone monitor, a skin temperature monitor.

[0017] A method for determining an assumed risk cost according to at least one embodiment of the present disclosure comprises providing a digital communication network (DCN) for a plurality of members of a population to communicate between each other; analyzing communications in the DCN between the plurality of members; determining one or more network characteristics based on the analysis of the communications; determining one or more phenotypical traits of an individual in the plurality of members; determining a contribution of the individual to a cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual and the one or more phenotypical traits of the plurality of members; and determining the assumed risk cost for a health care cost in the population based on the one or more network characteristics.

[0018] Any of the aspects herein, wherein the one or more network characteristics are characteristics of an individual member in the plurality of members, characteristics of other members of the plurality of members that the individual member is in communication with, and a strength and direction of the communications between the individual member and the other members.

[0019] Any of the aspects herein, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.

[0020] Any of the aspects herein, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member traits.

[0021] Any of the aspects herein, wherein the one or more phenotypical traits include one or more behavioral traits.

[0022] Any of the aspects herein, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising: determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members, wherein one or more phenotypical traits of the other members is weighted based on theAttorney Docket No. 774223: SEQ-019PCstrength and direction of the communications between the individual member and the other members.

[0023] Any of the aspects herein, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

[0024] Any of the aspects herein, wherein the analyzed communications only include forward communications.

[0025] Any of the aspects herein, wherein the DCN is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.

[0026] A system for determining an assumed risk cost according to at least one embodiment of the present disclosure comprises a computer processor; a data repository in communication with the computer processor and storing: one or more communications, each communication having a communication strength and a communication direction, one or more network characteristics having a characteristic of an individual member and characteristics of other members of the plurality of members, and an assumed risk cost, a digital communications network (DCN) which, when executed by the computer processor, provides a network for a plurality of members of a population to interact with at least each other or the DCN; and a server controller which, when executed by the computer processor: analyzes the one or more communications in the DCN between the plurality of members; identifies an individual member of the plurality of members based on the analysis of the one or more communications; determines the one or more network characteristics based on the analysis of one or more the communications, the one or more network characteristics comprising: the characteristics of the individual member, the characteristics of other members of the plurality of members that the individual member is in communication with, and the strength and the direction of the communications between the individual member and the other members; and determines the assumed risk cost for a health care cost in the population based on at least the one or more network characteristics.

[0027] Any aspect in combination with any one or more other aspects.

[0028] Any one or more of the features disclosed herein.

[0029] Any one or more of the features as substantially disclosed herein.

[0030] Any one or more of the features as substantially disclosed herein in combination with any one or more other features as substantially disclosed herein.Attorney Docket No. 774223: SEQ-019PC

[0031] Any one of the aspects / features / embodiments in combination with any one or more other aspects / features / embodiments.

[0032] Use of any one or more of the aspects or features as disclosed herein.

[0033] It is to be appreciated that any feature described herein can be claimed in combination with any other feature(s) as described herein, regardless of whether the features come from the same described embodiment.

[0034] The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

[0035] The phrases “at least one”, “one or more”, and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together. When each one of A, B, and C in the above expressions refers to an element, such as X, Y, and Z, or class of elements, such as XI -Xn, Yl-Ym, and Zl-Zo, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., XI and X2) as well as a combination of elements selected from two or more classes (e.g., Y1 andZo).

[0036] The term “a” or “an” entity refers to one or more of that entity. As such, the terms “a” (or “an”), “one or more” and “at least one” can be used interchangeably herein. It is also to be noted that the terms “comprising”, “including”, and “having” can be used interchangeably.

[0037] The preceding is a simplified summary of the disclosure to provide an understanding of some aspects of the disclosure. This summary is neither an extensive nor exhaustive overview of the disclosure and its various aspects, embodiments, and configurations. It is intended neither to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure but to present selected concepts of the disclosure in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other aspects, embodiments, and configurations of the disclosure are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.Attorney Docket No. 774223: SEQ-019PC

[0038] Numerous additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the embodiment descriptions provided hereinbelow.BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Aspects of the described embodiments are more evident in the following description, when read in conjunction with the attached Figures.

[0040] FIG. 1 shows a simplified block diagram of devices, in accordance with one or more embodiments.

