WO2016077727A1 - Method and apparatus for performing health risk assessment - Google Patents

Abstract

A method and computing device for performing a health risk assessment are provided. The method includes receiving personalized input data from one or more input data sources for a health risk assessment of an assessment candidate. The personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate. The personalized input data is pre-processed to select a time interval for the health risk assessment based on the demographic information. The personalized input data is processed using multiple mortality risk algorithms of a risk computation engine to determine a multiple mortality risk probabilities for selected time interval. The multiple mortality risk algorithms are associated different causes of death. The risk computation engine is arranged in a modular risk computation framework. The modular risk computation framework is configured for re-use by each mortality risk algorithm. The computing device includes an input data source interface and a processor.

Description

METHOD AND APPARATUS FOR PERFORMING HEALTH RISK ASSESSMENT

CROSS-REFERENCE TO RELATED APPLICATION

[0001 ] This application is based on and claims priority to U.S. Provisional Application No. 62/080,149, filed November 14, 2014, which is incorporated herein by reference in its entirety.

STATEMENT REGARDING FEDERALLY FUNDED RESEARCH

[0002] This invention was made with government support under Grant Nos. RO1 HS020919 awarded by the Agency for Healthcare Research and Quality (AHRQ). The government has certain rights in the invention.

BACKGROUND

[0003] This disclosure relates to a computerized health information tool for health risk assessments of individuals, including patients, employees, and other persons. In various embodiments, the disclosed health risk assessment (HRA) can compute and report an individual's probability of dying from specific diseases over a defined period of time based on various criteria, including demographic, medical, behavioral, and lifestyle information.

[0004] Over half of all deaths before age 65 can be attributed to lifestyle factors. In order to reduce the annual incidence of these causes of death, it is beneficial to understand the contribution of various lifestyle factors to these deaths. Tools that can help to assess the impact of these precursors of disease and trauma include the methodology of health risk appraisal. Many of the decisions made in the course of development of a health risk appraisal instrument are inherently transient and subject to constant improvement and customization. Various factors contribute to the definition of any health risk appraisal.

[0005] Dating to the fifth century B.C., the Hippocratic tradition emphasized prognostication and prevention, using patient-centered regimens of dietetics and exercise to maintain or regain health. But it was not until 1968 that a system for appraising the health risks for individuals was first proposed in the practice literature as a component of comprehensive healthcare. Developed through a pilot study initiated in 1963, the method used a four-part rubric to assist physicians in assessing and mitigating adult patients' health risks: 1 ) Basic average health hazards by age, sex, and race over a 10-year period; 2) Health hazards for the individual, reflecting history and physical examination, routine tests, and specialized tests and consultations; 3) Factors for adjusting individual health hazards; and 4) Individualized preventive medicine programming reflecting the whole-person concept.

[0006] In 1970, a manual for physicians, Robbins et al., How to Practice Prospective Medicine, Methodist Hospital, Indianapolis, Indiana, provided a sample HRA questionnaire, risk computations, and a feedback strategy. Although the medical profession did not generally adopt HRAs, instruments proliferated elsewhere, most notably through workplaces and community-based health promotion programs. In 1980, CDC released publicly available HRA software that used a 31 -item, self-administered questionnaire to compute adult health risk. In 1986, CDC collaborated with the Carter Center of Emory University in Atlanta to review the scientific basis for individual HRAs and began a program to distribute HRA software through state public health programs. At the end of that project, the HRA program was transferred to the Atlanta-based Carter Center, where it continued until 1991 . At that time, a nonprofit corporation, Healthier People Network, was established to keep the HRA in the public domain.

[0007] For these and other reasons, there is a need to define an improved process for computerized health risk assessments that exploits a variety of information and interrelationships between various causes of death in relation to various source data characterizing behaviors and lifestyles for assessment candidates.

SUMMARY

[0008] In one aspect, a method for performing a health risk assessment is provided. In one embodiment, the method includes: receiving personalized input data at a computing device from one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate; pre-processing the personalized input data at the computing device to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; and processing the personalized input data at the computing device using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.

[0009] In another aspect, an apparatus for performing a health risk assessment is provided. In one embodiment, the apparatus includes: one or more input data source interfaces configured to receive personalized input data from a corresponding one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate; and at least one processor configured to pre-process the personalized input data to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; wherein the at least one processor is configured to process the personalized input data using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.

[0010] In yet another aspect, a non-transitory computer-readable medium storing program instructions that, when executed by a processor, cause a computing device to perform a method for performing a health risk assessment. In one embodiment, the method includes: receiving personalized input data at a computing device from one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate; pre-processing the personalized input data at the computing device to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; and processing the personalized input data at the computing device using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.

[001 1 ] Further scope of the applicability of the present invention will become apparent from the detailed description provided below. It should be understood, however, that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art.

DESCRIPTION OF THE DRAWINGS

[0012] The present invention exists in the construction, arrangement, and combination of the various parts of the device, and steps of the method, whereby the objects contemplated are attained as hereinafter more fully set forth, specifically pointed out in the claims, and illustrated in the accompanying drawings in which:

[0013] FIG. 1 is a drawing of a display device with a graphic display of a current risk age and a target risk age resulting from a health risk assessment (HRA) using an exemplary embodiment of an HRA system;

[0014] FIG. 2 is an account creation dialog window for an exemplary embodiment of an HRA system;

[0015] FIG. 3 is a graph showing current and target risks for common causes of death resulting from performing an HRA using an exemplary embodiment of an HRA system; [0016] FIG. 4 is a functional diagram of HRA operations for an exemplary embodiment of an HRA system;

[0017] FIG. 5 is a table showing precursors and relationships to causes of death in conjunction with performing an HRA using an exemplary embodiment of an HRA system;

[0018] FIG. 6 is a table showing optimum values for modifiable precursors in conjunction with performing an HRA using an exemplary embodiment of an HRA system;

[0019] FIG. 7 is a graph showing precursors and risk years gained in conjunction with adopting the recommended actions presented in the adjacent text frame in conjunction with performing an HRA using an exemplary embodiment of an HRA system;

[0020] FIG. 8 is a table showing causes of death and modifiable precursors associated therewith in conjunction with performing an HRA using an exemplary embodiment of an HRA system;

[0021 ] FIG. 9 is a table showing a comprehensive list of causes of death used in conjunction with performing an HRA using an exemplary embodiment of an HRA system and the corresponding current, target, and average risk calculations;

[0022] FIG. 10 is a table showing recommended preventive services for men and women based on age in conjunction with performing an HRA using an exemplary embodiment of an HRA system;

[0023] FIG. 1 1 is a flow chart of an exemplary embodiment of a workflow process in conjunction with performing a health risk assessment using an exemplary embodiment of an HRA system;

[0024] FIG. 12 is a block diagram showing an exemplary embodiment of an HRA system in which a play framework is integrated with risk and report computations for a health risk assessment;

[0025] FIG. 13 is a flow chart of an exemplary embodiment of a process for performing a health risk assessment using an exemplary embodiment of an HRA system; and [0026] FIG. 14 is a flow chart of an exemplary embodiment of a process for performing a health risk assessment; and

[0027] FIG. 15 is a block diagram of an exemplary embodiment of a computing device for performing a health risk assessment.