[0041] FIG. 2 is a flow diagram that illustrates the operation of a method, in accordance with one or more embodiments.

[0042] FIG. 3 A shows an example of a computing system, in accordance with one or more embodiments.

[0043] FIG. 3B shows an example of a network, in accordance with one or more embodiments.DETAILED DESCRIPTION

[0044] Various embodiments are directed to determining health care risks costs using a digital communication network (DCN).

[0045] As previously described, determining a cost of assuming risks for a healthcare cost of a population (e.g., underwriting a risk cost) is well-developed using conventional factors such as a phenotype of a population, prior utilization of healthcare history of the population, and the population size and demographics. However, and as previously described, there are certain types of healthcare risks associated with certain phenotypes that are difficult to determine using conventional factors. For example, obesity may be contagious, however, the structure of the relationships between members at risk are unknown to those calculating the risks (e.g., risk underwriters). Thus, it is desirable to determine the structure of such relationships.

[0046] The system shown in FIG. 1 includes a data repository (100). The data repository (100) is a type of storage unit or device (e.g., a file system, database, data structure, or any other storage mechanism) for storing data (described below). The data repository (100) may include multiple different, potentially heterogeneous, storage units and / or devices.Attorney Docket No. 774223: SEQ-019PC

[0047] The data repository (100) stores communications (102). The communications (102) are communications from members in a digital communication network (DCN) (142) (described in more detail below). The communications (102) may be, for example, textbased, image -based, multimedia communications, audio communications, or any combinations thereof. The communications (102) may be between members, communications in a forum, communications with the DCN (142), or communications generated by the DCN (142) (e.g., communications from a conversation bot, also referred to as a chat bot). The communications (102) may include, for example, apost, a direct message, a comment, and / or visual and / or audio media by one or more members of the DCN (142) or generated by the DCN (142) (or a server controller such as a server controller (134) of the DCN (142)). The communications (102) may also be referred to as bot communications when the communications are generated and received from one or more conversation bots of the DCN (142). The communication (102) between members of the population can also be one to one, one to many, one to system, system to one, system to many.

[0048] The communications (102) may have attributes such as a communication strength (104) and / or a communication direction (106). The communication strength (104) correlates to whether a member of the DCN (142) would consider the communication (102). For example, a member may disregard communications (102), meaning the communication (102) may have a lower communication strength (104) than other communications (102) that the member may read and reflect on.

[0049] The communication strength (104) may be identified by common phrases or terms that are brought up during the communications. For example, if a term is consistently used by multiple members at least twice a day, then the communication strength (104) may be considered a “strong” communication with a higher communication strength (104). On the other hand, if a term is used by one or two members once a month, then the communication strength (104) may be considered a “weak” communication.

[0050] The communication direction (106) correlates to whether communications (102) of a topic are likely to spread or travel within a subset or all members of the DCN (142). For example, a communication (102) regarding how to relieve an adverse health condition may likely spread within a grouping of members with the adverse health condition. Thus, in such examples, the communication direction (106) may be high for the topic of the adverse health condition within the grouping of members with the adverse health condition.Attorney Docket No. 774223: SEQ-019PC

[0051] The communication direction (106) may be identified by terms that indicate the communication is trending towards a certain topic. For example, some members may begin showing interest in a certain type of food or beverage and in several weeks, more members may begin to show the same interest in the food or beverage. Such trend may indicate that the communication direction (106) is being directed towards the food or beverage.

[0052] The data repository (100) also stores network characteristics (108). The network characteristics (108) refer to characteristics of the members of the DCN (142). For example, the network characteristics (108) can refer to a structure of relationships between members of the DCN (142) and / or relationships between members and the DCN (142). The network characteristics (108) can include, for example, member characteristics (110) and member phenotypical traits (112) that can be used to inform or identify relationships between the members of the DCN (142) and / or relationships between members and the DCN (142).

[0053] The member characteristics (110) can include information about each member or groups of members. For example, the member characteristics (110) may include demographic information such as an age, income, employment, location of one or more members. The member characteristics (110) can also include information about an adverse health condition of the member(s).

[0054] The member phenotypical traits (112) are observable characteristics of the members or individual members of the DCN (142). For example, an individual’s height, eye color, and blood type are member phenotypical traits (112). In some embodiments, the member phenotypical traits (112) includes behavioral traits. Behavioral traits may include, for example, anxiety.

[0055] The data repository (100) also stores a member contribution (114). The member contribution (114) is an individual member’s contribution to an assumed risk cost (116). The member contribution (114) can correlate to, for example, actions taken by the individual member that negatively or positively contribute to the assumed risk cost (116). For example, an individual member that performs risky activities or activities that contribute to or increase an adverse health condition of the individual member may negatively impact the assumed risk cost (116). In the same example, the member contribution (116) may be a negative value or a negative factor when determining the assumed risk cost (116).

[0056] The data repository (100) also stores the assumed risk cost (116). The assumed risk cost (116) is a cost associated with a health care cost of a population. The health care cost can include expenses incurred by providers in the delivery of health care, expenses incurred by anAttorney Docket No. 774223: SEQ-019PCindividual member in maintenance, diagnostics, and treatment for an adverse health condition, expenses incurred by health care companies in developing and manufacturing medical devices, equipment, or pharmaceuticals, etc. The assumed risk cost (116) is a predicted cost of the health care cost of the population in the future. For example, the assumed risk cost (116) can be a predicted health care cost of the population in ten years.

[0057] The system shown in FIG. 1 may include other components. For example, the system shown in FIG. 1 also may include a server (130). The server (130) is one or more computer processors, data repositories, communication devices, and supporting hardware and software. The server (130) may be in a distributed computing environment. The server (130) is configured to execute one or more applications, such as a communication analyzer (136). An example of a computer system and network that may form the server (130) is described with respect to FIG. 3A and FIG. 3B.

[0058] The server (130) also includes a computer processor (132). The computer processor (132) is one or more hardware or virtual processors which may execute computer readable program code that defines one or more applications, such as the communication analyzer (136). An example of the computer processor (132) is described with respect to the computer processor(s) (302) of FIG. 3 A.

[0059] The server (130) also may include a server controller (134). The server controller (134) is software or application specific hardware which, when executed by the computer processor (136), controls and coordinates operation of the software or application specific hardware described herein. Thus, the server controller (134) may control and coordinate execution of the communication analyzer (136).

[0060] The server (130) also may include the communication analyzer (136). The communication analyzer (136) is software or application specific hardware which, when executed by the computer processor (132), analyzes communication (102) in the DCN (142) to, for example, determine the communication strength (104), the communication direction (106), and / or network characteristics (108). For example, the communication analyzer (136) may receive communications (102) from the individual member and output the communication strength (104), the communication direction (106), and / or network characteristics (108).

[0061] The server (130) also includes the DCN (142). The DCN (142) is a network through which members of a population can interact with each other, or with a system supported byAttorney Docket No. 774223: SEQ-019PCthe DCN (142). The DCN (142) can also interact with the members of the population and, for example, transmit communications (102) to one or more members of the DCN (142).

[0062] The system shown in FIG. 1 also may include one or more user devices (150). The user devices (150) may be considered remote or local. A remote user device is a device operated by a third-party (e.g., an end user of a chatbot) that does not control or operate the system of FIG. 1. Similarly, the organization that controls the other elements of the system of FIG. 1 may not control or operate the remote user device. Thus, a remote user device may not be considered part of the system of FIG. 1.

[0063] In contrast, a local user device is a device operated under the control of the organization that controls the other components of the system of FIG. 1. Thus, a local user device may be considered part of the system of FIG. 1.

[0064] In any case, the user devices (150) are computing systems (e.g., the computing system (300) shown in FIG. 3A) that communicate with the server (130). The user devices (150) may include awearable monitor (156). In an alternative embodiment, a separate wearable device may be in communication with the user device (150), such as a smart watch, or blood pressure monitor. The user devices (150) may also include a user input device (152) and / or a display device (154).

[0065] While FIG. 1 shows a configuration of components, other configurations may be used without departing from the scope of one or more embodiments. For example, various components may be combined to create a single component. As another example, the functionality performed by a single component may be performed by two or more components.