DETAILED DESCRIPTION

[0028] The disclosed health risk assessment (HRA) enables a patient (the term "patient" broadly encompasses hospital in-patients, out-patients, as well as persons not currently under medical care who are receiving a health risk assessment screening provided by an employer, local health food establishment, or so forth) to get a handle on the patient's health with a rapid HRA. The HRA shows how lifestyle choices affect the patient's health, and enables the patient to see what can be done by the patient to be healthier and live a longer life.

[0029] In one aspect, embodiments of the disclosed HRA enable the patient to find his or her Risk Age. The Risk Age informs the patient of how many years of life can be gained (in a statistical or actuarial sense) by lifestyle improvements and lowering other health risks. The Risk Age may, for example, be presented graphically on a display device (e.g. a computer display device) such as in the illustrative display example of FIG. 1 (where the abscissa of the plot is age in years running from 0-65 years in this example).

[0030] In some embodiments the HRA is a web-based system. Patient privacy concerns (e.g. HIPAA compliance) are addressed by employing conventional secure- socket connection or other Internet traffic security system(s) in conjunction with a user password. A suitable account creation dialog window via which a patient may create an account with the HRA system is presented in FIG. 2 (this is merely an illustrative example).

[0031 ] In another approach, data entry may be performed by hospital staff, for example a nurse consultant who works with the patient to perform the web-based health risk assessment, in which case the account may be integrated with the patient's Electronic Medical Record (EMR) and hence be protected by the patient privacy security mechanisms of the EMR. In this approach, patient demographic, medical, behavioral and lifestyle information can be collected for input by hospital staff or transferred directly from the EMR to the HRA.

[0032] Embodiments of HRA disclosed herein represent a mechanism to describe a person's chances or risks of becoming ill or dying from diseases and other causes. Feedback in the form of a report can help a person decide how to reduce their risks.

[0033] An exemplary embodiment of an HRA system estimates and describes a patient's (i.e. person's) chances of becoming ill or dying from certain diseases (e.g. high blood pressure, heart disease) and other causes (e.g. smoking, not wearing seat belts) over a certain period of time (e.g. 10 years). In some embodiments, the HRA processing includes: 1 ) reviewing information on a person's lifestyle and health behaviors, laboratory values and physical measures; 2) estimating the risk of death and/or illness (current Risk Age); 3) estimating how much risk can be reduced based on epidemiologic data, mortality statistics, and actuarial techniques, thus yielding a target Risk Age; and 4) providing feedback in the form of a report generated based on the patient's current and target Risk Age values. The HRA provides a proactive response to the risk factors that cause disease or injury. By informing the patient of the patient's risks for the onset of a given disease, priorities and programs can be developed to reduce these risks potentially forestalling or eliminating the disease or condition.

[0034] Over half of all deaths before age 65 can be attributed to lifestyle factors, such as: 1 ) patient smoking and/or patient use of smokeless tobacco; 2) alcohol consumption and/or other substance abuse; 3) patient nutrition, exercise, and stress; 4) driving habits, seat belt usage, and ATV (all-terrain vehicle) usage by the patient; and 5) the extent to which the patient makes use of preventive services such as mammograms, pap smears and colonoscopies. Such factors are also referred to herein as "precursors."

[0035] Now, more than 40 years after publication of the first method of performing a clinical health assessment, HRAs are accepted processes to identify an array of risk factors associated with developing specific acute or chronic disease conditions. Further, HRAs offer providers a tool for recommending clinical preventive screenings and treatment to support patients' health improvement efforts. [0036] Although the HRA was originally developed as a hand-tallied instrument to collect health risk data from individuals to produce a personalized epidemiological- based profile predicting future mortality, it has since evolved into an interactive electronic tool that provides a personal health assessment score such as a "health age" or "risk age," tailored educational messages, on-line modeling of the effects of making lifestyle changes, goal setting guidance, and other resources to motivate behavior change and achieve risk reduction.

[0037] Tools that can help to assess the impact of precursors of disease and trauma include the methodology of health risk appraisal. Many of the decisions made in the course of development of a health risk appraisal instrument are inherently transient and subject to constant improvement and customization. Several factors contribute to the definition of a health risk appraisal, such as: age and sex; culture of the target population; selection of causes of illness, injury, or death; identification of the precursors of these outcomes; and the quality of the synthesis of the underlying science. Disclosed HRA system embodiments provide a personal health assessment score such as a "health age" or "risk age," tailored educational messages, on-line modeling of the effects of making lifestyle changes, goal setting guidance, and other resources to motivate behavior change and achieve risk reduction.

[0038] The current Risk Age is one of the values provided by the generated HRA report. The current Risk Age compares the patient's total risk from all causes of death to the total risk of a comparable population, e.g. members of the population of the same age range and sex. If the patient has a lot of risk factors, the current Risk Age will go up because your risk of dying will increase and therefore be similar to someone older than you are who will die in a shorter number of years than an average person of your age. The current Risk Age gives the patient an idea of his or her risks compared with the population average in terms of an age. For example, a chronologically 40 year old male can readily comprehend that if the HRA states that he has the health of a 55 year old male, he is not as healthy as he could be.

[0039] The patient's target Risk Age is another value provided by the generated HRA report. The target Risk Age indicates what the patient's risk age would be if the patient made lifestyle changes recommended in the report thereby reducing the patient's risks. Thus the target Risk Age is lower than the current Risk Age (except in the case of the rare individual who has no risk factors showing up on the questionnaire at all - in that rare case the target Risk Age equals the current Risk Age).

[0040] A Risk Age (also sometimes referred to herein as an appraised age) is thus an overall measure of risk based on the patient's current risk levels as compared with a hypothetical "average" person in the same age range and of the same sex. A Risk Age is not a "biological" or "chronological" age. A Risk Age is also not a life expectancy estimate (such as might be generated by an actuarial analysis). Rather, the Risk Age is an intuitively understandable numerical indicator intended to enable the patient to compare modifiable risk with peers. An appraised age that is the same as the actual age signifies that the patient is an average risk level for the patient's age and sex group in the general U.S. population. Similarly, higher appraised ages signify above average risk and lower appraised ages indicate lower than average risk compared with a cohort with the same fixed characteristics. In general risk age is built on the concept that overall mortality risk increases geometrically with age at about 8% per year.

[0041 ] One illustrative embodiment designed for middle-age adults (HRA Midlife version) includes specific risk prediction algorithms for each of 19 different causes of death (CODs), each of which carries its own specific validity. Consider heart attack and stroke as illustrative examples. For heart attack and stroke, the prediction models in this illustrative embodiment are derived from the Framingham Heart Study data (D'Agostino, Russell, Huse, Ellison, et al., 2000; D'Agostino, Belanger, Markson, Kelly- Hayes, & Wolf, 1995; Wolf, D'Agostino, Belanger, & Kannel, 1991 .). This longitudinal study, covering more than a half century, is probably one of the best examples of predictive validity in the medical literature. In this illustrative adult HRA, heart attack and stroke combined account for approximately 30 percent of the mortality outcomes. For an additional 24 causes of death covered in the illustrative HRA Midlife version, the research base was not adequate to identify with reasonable validity causal risk factors for use in risk prediction. For these 24 causes of death, individuals receive an estimate based age, race, sex, and population average mortality only. For all 43 causes of death, an actuarial extension procedure is employed with age projections tailored to specific age, race, and gender cohorts. Mortality estimates are based on current nationally representative mortality statistics averaged over three years. The Centers for Disease Control and Prevention (CDC) National Center for Health Statistics database was used. This database includes data from approximately 7.8 million death certificates. For practical purposes, this represents population data. In addition to estimating risk for 43 causes of death, the HRA Midlife version produces an estimate of Risk Age (current and target). As already described, Risk age compares the calculated risks based on a person's answers with the population average and is a tool that can help individuals understand the potential benefits for adopting healthy behaviors and avoiding health hazards.

[0042] In one illustrative HRA system implementation, performing a health risk assessment entails the following actions. The patient answers questions about his or her health risks. That is, the patient fills out the HRA system questionnaire, which preferably employs real-time branching to avoid presenting the patient with questions that are irrelevant to the patient, e.g. if the patient identifies as a nonsmoker then questions specific to smokers, such as "How many packs per day do you smoke?" are not presented. The HRA system then executes risk assessment algorithms and presents a report, which in some embodiments includes cause-of-death-specific current and target risks presented in an intuitive format such as the illustrative bar graph shown in FIG. 3.

[0043] The HRA system can be variously embodied. In one configuration, a server computer is programmed to perform he risk assessment for a user (i.e. patient), and the HRA system further includes a user interface computer connected with the server computer via the Internet, the user interface computer being operated by the user to input information used by the server computer in performing the health risk assessment for the user. In another configuration, the HRA system is a standalone system executing on a non networked computer that performs both user information input and health risk assessment computation functions. The HRA system may also be embodied as a non-transitory storage medium storing instructions readable and executable by a computer (e.g. the server computer, or the standalone computer, depending upon the configuration) to perform the health risk assessment for a user based on information including the user's age, the user's sex, information on the user's behaviors, and medical information of the user, and optionally further including the user's race/ethnicity. The information may be variously acquired, for example by acquiring at least a portion of the information by reading the user's Electronic Medical Record (EMR) and/or by acquiring at least a portion of the information by the user (or a nurse or other health practitioner) entering the information via a user interface computer (or via the standalone computer in that configuration). The non-transitory storage medium may, for example, comprise a hard disk or other magnetic storage medium, a flash memory or other electronic storage medium, an optical disk or other optical storage medium, a network-based RAID, or so forth.

[0044] In one embodiment, an exemplary embodiment of an electronic HRA system includes a computer programmed to perform an HRA for a user. In another embodiment the electronic HRA system is configured to compute and display or print a current Risk Age representing the total risk from all causes of death for the user to the total risk of a population comparable to the user, the current Risk Age being in units of years where an older Risk Age indicates a higher risk of death and a younger Risk Age indicates a lower risk of death. In a further embodiment of the electronic HRA system, the Risk Age is configured to correspond to the age of a person having the remaining life expectancy predicted by the HRA system for the user.

[0045] In yet another embodiment, the electronic HRA system includes a questionnaire component via which the user inputs health information, the questionnaire component employing a real-time branching logic in which questions made inapplicable to the user based on previously entered answers are hidden from the use. In still another embodiment, the electronic HRA system stores formulas used in computing the risk assessment in database tables. In still yet another embodiment, the HRA system computes risk assessment for at least one cause of death based on age, sex and race/ethnicity.

[0046] In another embodiment, the HRA system computes a total risk assessment for a user based on inputs including the age, sex and race/ethnicity of the user. In yet another embodiment, the HRA system is configured to acquire information, including the user's age, the user's sex, information on the user's behaviors, and medical information of the user; estimate a current risk of death for the user based on the acquired information; estimate a target risk of death based on how much the current risk of death risk can be reduced by modifications to the user's behavior; and generate a report including the patient's current risk of death and the user's target risk of death values. In a further embodiment, the current risk of death is represented as a current Risk Age and the target risk of death is represented as a target Risk Age. In this embodiment, the Risk Age is the age of a person having the remaining life expectancy predicted by the HRA system for the user and the target Risk Age is less than or equal to the current Risk Age. In another further embodiment, the information on the user's behaviors includes information on the user's diet, exercise, alcohol use, and tobacco use. In yet another further embodiment, the report includes information on how the user's current risk of death is related to items of a questionnaire used to acquire at least a portion of the information.

[0047] In yet another embodiment of the HRA system, the computer is programmed to perform the health risk assessment for the user including risk of death due to Alzheimer's disease. In still another embodiment of the electronic HRA system, the computer is programmed to perform the health risk assessment for the user including risk of death due to distracted driving.

[0048] In still yet another embodiment of the electronic HRA system, the computer is programmed to perform the health risk assessment comprising a risk of death over a plurality of time intervals. In a further embodiment, the plurality of time intervals includes 20 years, 10 years, and 5 years.

[0049] In another embodiment of the electronic HRA system, the computer is programmed to perform the health risk assessment comprising a risk of death over a time interval chosen based on the user's age. In a further embodiment, the time interval is chosen as 20 years if the user's age is 18-39, 10 years if the user's age is 40-64, and 5 years if the user's age is greater than 64.

[0050] In yet another embodiment of the electronic HRA system, the computer is a server computer and the HRA system further includes a user interface computer connected with the server computer via the Internet, the user interface computer being operated by the user to input information used by the server computer in performing the health risk assessment for the user. [0051 ] Various exemplary embodiments of a non-transitory storage medium store instructions readable and executable by a computer to implement an electronic HRA system configured to provide various combinations of the features disclosed herein.

[0052] An exemplary embodiment of an method for performing an HRA for a user includes: inputting to a computer information including the user's age, the user's sex, information on the user's behaviors, and medical information of the user; using the computer, estimating a current risk of death for the user based on the acquired information; and displaying or printing a report including the patient's current risk of death.

[0053] In another embodiment, the method also includes using the computer, estimating a target risk of death based on how much the current risk of death risk can be reduced by modifications to the user's behavior. In this embodiment, the report further includes the user's target risk of death. In a further embodiment, the current risk of death is represented as a current Risk Age and the target risk of death is represented as a target Risk Age. In this embodiment, the Risk Age is the age of a person having the remaining life expectancy predicted by the HRA system for the user and the target Risk Age is less than or equal to the current Risk Age.

[0054] In yet another embodiment of the method, the information on the user's behaviors includes information on the user's diet, exercise, alcohol use, and tobacco use. In still another embodiment of the method, the report includes information on how the user's current risk of death is related to items of a questionnaire used to acquire at least a portion of the information. In still yet another embodiment, the method also includes: estimating the current risk of death including risk of death due to Alzheimer's disease. In another embodiment, the method also includes estimating the current risk of death including risk of death due to distracted driving. In yet another embodiment of the method, the information inputted to the computer further includes the user's race/ethnicity, and estimating the current risk of death for the user is further based on the user's race/ethnicity.

[0055] The various embodiments of the HRA system disclosed herein are designed to support adults in understanding their health risks. The HRA system promotes health behavior change, with the goal of reducing risks and living a healthier- life. The HRA system can be implemented in any desired language, including English. Survey questions are completed in a computerized form, via a web browser or tablet computer. Risk computation software libraries (residing on a local computer or on a server) produce individual risk reports. Group reports (optionally anonymized) can be created via queries from a provider/administrator interface.

[0056] An HRA can be operationally defined in four component parts: 1 ) an epidemiological, biomedical, and behavioral database; 2) the participant's (i.e. "patient's") database, most generally derived from a questionnaire made up of items that directly or indirectly assess a known precursor of the health outcome. (An example is "Do you smoke?" as a known precursor of lung cancer.); 3) a model, statistical or otherwise, for weighting risk indicators, generally referred to herein as the "algorithm." These can be both quantitative and qualitative. Where possible, emphasis is placed on quantitative estimates; and 4) the output or feedback component, which is the Participant's risk assessment report. This component may also contain a series of recommendations regarding how risks can be reduced. It is designed so that local information on relevant community resources can be included in a future localized version.