[0066] FIG. 2 is a logic flow diagram that illustrates a method, and a result of execution of computer program instructions, in accordance with various embodiments. The method can be used to determine an assumed risk cost in a population. The method can also, in other instances, be used in part to facilitate or accomplish at least one of: manage the health care cost of the population; reduce the health care risk in the population; and / or slow the progression of an adverse health condition. The adverse health condition can be, for example, chronic systemic inflammation, malaise, low energy, a disease, a health risk, social dysfunction, or a prodromal disease. In embodiments where the adverse health condition is a disease, the disease can be, for example obesity, diabetes, rheumatoid arthritis, Crohn’s disease, psoriasis, eczema, cardiovascular disease, congestive heart failure, chronic obstructive pulmonary disease, asthma, depression, or anxiety. In embodiments where theAttorney Docket No. 774223: SEQ-019PCadverse health condition is a health risk, the health risk can be, for example falling or becoming infected with an illness.

[0067] At Block 202, a step of providing a digital communication network (DCN) is provided. The DCN may be the same as or similar to the DCN (142). As previously described, members of the population enrolled in the DCN can communicate with each other or with the DCN itself. The members of the population can also communication with one or more conversation bots.

[0068] At Block 204, a step of analyzing communications in the DCN is provided. The communications may be the same as or similar to the communications (102) and may be analyzed by, for example, a communication analyzer such as the communication analyzer (136). The communications may be analyzed to determine network characteristics such as the network characteristics (108) as described in Block 208 below.

[0069] The communications may be analyzed to also determine a communication strength such as the communication strength (104) or a communication direction such as the communication direction (106). The communication strength and the communication direction may be determined by key words, words common in the communications, a frequency of words in the communication, etc. For example, if a term is consistently used by multiple members at least twice a day, then the communication may be considered a “strong” communication. On the other hand, if a term is used by one or two members once a month, then the communication may be considered a “weak” communication. Similarly, some members may begin showing interest in a certain type of food or beverage and in several weeks, more members may begin to show the same interest in the food or beverage. Such trend may indicate that the communications are being directed towards the food or beverage.

[0070] At Block 206, a step of identifying an individual member is provided. The step of Block 206 may be optional in some embodiments. The individual member may be identified from the analysis of the communications performed at Block 204. For example, the communications may be one or more posts by the individual member in the DCN and the DCN may identify that the individual member authored each of the one or more posts.

[0071] At Block 208, a step of determining the network characteristics is provided. The network characteristics may be determined from the analysis of the communications performed at Block 204. The network characteristics can include, for example, member characteristics such as the member characteristics (110) and member phenotypical traits suchAttorney Docket No. 774223: SEQ-019PCas the member phenotypical traits (112) that can be used to inform or identify relationships between the members of the DCN and / or relationships between members and the DCN.

[0072] As previously described, the member characteristics can include information about each member or groups of members. For example, the member characteristics may include demographic information such as an age, income, employment, location of one or more members. The member characteristics can also include information about an adverse health condition of the member(s). The member characteristics can be determined through the analysis of the communications performed in Block 204. For example, demographic information can be determined from member communications that share information about the member. The member characteristics can also be determined from member input received via a user device such as the user device (150). For example, the member characteristics can be derived from information that the member provided when joining the DCN.

[0073] At Block 210, a step of determining one or more phenotypical traits is provided. In some embodiments, the step of Block 210 is optional. As previously described, the member phenotypical traits observable characteristics of the members or individual members of the. For example, an individual’s height, eye color, and blood type are member phenotypical traits. In some embodiments, the member phenotypical traits includes behavioral traits. Behavioral traits may include, for example, anxiety. The behavioral traits may be derived from the analysis of communications on the DCN. In some instances, commons words associated with the behavioral traits may be tracked and monitored to determine such behavioral traits. For example, words such as “worry” or “anxious” appearing in the communications may be tracked and associated with anxiety.

[0074] The member phenotypical traits can be determined through the analysis of the communications performed in Block 204. For example, member phenotypical traits can be determined from member communications that share information about the member. The member phenotypical traits can also be determined from member input received via the user device. For example, the member characteristics can be derived from information that the member provided when joining the DCN.

[0075] At Block 212, a step of determining an assumed risk cost is provided. The assumed risk cost is the same as or similar to the assumed risk cost (116) of a health care cost of a population. As previously described, the health care cost can include expenses incurred by providers in the delivery of health care, expenses incurred by an individual member in maintenance, diagnostics, and treatment for an adverse health condition, expenses incurred byAttorney Docket No. 774223: SEQ-019PChealth care companies in developing and manufacturing medical devices, equipment, or pharmaceuticals, etc.