[0057] Various embodiments of an HRA can be directed to one or more age groups using abbreviated or more comprehensive input data, such as: 1 ) adults ages 18 to 64; 2) older adults ages 65+ using a 20-minute questionnaire; and 3) older adults ages 65+ using a more comprehensive questionnaire. The HRA can be compatible with PC, Macintosh, or both. The HRA can be implemented in a standalone computer, networked computer, or in a client-server arrangement. HRA software can be run on the Internet, a local area network or intranet, or as standalone software on a non- networked PC or Mac. iPad and Android Tablet versions of the HRA software are also available.

[0058] HRA reports are individualized based on participant characteristics. Fundamental differences in health, health behaviors and health risks exist based upon a participant's age, sex and race/ethnicity. For an exemplary embodiment, an individual report may include: 1 ) date of report; 2) age and sex of participant; 3) Current and Target Risk Age; 4) graphical summary of ways to reduce risk, ordered according to magnitude of risk reduction; 5) comprehensive table of mortality risks for multiple (e.g., 43) causes of death; 6) positive feedback regarding ways a participant is living healthy; 7) recommended lifestyle changes; 8) a customizable action plan; 9) results summary for healthcare providers; and 10) a question by question explanation of how participant answers relate to risks. Exemplary reports may include text and images, and some interactivity including collapsing/opening optional elements to reduce screen clutter and selection of options for living healthier in the action plan. Selected links to helpful resources, and messaging tailored to each participant based upon their responses is also included.

[0059] As for older adults (e.g., adults aged 65-90), an HRA older adult report may include a midlife HRA report and also risk estimates in the areas of mental and physical morbidity and functional status. For elderly participants (here delineated as of age 91 or greater), the report includes risk estimates in the areas of mental and physical morbidity and functional status and may omit mortality risk computation and information. The elderly adult report includes the option for elderly participant to read further information on topics of interest from the National Institute on Aging.

[0060] Administrators of the HRA system can have the ability to manage users, review responses and generate reports based on historical responses. Administrators can also download customized group summary data for a batch of HRA participants. This enhances the ability of the health care administrators to use the HRA program to set priorities for intervention programs designed to reduce health risks for the population of interest. Complete data can be downloaded via an export function that allows the HRA Administrative user to export the questionnaire responses and the risk estimates to a data file for research purposes. Role-based access control limits the authority of users and is determined and set by the Software Administrators only.

[0061 ] An HRA is an information tool that reports an individual's probability of dying from specific diseases over a defined period of time. It should not be confused with traditional medical examinations that detect and diagnose disease. Increasingly, health care providers and health care systems have begun to embrace the use of health risk assessment in routine primary care. The use of HRA's in primary care will continue to develop as an important means of promoting screening and prevention and reaching shared decisions between patients and providers.

[0062] The objectives of the HRA are to 1 ) identify precursors (modifiable and non-modifiable risk factors) that are associated with poor health outcomes; and 2) communicate information on how to reduce risks and live healthier to participants and their providers. The HRA system quantifies the probably impact of both risk behaviors and positive changes to risk behaviors for each individual.

[0063] The HRA uses an individual's health-related behaviors and personal characteristics, U.S. mortality statistics, and epidemiologic data to compute that individual's probability of dying, for example, in the next 10 years, from 43 different causes of death, including heart attack, cancers, and injuries. The questionnaire covers such habits as smoking, seat belt use, and exercise. In addition, physiological data such as weight, blood pressure, and cholesterol are requested. A combination of form validation rules and missing value estimation strategies are used to assure the most accurate risk estimates, even in the presence of user data entry errors.

[0064] The HRA system is primarily designed for individuals who are largely free of serious illness, such as cancer, heart disease or kidney disease. While many of the recommendations will be practically useful for just about any person, those with serious health problems or disability may find information in the reports to be inaccurate or not in-line with their individual circumstances. Persons with diabetes, high blood pressure, and many other acute/chronic problems can complete an HRA and receive useful feedback, but the usefulness and accuracy of that feedback will vary according to the level of severity of their current health conditions and their history of life-threatening complications (such as heart attack or stroke). Especially in the case of persons with existing serious illness, it is strongly recommended that professional interpretation and follow-up be provided to clarify any potential issues or inaccuracies.

[0065] With reference to FIG. 4, an exemplary embodiment of HRA operations by which the HRA estimates mortality risk is shown in a functional block diagram. For example, HRA operations can provide an adult (midlife) HRA.

[0066] With reference to FIG. 5, an exemplary matrix showing relationships between questions for an HRA and certain causes of death is shown in tabular form. The table includes columns relating to HRA survey questions and rows relating to 43 causes of death covered by the exemplary HRA system. Where a checkmark occurs in a cell of the matrix, there is a known causal relationship between the risk factor covered by the specific HRA question specified in that column and the specific cause of death specified in that row.

[0067] Because most of the prediction algorithms are based on the national death certificate data and these data are age/sex specific, all causes of death have checkmarks in the sex and age columns. Thus, for example, the prediction algorithm for Lung Cancer, row 10, is based on the Sex and Age questions, 1 and 2, and four smoking questions, 15, 16, 17a, and 17b. The Cause of Death-by Risk-Factor matrix of FIG. 5 thus presents an overview of the scientific content of an exemplary midlife HRA system.

[0068] The various embodiments of the HRA systems described herein can implement any suitable combination of the features described herein. For example, an exemplary embodiment of the HRA system may be configured to function on the Internet, although a standalone (non networked) version is also contemplated. Exemplary HRA systems may be implemented in Java and Javascript, Objective C, and Android. An exemplary embodiment of the HRA system may include client-based risk estimation Javascript software libraries. Exemplary HRA systems may include client- based risk estimation Java software libraries. An exemplary embodiment of the HRA system may include server-based risk estimation Java software libraries. Exemplary HRA systems may employ a database driven model for risk computation. For example, instead of hard coding questions, questionnaire position, algorithms, equations, parameters and variables, report contents, report visualization and report messages, these items are instead stored in a database. The database contains risk values or tabular information for risk computation and, in some cases, the actual code that is used to generate and render the pages seen by the user. This has numerous advantages, such as updating the system to incorporate a more current formula (based on new research or updated population statistics reflecting changes in the population) can be implemented by updating the relevant database tables (e.g. represented as spreadsheets, relational database tables, or the like) without modifying the underlying code.

[0069] An exemplary embodiment of the HRA system may employ a generalized risk computation method, through which multiple risk algorithms can be calculated using a single software function. This improves performance by eliminating the need for sequential computation of risks for all 43 risk factors. Exemplary HRA systems may output full color visual displays of calculated risk information, and/or histograms, bar charts and other graphics that replace or augment reported numerical values. An exemplary embodiment of the HRA system may implement modular software, such that specific components can be added or removed without compromising the integrity of the rest of the HRA system. For example, a PHQ-9 depression scale algorithm can be included (or not) as a modular component of the software. Similarly, messages that are included in reports can be customized for particular organizations and/or populations.

[0070] An exemplary embodiment of the HRA system may implement real-time branching logic for the questionnaire. In this technique, questions are presented or hidden in real-time as the user answers preceding questions. For example, regarding smoking status, respondents are presented with three response options of "current smoker," "used to smoke," and "never smoked." For those who respond "never smoked" the questions for "current smoker" and "used to smoke" are hidden.