[0076] The assumed risk cost is determined based on the communication strengths, communication directions, and / or network characteristics such as member characteristics and / or member phenotypical traits. For example, communications directed towards acceptance of risky behaviors with a high communication strength may be used to increase the cost of assuming the risk of the health care cost in the population. In another example, communications between members indicating that the direction of the communications is trending towards healthy behaviors and positive lifestyle adjustments may decrease the cost of assuming the risk of the health care cost in the population.

[0077] In some embodiments, the strength of the communication relationships is used to weigh the phenotypical contribution to the cost of assuming a risk of a health care cost. For example, if a first member and a second member are communicating and have the same demographics and the same medical diagnosis but the first member is anxious and the second member is not during the communication, then the costs for the first member may have a high probability of being two, three, or four times more compared to the second member. Thus, identification and / or quantification of the strength of the communication (e.g., is one of the members stressed, upset, etc. during communications with another member) is useful in more accurately understanding financial risk.

[0078] In some embodiments, the network characteristics may be used to group the members of the population into subgroups that can be used in calculating the cost of assuming a risk of a health care cost in the population. For example, a subgroup may be based on members living in the same geographical area and such geographical area may indicate a higher level of risk for such members. Similarly, one or more characteristics of an individual of the population can be used in calculating the cost of assuming a risk of a health care cost in the population. For example, an individual that is a member of a subgroup may have communications with a system or another member indicating an increase in behavior that would increase the cost of assuming a risk of a health care cost in the population. The communication from the individual may indicate that the subgroup may also have an increase in the same behavior.

[0079] In other embodiments, phenotypical traits of the members who are in communication with the individual member of the population may be used. For example, the blood type of the members in communication with the individual member may be used in calculating theAttorney Docket No. 774223: SEQ-019PCcost of assuming a risk of a health care cost of the population. Further, the phenotypical traits of the individual may be used in calculating the cost of assuming a risk of a health care cost.

[0080] In some embodiments, only forward communications may be considered in calculating the cost of assuming a risk of a health care cost. For example, forward communications may include communications from a certain time period (e.g., when a new customer joins the DCN) and after the time period. In such example, communications prior to the time period may not be included in the analysis as it may not be available.

[0081] At Block 214, a step of determining the individual member contribution to the assumed risk cost is provided. In some embodiments, the step of Block 214 is optional. The individual member contribution may be the same as or similar to the member contribution (114). As previously described, the individual member contribution correlates to, for example, actions taken by the individual member that negatively or positively contribute to the assumed risk cost. For example, an individual member that performs risky activities or activities that contribute to or increase an adverse health condition of the individual member may negatively impact the assumed risk cost. In the same example, the member contribution may be a negative value or a negative factor when determining the assumed risk cost. In another example, the individual member may adopt healthy habits that improve their adverse health condition. In such examples, the individual member contribution may be a positive value or a positive value when determining the assumed risk cost.

[0082] The method described in FIG. 2 can include more or less steps. One or more steps or any combination of steps may also be repeated in the method described in FIG. 2.

[0083] One or more embodiments may be implemented on a computing system specifically designed to achieve an improved technological result. When implemented in a computing system, the features and elements of the disclosure provide a significant technological advancement over computing systems that do not implement the features and elements of the disclosure. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be improved by including the features and elements described in the disclosure.

[0084] For example, as shown in FIG. 3A, the computing system (300) may include one or more computer processor(s) (302), non-persistent storage device(s) (304), persistent storage device(s) (306), a communication interface (308) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities that implement the features and elements of the disclosure. The computer processor(s) (302)Attorney Docket No. 774223: SEQ-019PCmay be an integrated circuit for processing instructions. The computer processor(s) (302) may be one or more cores, or micro-cores, of a processor. The computer processor(s) (302) includes one or more processors. The computer processor(s) (302) may include a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), combinations thereof, etc.

[0085] The input device(s) (310) may include a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. The input device(s) (310) may receive inputs from a user that are responsive to data and messages presented by the output device(s) (312). The inputs may include text input, audio input, video input, etc., which may be processed and transmitted by the computing system (300) in accordance with one or more embodiments. The communication interface (308) may include an integrated circuit for connecting the computing system (300) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) or to another device, such as another computing device, and combinations thereof.