[0071 ] Exemplary HRA systems may implement an action plan based on Prochaska's trans-theoretical model of change that provides feedback tailored to an individual's stage of readiness to change. Behaviors assessed by the HRA system may include diet, exercise, alcohol use and smoking. An exemplary embodiment of the HRA system may include an interactive section in the report that provides real-time opportunities for respondents to select various risk behaviors and specific actions to change them. Action plans use may use computed risks, sophisticated branching logic, and user choices (stages of change above for example) to present patients with actionable items tailored to their unique conditions, behaviors and risks. Exemplary HRA systems may use report messages and action plan items that are prioritized and displayed dynamically according to patient real-time responses and the potential for maximum health benefit. An exemplary embodiment of an HRA system may provide generated reports that include a report section tailored to providers, for use in conjunction with primary care wellness visits. This may include internationally recognizable icons (color coded faces) to indicate three risk levels across cardiovascular risk indicators, personal risk factors, preventive service risks, diet and exercise, overall health, readiness to change stage and atherosclerotic cardiovascular disease.

[0072] Exemplary HRA systems may provide a questionnaire summary page with navigation that allows users to review and edit their responses. An exemplary embodiment of the HRA system may implement reports that include a question-by- question section that provides detailed information regarding how the user's risks are related to each item in the questionnaire. Exemplary HRA systems may provide a comprehensive table of risks that are displayed or printed for all causes of death in the report. The comprehensive table may include information on number of death per thousand in the next 5, 10 or 20 years for "men/women like you," "men/women like you who live healthy" and "men/women on average." These risks in the comprehensive table may be presented based on age and race of respondent.

[0073] An exemplary HRA system may include Alzheimer's disease as a cause of death for use in mortality risk calculation. Exemplary HRA systems may include risk algorithms for cancers, heart attack, stroke and motor vehicle injury that are based on current population risk behavior data. Suitable sources for this data include U.S. National Mortality Statistics, National Safety Council, National Health Interview Survey (NHIS) and updated Framingham Study data. Up-to-date mortality data may be used for causes of death. Suitable sources for this data include the Centers for Disease Control (CDC). An exemplary HRA system may use sex, age, and race/ethnicity specific mortality tables in conjunction with the risk algorithms. Use of race/ethnicity- specific mortality tables, in particular, provides more accurate predictions for persons of minority racial and ethnic backgrounds.

[0074] Exemplary HRA systems may calculate mortality risks differently based upon age. For example, younger users (e.g. ages 18-39) may receive 20 year risks, middle aged (e.g. ages 40 to 64) users may receive 10 year risks, and older users (e.g. over age 64) may receive 5 year risks. In another exemplary embodiment, risks may provide for 20, 10, and 5 years (or other selected time horizons) for users of any age.

[0075] An exemplary HRA system may include an algorithm for incorporating the risk of driving distracted (for example, on cell phone, texting or e-mailing). Suitable sources for this data include the National Safety Council.

[0076] Exemplary HRA systems may update algorithms based on national risk behavior statistics to account for population changes in the prevalence of risk behaviors (e.g. reduction in the proportion of the population who are smokers). An exemplary HRA system may use algorithms based on national statistics that were developed for race-specific mortality predictions, including race-specific rates of risk behaviors (e.g. smoking, alcohol use).

[0077] Exemplary HRA systems may use a particular syntax to implement an actuarial extension procedure and estimate mortality risk for 1 , 2, 5, 10, and 20-year risks in the R statistical package. For example, the syntax can be used on demand to create updated mortality tables derived from CDC data.

[0078] An exemplary HRA system may provide report messages that are consistent with current published evidence and/or currently accepted facts, inferences, and conclusions. Sources include journal publications (e.g., NEJM, JAMA), U.S. Preventive Services Task Force yearly reports and American Heart Association. Exemplary HRA systems may use questionnaire items that are consistent with current published standards, current published evidence, and/or currently accepted facts, inferences, and conclusions (e.g., NEJM, JAMA), U.S. Preventive Services Task Force yearly reports and American Heart Association.

[0079] An exemplary HRA system may provide an administrative interface and a set of provider and administrative functions, including role-based access control. Exemplary HRA systems may give providers and administrators the ability to create an HRA report with one button click, based on any previously entered HRA data. Using the administrative interface, administrative users can review HRA responses and computed risks for any patient, for any of their previously computed HRA's. An exemplary HRA system can include a secure login system, through which users can establish accounts, login and complete other account tasks, such as resetting a password. [0080] Exemplary HRA systems can be supported by a website Home Page accessible via the Internet, for example providing an information video. An exemplary HRA system can employ an interface layout, color scheme and software theme that provides an appealing user experience that may be implemented in JavaScript and that may use query and twitter bootstraps.

[0081 ] Exemplary HRA systems may provide the ability to create a PDF of the report allowing users to print the report from the report page and/or to email a PDF of the report. An exemplary embodiment of an HRA system may provide printable versions of the report across various platforms (e.g. IOS, Android and web versions). Exemplary HRA systems may provide reports that are enriched with color images and color cues, for example a red internationally recognized icon - frown face - for high risks.

[0082] An exemplary HRA system may provide the ability for users to take a health risk assessment anonymously as a guest. Guest users may still be able to create a report and email the report. However, the data generated by the guest user is not retrievable; whereas, for a user with an account, the generated data is retrievable. Exemplary HRA systems may be accessible via an iPad tablet version that communicates with a server to retrieve/store login and HRA data. An exemplary HRA system may be accessible via an Android tablet version that communicates with the server to retrieve/store login and HRA data. Exemplary HRA systems may incorporate error-handling procedures that inform users of when the HRA software is offline and/or when the HRA software cannot compute a complete report.

[0083] An exemplary HRA system may be supported by a website page with information for clinicians. This information includes data on Affordable Care Act (ACA) provisions for HRA use in annual wellness visits, details on how to deploy the HRA system in a medical practice or medical institution, and in illustrative embodiments the website further includes links to U.S. Preventive Services Task Force and science and technical details of the HRA. Exemplary HRA systems may be supported by a set of disease and risk specific informational supporting materials (e.g. more information about flu, depression etc.) that users can access through links in their HRA system-generated report. The supporting material is suitably derived from reports created by the National Institute on Aging and other Federal agencies. [0084] An exemplary HRA system may include an older adult version with visualizations in full color based on the person's level of risk. The visualizations may be dynamically scaled based upon calculated values. Exemplary HRA systems may implement the American College of Cardiology (ACA), American Heart Association (AHA) atherosclerotic heart disease algorithm (for assessment of cardiovascular risk, to be used by providers to support decisions about cholesterol medication). An exemplary HRA system may include an Application Programming Interface (API) that allows other software programs and cross-platform devices to interface with the HRA system. For example, an API may be utilized in the iPad version and may be able to operate offline as a stand-alone version without accessing the Internet. However, HRA data is not stored on the server and not be available for retrieval after offline operation.

[0085] The various embodiments of the HRA systems described herein can also implement any suitable combination of the features described in this paragraph. For example, an exemplary HRA system may also include, without being limited to, a data analytics backend for administrators and providers. Exemplary HRA systems may also provide further modularization of the report. An exemplary HRA system may also include interactive risk visualizations that allow users to test out "what if scenarios for their risks and manipulate a small subset of factors. Exemplary HRA systems may also include a kids and teens risk assessment version that uses the adult and older adult HRA software platforms described herein. An exemplary HRA system may also implement API features providing cross-compatibility with EPIC, MyChart and other Electronic Health Record (EHR) systems.