[0086] Further, the output device(s) (312) may include a display device, a printer, external storage, or any other output device. One or more of the output device(s) (312) may be the same or different from the input device(s) (310). The input device(s) (310) and output device(s) (312) may be locally or remotely connected to the computer processor(s) (302). Many different types of computing systems exist, and the aforementioned input device(s) (310) and output device(s) (312) may take other forms. The output device(s) (312) may display data and messages that are transmitted and received by the computing system (300). The data and messages may include text, audio, video, etc., and include the data and messages described above in the other figures of the disclosure.

[0087] Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a solid-state drive (SSD), compact disk (CD), digital video disk (DVD), storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by the computer processor(s) (302), is configured to perform one or more embodiments, which may include transmitting, receiving, presenting, and displaying data and messages described in the other figures of the disclosure.Attorney Docket No. 774223: SEQ-019PC

[0088] The computing system (300) in FIG. 3 A may be connected to, or be a part of, a network. For example, as shown in FIG. 3B, the network (320) may include multiple nodes (e.g., node X (322) and node Y (324), as well as extant intervening nodes between node X (322) and node Y (324)). Each node may correspond to a computing system, such as the computing system shown in FIG. 3 A, or a group of nodes combined may correspond to the computing system shown in FIG. 3 A. By way of an example, embodiments may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments may be implemented on a distributed computing system having multiple nodes, where each portion may be located on a different node within the distributed computing system. Further, one or more elements of the aforementioned computing system (300) may be located at a remote location and connected to the other elements over a network.

[0089] The nodes (e.g., node X (322) and node Y (324)) in the network (320) may be configured to provide services for a client device (326). The services may include receiving requests and transmitting responses to the client device (326). For example, the nodes may be part of a cloud computing system. The client device (326) may be a computing system, such as the computing system shown in FIG. 3A. Further, the client device (326) may include or perform all or a portion of one or more embodiments.

[0090] The computing system of FIG. 3 A may include functionality to present data (including raw data, processed data, and combinations thereof) such as results of comparisons and other processing. For example, presenting data may be accomplished through various presenting methods. Specifically, data may be presented by being displayed in a user interface, transmitted to a different computing system, and stored. The user interface may include a graphical user interface (GUI) that displays information on a display device. The GUI may include various GUI widgets that organize what data is shown, as well as how data is presented to a user. Furthermore, the GUI may present data directly to the user, e.g., data presented as actual data values through text, or rendered by the computing device into a visual representation of the data, such as through visualizing a data model.

[0091] Various operations described are purely exemplary and imply no particular order. Further, the operations can be used in any sequence when appropriate and can be partially used. With the above embodiments in mind, it should be understood that additional embodiments can employ various computer-implemented operations involving data transferred or stored in computer systems. These operations are those requiring physicalAttorney Docket No. 774223: SEQ-019PCmanipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

[0092] Any of the operations described that form part of the presently disclosed embodiments may be useful machine operations. Various embodiments also relate to a device or an apparatus for performing these operations. The apparatus can be specially constructed for the required purpose, or the apparatus can be a general-purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general-purpose machines employing one or more processors coupled to one or more computer readable medium, described below, can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.

[0093] The procedures, processes, and / or modules described herein may be implemented in hardware, software, embodied as a computer-readable medium having program instructions, firmware, or a combination thereof. For example, the functions described herein may be performed by a processor executing program instructions out of a memory or other storage device.

[0094] The foregoing description has been directed to particular embodiments. However, other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. Modifications to the above-described systems and methods may be made without departing from the concepts disclosed herein.Accordingly, the invention should not be viewed as limited by the disclosed embodiments. Furthermore, various features of the described embodiments may be used without the corresponding use of other features. Thus, this description should be read as merely illustrative of various principles, and not in limitation of the invention.

[0095] The present invention may be further exemplified by one, or a combination of one or more of, the following statements:

[0096] Statement 1. A method for determining an assumed risk cost in a population, the method comprising: providing a digital communication network (DCN) for a plurality of members of the population to communicate between each other; analyzing communications in the DCN between the plurality of members; determining one or more network characteristics of the communications based on the analysis of the communications; andAttorney Docket No. 774223: SEQ-019PCdetermining a cost of assuming risks for a health care cost in the population based on at least the one or more characteristics.