[0086] The various embodiments of the HRA systems described herein may also implement questionnaires, statistical results, survey results, results of studies, and algorithms to assess, for example, morbidity, functional status, social support, and nutrition. These features are particularly useful to older adults and elderly adults. For example, in one embodiment, in addition to the adult HRA, the older adult HRA includes the 43 questions from the adult (midlife) HRA yielding the 10 year mortality estimates, plus additional questions for persons ages 55-90. These added questions focus on estimates of morbidity, functional status, social support, nutrition, and much more. [0087] The additional questions can be divided into eight modules: 1 ) existing conditions (e.g., 29 questions), 2) home safety (e.g., 13 questions), 3) functional status (e.g., 22 questions), 4) nutrition (e.g., 26 questions), 5) social support (e.g., 12 questions), 6) mental health (e.g., 20 questions), 7) vision, hearing, dental (e.g., 22 questions), and 8) demographics (e.g., 8 questions). In this embodiment, the individualized report may include the standard report for the midlife HRA questions plus a report sections on other outcomes related to older adults and elderly adults. For example, the additional report sections may include: 1 ) risk for institutionalization, 2) functional status, 3) physical disability, 4) falls, 5) vision impairment, 6) hearing impairment, 7) nutrition, 8) adverse drug reactions, 9) influenza/pneumonia, 10) mental health, 1 1 ) home environment hazards, 12) burns and fire related injury, 13) tetanus, 14) existing conditions, and 15) social support.

[0088] An exemplary HRA system may also provide options for users to print information and/or brochures produced by the National Institute on Aging. For example, the HRA system may provide the user with a list of the information and/or brochures so he or she can select those of interest. In addition, specific information and/or brochures may be linked contextually according to user responses.

[0089] The various embodiments of the HRA systems described herein may compute a risk age for the individual whose health is being assessed. The risk age is a composite numerical representation of risk. Risk age compares a participant's total risk from all causes of death to the total risk of those who are their age and sex. If an individual has lots of risk factors, their risk age will go up because the risk of dying will increase. The risk age would then show that the health of the person being assessed is similar to someone older who would be expected to die sooner than an average person the same age as the person being assessed. Risk age gives a user an idea of their risks compared with the population average in terms of an age rather than a probability. For example, a 55 year old person might end up with a risk age that indicates their health is currently equivalent to an average 60 year old person.

[0090] The various embodiments of the HRA systems described herein may also compute a target risk age for the individual whose health is being assessed. The target risk age indicates what a person's risk age would be if they made recommended lifestyle changes to reduce their risks. Thus, the target risk age is expected to be lower than the risk age unless no lifestyle changes are recommended. This scenario would be rare, but would result in the target risk age being the same as the risk age. Usually there are some lifestyle changes that could be improved upon which would make the target risk age lower than the risk age. The various embodiments of the HRA systems described herein may also compute risk years gained and display the number of risk years a person could gain from making each recommended change.

[0091 ] Average risks, displayed only in the comprehensive table of risks (hidden by default) are based on the age/sex specific population average mortality risks derived from a three year national average of the national death certificate data and census data. Certain components of the HRA system may be based on scientific studies and data that have demonstrated validity. For example, within the adult HRA version, there are multiple algorithms for each of 19 different causes of death (CODs), each of which carries its own specific validity.

[0092] As discussed above, an exemplary HRA system may include algorithms for 43 different causes of death. For 24 of the 43 causes of death covered in the midlife HRA version, the research base was not adequate to identify with reasonable validity causal risk factors that can be used in a prediction algorithm yielding a risk level for a specific individual. For these 24 causes of death, the HRA system offers population average mortality estimates. The validity of the outputs for these 24 causes of death lies in the fact that they are based on national mortality statistics averaged over three years and based on approximately 7.8 million death certificates. For practical purposes, this represents population data. A wide variety of data sources were employed for the other 19 models. Consider heart attack and stroke as two illustrative examples. For heart attack and stroke, the prediction models were derived from the Framingham Heart Study data (D'Agostino, Russell, Huse, Ellison, et al., 2000; D'Agostino, Belanger, Markson, Kelly-Hayes, & Wolf, 1995; Wolf, D'Agostino, Belanger, & Kannel, 1991 .). This longitudinal study, covering more than a half century, is probably one of the best examples of predictive validity in the medical literature. In the adult HRA, heart attack and stroke combined will account for some 31 percent of the mortality outcomes. [0093] The various embodiments of HRA systems disclosed herein can take a user's questionnaire answers, perform the needed preprocessing, compute the risks, and visualizes the results in a report, both on the screen and as a pdf-formatted report that can be saved, printed, or transmitted to a recipient address via e-mail or any other suitable communication technique. In certain HRA system embodiments, the report computation and visualization can be mixed together. In these embodiments, the report generation process can be performed on the client-side. This architecture provides very-large scalability and minimal load on the server.

[0094] In another HRA system embodiment, the report generation paradigm is a very thin client-side-computing paradigm in which selected report computation functions are moved to the server. For example, server-side report computation may include risks computations, the "risk age" computation, and the "risk years gained" computation. The server-side risk computations may be implemented in Java or JavaScript. One of the major goals of the Java risk computation is to make the code generalized so that code changes are not needed each time a risk formula or a data value changes. Thus, a generalized Java method runRiskCalculation() is created. The generalized method runRiskCalculation() will take the questionnaire responses as input, look up the risk data and formulas in a risk dataset, and evaluate the formulas for each type of risks.

[0095] When a risk value is being calculated, this method first finds the corresponding formula for the associated risk by the risk name. Then, it parses the risk formula into 4 types of tokens: numbers (1 , 2, 3...), operators (+, -, ^*, /, ...), precursors (i.e., questionnaire response values), and risk factors. The token that represents a precursor is replaced with a "number token" containing the value corresponding to the questionnaire response value. For tokens representing risk factors, the token is replaced by a value token after performing a lookup into the corresponding risk factor tables within the risk computing dataset. If any of the questions or risk values could not be resolved, a value of "not answered" is returned.

[0096] Since the user is not required to answer all questions, default values are used to fill in the unanswered questions so that all applicable risks can be computed. An answer value is only replaced by a default value if the question was left unanswered by the user, and no user responses will be overwritten be a default value. The default values can be specified as either a single value or a formula that is later evaluated. The method runRiskCalculation() may run more than once during the entire report calculation. For the first time, it computes the user's current risks based on the user's responses. For later runs, it replaces the user's responses with a table of optimal values, and computes the target risks. FIG. 6 provides an exemplary list of modifiable precursors and corresponding optimum values.

[0097] To give the user a quick and easy way to understand the magnitude of their overall health risks, both current and target risk ages may be computed. The risk age is a value based on the different computed causes of death risks. The current risk age uses risks computed based on the user's responses supplemented with default values for unanswered questions, and the target risk age uses risks computed with optimal values.

[0098] The formula used to compute the risk age is:

Ris ge

where In = Logarithm with base e; Total Risk = Summation of risks for all causes of death; and Total Population Average Risk = Summation of risks for all causes of death for a select population. For example, the population may be selected based on identifying and/or demographic information for the person being assessed, such as age, sex (i.e., gender), and/or ethnicity (e.g., race).