[0097] Statement 2. The method for determining an assumed risk cost of Statement 1, wherein the one or more network characteristics are characteristics of an individual member in the plurality of members, characteristics of other members of the plurality of members that the individual member is in communication with, and a strength and direction of the communications between the individual member and the other members.

[0098] Statement 3. The method for determining an assumed risk cost of Statement 2, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.

[0099] Statement 4. The method for determining an assumed risk cost of Statement 3, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member.

[0100] Statement 5. The method for determining an assumed risk cost of Statement 3, wherein the one or more phenotypical traits include one or more behavioral traits.

[0101] Statement 6. The method for determining an assumed risk cost of Statement 4, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising: determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members, wherein one or more phenotypical traits of the other members is weighted based on the strength and direction of the communications between the individual member and the other members.

[0102] Statement 7. The method for determining an assumed risk cost of Statement 1, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

[0103] Statement 8. The method for determining an assumed risk cost of Statement 1, wherein the analyzed communications only include forward communications.

[0104] Statement 9. The method for determining an assumed risk cost of Statement 1, wherein the system is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.Attorney Docket No. 774223: SEQ-019PC

[0105] Statement 10. The method for determining an assumed risk cost of Statement 9, wherein the biometric is derived from at least one of: a wearable device, a biometric sample, and answers to a survey or psychometric instrument.

[0106] Statement 11. The method for determining an assumed risk cost of Statement 10, wherein the wearable device is an accelerometer, a HR monitor, a HRV monitor, a continuous glucose monitor, a continuous ketone monitor, a skin temperature monitor.

[0107] Statement 12. A method for determining an assumed risk cost, the method comprising: providing a digital communication network (DCN) for a plurality of members of a population to communicate between each other; analyzing communications in the DCN between the plurality of members; determining one or more characteristics of the communications based on the analysis of the communications; determining one or more phenotypical traits of an individual in the plurality of members; and determining a contribution of the individual to a cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual and the one or more phenotypical traits of the plurality of members; and determining a cost of assuming risks for a health care cost in the population based on the one or more characteristics.

[0108] Statement 13. The method for determining an assumed risk cost of Statement 12, wherein the network one or more characteristics are characteristics of an individual member in the plurality of members, characteristics of other members of the plurality of members that the individual member is in communication with, and a strength and direction of the communications between the individual member and the other members.

[0109] Statement 14. The method for determining an assumed risk cost of Statement 13, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.

[0110] Statement 15. The method for determining an assumed risk cost of Statement 14, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member traits.

[0111] Statement 16. The method for determining an assumed risk cost of Statement 14, wherein the one or more phenotypical traits include one or more behavioral traits.

[0112] Statement 17. The method for determining an assumed risk cost of Statement 15, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising: determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the cost of assuming risks for aAttorney Docket No. 774223: SEQ-019PChealth care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members, wherein one or more phenotypical traits of the other members is weighted based on the strength and direction of the communications between the individual member and the other members.

[0113] Statement 18. The method for determining an assumed risk cost of Statement 12, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

[0114] Statement 19. The method for determining an assumed risk cost of Statement 12, wherein the analyzed communications only include forward communications.

[0115] Statement 20. The method for determining an assumed risk cost of Statement 12, wherein the system is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.

[0116] Statement 21. The method for determining an assumed risk cost of Statement 20, wherein the biometric is derived from at least one of: a wearable device, a biometric sample, and answers to a survey or psychometric instrument.

Claims

Attorney Docket No. 774223: SEQ-019PCCLAIMSWhat is claimed is:

1. A method for determining an assumed risk cost in a population, the method comprising:providing a digital communication network (DCN) for a plurality of members of the population to communicate between each other;analyzing communications in the DCN between the plurality of members; identifying an individual member of the plurality of members based on the analysis of the communications;determining one or more network characteristics based on the analysis of the communications, the one or more network characteristics comprising:characteristics of the individual member,characteristics of other members of the plurality of members that the individual member is in communication with, anda strength and a direction of the communications between the individual member and the other members; anddetermining the assumed risk cost for a health care cost in the population based on at least the one or more network characteristics.