[0099] With continuing reference to FIG. 7, another HRA system embodiment may implement a risk year gain computation. For example, the final report can show the user a chart "What can you do to lower your risk age?" by listing the user's top four precursors that can be modified to lower his/her risk age. See FIG. 7 for an example of a risk year gain chart. To determine which precursors are shown in the graph, the risk years gained is computed by changing each modifiable precursor to its optimal value. This is done by modifying the questionnaire responses with the optimal value of each modifiable precursor one at a time and observing the change in the computed risk age. However, since many causes of death depend on multiple precursors, a few steps of computation need to be performed. An exemplary list of all causes and modifiable precursors that are associated to the subject cause are provided in FIG. 8. [00100] To compute the risk years gained for each modifiable precursor, each precursor is set to its optimal value one at a time, and the risks are recomputed. The precursor risk reduction for precursor N and risk M (PRR_NM) is defined to be the change in the risk value for risk M when setting the precursor N to its optimal value.

[00101 ] Once the risk reductions have been computed with each precursor, the actual impact of each change with respect to other precursors is determined. Since multiple precursors can affect a single cause of death and the risk years for the cause of death is not a simple summation of the risk reductions, weights must be computed for each precursor and the risk years gained for a cause of death distributed among these precursors based on the weights. The following formula is used to perform this calculation:

(PRR_{N M})

PRR_1M + PRR₂ M + ··· + PRRk,M )

where P_N is the weighted precursor attributable risk for precursor N, risk M; PRR_NiM is the precursor risk reduction for precursor N, risk M; TAR_M is the risk reduction for risk M when all precursors are set to optimal.

[00102] Once the weighted precursor attributable risk is computed for each precursor-risk pair, these risks need to be translated to risk years gained. First, the total weighted attributable risk (TP_N) is computed for each precursor by summing the P_NM values for all risks M with a fixed precursor N. Then, a similar weight-based calculation is performed using the following formula:

RYG_N = (—TPN— * TRYG

^N TP₁+TP₂ + - + TP_k)

where RYG_N is the risk years gained for precursor N that will be displayed in the graph;

TP_N is the total weighted attributable risk for precursor N; TRYG is the total risk years gained when all precursors are set to optimal.

[00103] With reference to FIG. 9, an exemplary HRA system may generate a report that includes a comprehensive table listing the user's mortality risks for each applicable cause of death from the list of death causes that are computed as part of the adult questionnaire. The exemplary table includes four columns. The leftmost column is a list containing each of 44 causes of death. The second and third columns are the current risks and target risks for the user. The fourth column is the average risk for an average person with identifying and/or demographic information that matches the user. For example, the identifying and/or demographic information may include gender (i.e., sex), age, and ethnicity (e.g., race) as the user, which are obtained from the mortality table in risk dataset. The risks displayed may be presented as the number of deaths in the next 5, 10, or 20 years (depending on the user's age) for 1 ,000 people of the same sex, age, race, and ethnicity as the user. In FIG. 9, the 5-year interval is depicted. The totals risk may also be displayed at the bottom of the table.

[00104] The server side report computations may also include various other elements in the report, such as the recommended preventative services, mental health part, and the risk factors for elder adult report. The computations of risk factors for elder adult highly resemble the risk computation discussed previously.

[00105] With reference to FIG. 10, an exemplary HRA system include a report section that provides recommendation messages for preventive services. The messages may be recommended based on the user's sex and age. An exemplary workflow for report computation in an exemplary embodiment of an HRA system is shown in FIG. 1 1 . After the report computations are done, a computation engine in the HRA system places the results in a JSON document and sends it to the client side for report visualization.

[00106] In another exemplary embodiment of the HRA system, a Model-View- Controller (MVC) architectural pattern is applied to the web architecture. This pattern splits the application into separate layers: a presentation layer and a model layer. The presentation layer is further split into view and controller layers.

[00107] The model is the domain-specific representation of the information on which the application operates. Domain logic adds 'meaning' to raw data (e.g., calculating if today is the user's birthday, or the totals, taxes, and shipping charges for a shopping cart). Most applications use a persistent storage mechanism such as a database to store data. MVC does not specifically mention the data access layer because it is understood to be underneath, or encapsulated by, the model.

[00108] The view renders the model into a form suitable for interactions, typically a user interface. Multiple views can exist for a single model, for different purposes. In a Web application the view is usually rendered in a 'web format' like HTML, XML or JSON. However there are some cases where the view can be expressed in a binary form, e.g. dynamically rendered chart diagrams.

[00109] The controller listens for HTTP requests, extracts relevant data from the 'event', such as query string parameters, request headers, and applies changes to the underlying model objects. The controller responds to events (typically, user actions) and processes them, and may also invoke changes on the model. Thus, the report computation component is suitable to be integrated with a controller.

[001 10] With reference to FIG. 12, an HTTP request and response path can be provided by an exemplary embodiment of an HRA system using an exemplary play framework integrated with the report computation component. When the controller receives the questionnaire responses from the client side, it will firstly retrieve the optimal values from the model, and then forward the questionnaire responses and optimal values to the report computation component. When the computations are done, it forwards back the results to the controller. The controller will forward the results to the model for storing them in the database, and then it will render the view, which is the report page with the computed results on it. Finally, the view will be sent to the client side, and a JavaScript code will generate the final report using the client's browser.

[001 1 1 ] After the computation and transmission, a personalized report can be created for the users based on user's questionnaire responses and the computed results. This report serves as an informative tool for the user to be able to quickly and easily understand their risks and what can be done to reduce their risks. In addition to the user, healthcare providers can use the reports as a quick summary of their patient's health. Various graphs and tables are used to provide easy to understand interpretations of the computed risks.

[001 12] An exemplary HRA system can implement report visualization at the client side. However, the report computation may be decoupled from the report visualization and may be executed on the server-side. The report data transmitted from the server can be parsed by the JavaScript code, and the computed results can be used to populate the corresponding parts in report visualizations. For maintenance purposes, the JavaScript code for report visualization can be decoupled to smaller modules and each module can be responsible for visualizing one section of the report.

[001 13] With reference to FIG. 13, a flow chart of an exemplary embodiment of a process for performing an HRA using a standalone computing device or networked computing devices arranged in a client-server architecture reflects the features described above in relation to user data collection, risk computations, further assessment computations, and report processing and visualization.

[001 14] With reference to FIG. 14, an exemplary embodiment of a process 1400 for performing a health risk assessment begins at 1402 where personalized input data is received at a computing device from one or more input data sources for a health risk assessment of an assessment candidate. The personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate. The computing device may include a server, a standalone computer, or any suitable computing device in any suitable combination. The standalone computer may include a desktop computer, a laptop computer, a tablet, a mobile phone, or any suitable standalone computer. The one or more input data sources may include user input devices (e.g., keyboards and/or pointing devices), local storage devices (e.g., memory and/or disk drive), remote computing devices (e.g., client computers or remote communication devices), remote storage devices (e.g., memory, disk drives, and/or servers), or any suitable input data source in any suitable combination. The demographic information may include age, gender, ethnicity, or any suitable demographic information in any suitable combination. The medical information may include pulse, blood pressure, cholesterol levels, or any suitable medical measurement or characteristic in any suitable combination. The behavioral and lifestyle information may include smoking habits, drinking habits, eating habits, sleeping habits, driving habits, living conditions, environmental conditions, working conditions, participation in sporting activities, participation in recreational activities, or any suitable behavioral and lifestyle information in any combination.