2. The method for determining an assumed risk cost of claim 1, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.

3. The method for determining an assumed risk cost of claim 2, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member.Attorney Docket No. 774223: SEQ-019PC4. The method for determining an assumed risk cost of claim 3, wherein the one or more phenotypical traits include one or more behavioral traits.

5. The method for determining an assumed risk cost of claim 1, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising:determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the assumed risk cost for a health care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members,wherein one or more phenotypical traits of the other members is weighted based on the strength and direction of the communications between the individual member and the other members.

6. The method for determining an assumed risk cost of claim 1, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

7. The method for determining an assumed risk cost of claim 1, wherein the analyzed communications only include forward communications.

8. The method for determining an assumed risk cost of claim 1, wherein the DCN is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.

9. The method for determining an assumed risk cost of claim 8, wherein the biometric is derived from at least one of: a wearable device, a biometric sample, and answers to a survey or psychometric instrument.Attorney Docket No. 774223: SEQ-019PC10. The method for determining an assumed risk cost of claim 9, wherein the wearable device is an accelerometer, a HR monitor, a HRV monitor, a continuous glucose monitor, a continuous ketone monitor, a skin temperature monitor.

11. A method for determining an assumed risk cost, the method comprising:providing a digital communication network (DCN) for a plurality of members of a population to communicate between each other;analyzing communications in the DCN between the plurality of members; determining one or more network characteristics based on the analysis of the communications;determining one or more phenotypical traits of an individual in the plurality of members;determining a contribution of the individual to a cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual and the one or more phenotypical traits of the plurality of members; anddetermining the assumed risk cost for a health care cost in the population based on the one or more network characteristics.

12. The method for determining an assumed risk cost of claim 11, wherein the one or more network characteristics are characteristics of an individual member in the plurality of members, characteristics of other members of the plurality of members that the individual member is in communication with, and a strength and direction of the communications between the individual member and the other members.

13. The method for determining an assumed risk cost of claim 12, wherein characteristics of the other members includes at least one or more phenotypical traits and demographic aspects of the other members.Attorney Docket No. 774223: SEQ-019PC14. The method for determining an assumed risk cost of claim 13, wherein characteristics of the individual member include at least one or more phenotypical traits and demographic aspects of the individual member traits.

15. The method for determining an assumed risk cost of claim 13, wherein the one or more phenotypical traits include one or more behavioral traits.

16. The method for determining an assumed risk cost of claim 14, wherein the one or more characteristics are one or more phenotypical traits and the method further comprising:determining one or more phenotypical traits of the individual member; and determining a contribution of the individual member to the cost of assuming risks for a health care cost based on the one or more phenotypical traits of the individual member and the one or more phenotypical traits of the other members,wherein one or more phenotypical traits of the other members is weighted based on the strength and direction of the communications between the individual member and the other members.

17. The method for determining an assumed risk cost of claim 11, wherein the communication between members of the population is one to one, one to many, one to system, system to one, system to many.

18. The method for determining an assumed risk cost of claim 11, wherein the analyzed communications only include forward communications.

19. The method for determining an assumed risk cost of claim 11, wherein the DCN is an artificial intelligence bot that derives its communications from the analysis of the communications in the digital communication network or biometrics provided to the system.

20. A system for determining an assumed risk cost, the system comprising:Attorney Docket No. 774223: SEQ-019PCa computer processor;a data repository in communication with the computer processor and storing:one or more communications, each communication having a communication strength and a communication direction,one or more network characteristics having a characteristic of an individual member and characteristics of other members of the plurality of members, andan assumed risk cost,a digital communications network (DCN) which, when executed by the computer processor, provides a network for a plurality of members of a population to interact with at least each other or the DCN; anda server controller which, when executed by the computer processor:analyzes the one or more communications in the DCN between the plurality of members;identifies an individual member of the plurality of members based on the analysis of the one or more communications;determines the one or more network characteristics based on the analysis of one or more the communications, the one or more network characteristics comprising:the characteristics of the individual member,the characteristics of other members of the plurality of members that the individual member is in communication with, andthe strength and the direction of the communications between the individual member and the other members; anddetermines the assumed risk cost for a health care cost in the population based on at least the one or more network characteristics.

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