[001 15] At 1404, the personalized input data is pre-processed at the computing device to select at least one time interval for the health risk assessment. Selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate. The at least one time interval may include 1 year, 2 years, 5 years, 10 years, 20 years, or any suitable time interval in any suitable combination.

[001 16] Next, the personalized input data is processed at the computing device using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device (1406). The first mortality risk algorithm is associated with a first potential cause of death. The risk computation engine is arranged in a modular risk computation framework. The modular risk computation framework is configured for reuse by each of a plurality of mortality risk algorithms.

[001 17] In another embodiment of the process 1400, the personalized input data includes a modifiable data portion and a non-modifiable data portion. In this embodiment, the process 1400 also includes selecting and acquiring optimized input data at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate. The optimized input data corresponds to the modifiable data portion of the personalized input data. The optimized input data is selected based at least in part on the demographic information for the assessment candidate. The optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate. The target input data is processed at the computing device using the first mortality risk algorithm of the risk computation engine to determine a first target mortality risk probability for each at least one time interval. The first target mortality risk probability relates to the first current mortality risk probability.

[001 18] In a further embodiment, the process 1400 also includes selecting and acquiring a first average mortality risk probability at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate. The first average mortality risk probability is selected based at least in part on the demographic information for the assessment candidate and the first potential cause of death. The first average mortality risk probability relates to the first current mortality risk probability.

[001 19] In an even further embodiment, the process 1400 also includes processing the demographic information, the first current mortality risk probability, and the first average mortality risk probability at the computing device using a risk age algorithm to determine a risk-adjusted age for the assessment candidate. The risk- adjusted age relates to an actual age of the assessment candidate.

[00120] In a still further embodiment, the process 1400 also includes processing the demographic information, the first target mortality risk probability, and the first average mortality risk probability at the computing device using the risk age algorithm to determine a target age for the assessment candidate. The target age relates to the risk- adjusted age of the assessment candidate.

[00121 ] In a still even further embodiment of the process 1400, the modifiable portion of the personalized input data includes at least first and second modifiable input data that are attributable to the first current mortality risk probability determined by the first mortality risk algorithm. In this embodiment, the process 1400 also includes selecting first and second reduction input data at the computing device from the optimized input data selected for the health risk assessment of the assessment candidate. The first reduction input data corresponds to the first modifiable input data and the second reduction input data corresponds to the second modifiable input data. The personalized input data with the first reduction input data substituted for the first modifiable input data forms a first set of risk reduction input data and the personalized input data with the second reduction input data substituted for the second modifiable input data forms a second set of risk reduction input data. The first set of risk reduction input data is processed at the computing device using the first mortality risk algorithm of the risk computation engine to determine a first risk gain probability for each at least one time interval. A difference between the first current mortality risk probability and the first risk gain probability is determined to identify a first risk reduction amount. The second set of risk reduction input data is processed at the computing device using the first mortality risk algorithm of the risk computation engine to determine a second risk gain probability for each at least one time interval. A difference between the first current mortality risk probability and the second risk gain probability is determined to identify a second risk reduction amount. The first risk reduction amount, the second risk reduction amount, and the first target mortality risk probability are processed at the computing device using a risk reduction algorithm to identify first and second weighted risk gain amounts. The first weighted risk gain amount is associated with optimizing the first modifiable input data and the second weighted risk gain amount is associated with optimizing the second modifiable input data.

[00122] In a further embodiment, the process 1400 also includes determining a difference between the risk-adjusted age and the target age to identify a total risk years gained amount for the assessment candidate. In this embodiment, the first weighted risk gain amount, the second weighted risk gain amount, and the total risk years gained amount are processed at the computing device using a risk year gain algorithm to identify first and second risk year gain amounts. The first risk year gain amount is associated with optimizing the first modifiable input data and the second risk year gain amount is associated with optimizing the second modifiable input data.

[00123] In yet another embodiment of the process 1400, the processing of the personalized input data includes using each of multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple current mortality risk probabilities for each at least one time interval. The multiple mortality risk algorithms including the first mortality risk algorithm. Each mortality risk algorithm and corresponding current mortality risk probability being associated with a different potential cause of death. Each mortality risk algorithm re-uses the modular risk computation framework for the risk computation engine. In this embodiment, the process 1400 also includes determining a composite current mortality risk probability at the computing device for the assessment candidate from the multiple current mortality risk probabilities resulting from processing the personalized input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

[00124] In a further embodiment of the process 1400, the personalized input data includes a modifiable data portion and a non-modifiable data portion. In this embodiment, the process 1400 also includes selecting and acquiring optimized input data at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate. The optimized input data corresponds to the modifiable data portion of the personalized input data. The optimized input data is selected based at least in part on the demographic information for the assessment candidate. The optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate. The target input data is processed at the computing device using each of the multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple target mortality risk probabilities for each at least one time interval. The multiple target mortality risk probabilities correspond to the multiple current mortality risk probabilities. A composite target mortality risk probability is determined at the computing device for the assessment candidate from the multiple target mortality risk probabilities resulting from processing the target input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

[00125] In an even further embodiment, the process also includes selecting and acquiring multiple average mortality risk probabilities at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate. The multiple average mortality risk probabilities are selected based at least in part on the demographic information for the assessment candidate and the corresponding multiple potential causes of death. A composite average mortality risk probability is determined at the computing device for the assessment candidate from the multiple average mortality risk probabilities associated with the multiple potential causes of death. The demographic information, the composite current mortality risk probability, and the composite average mortality risk probability are processed at the computing device using a risk age algorithm to determine a risk-adjusted age for the assessment candidate, wherein the risk-adjusted age relates to an actual age of the assessment candidate. The demographic information, the composite target mortality risk probability, and the composite average mortality risk probability are processed at the computing device using the risk age algorithm to determine a target age for the assessment candidate. The target age relates to the risk-adjusted age of the assessment candidate.

[00126] With reference to FIG. 15, an exemplary embodiment of a computer device 1500 for performing a health risk assessment includes one or more input data source interfaces 1502 and at least one processor 1504. The one or more input data source interfaces 1502 configured to receive personalized input data from a corresponding one or more input data sources 1506 for a health risk assessment of an assessment candidate. The personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate. The computing device 1500 may include a server, a standalone computer, or any suitable computing device in any suitable combination. The standalone computer may include a desktop computer, a laptop computer, a tablet, a mobile phone, or any suitable standalone computer. The one or more input data sources 1506 may include user input devices 1506-1 (e.g., keyboards and/or pointing devices), local storage devices 1506-2 (e.g., memory and/or disk drive), remote computing devices 1506-3 (e.g., client computers or remote communication devices), remote storage devices 1506-4 (e.g., memory, disk drives, and/or servers), or any suitable input data source 1506 in any suitable combination. The demographic information may include age, gender, ethnicity, or any suitable demographic information in any suitable combination. The medical information may include pulse, blood pressure, cholesterol levels, or any suitable medical measurement or characteristic in any suitable combination. The behavioral and lifestyle information may include smoking habits, drinking habits, eating habits, sleeping habits, driving habits, living conditions, environmental conditions, working conditions, participation in sporting activities, participation in recreational activities, or any suitable behavioral and lifestyle information in any combination.

[00127] The least one processor 1504 configured to pre-process the personalized input data to select at least one time interval for the health risk assessment. The selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate. The at least one time interval may include 1 year, 2 years, 5 years, 10 years, 20 years, or any suitable time interval in any suitable combination.

[00128] The at least one processor 1504 is configured to process the personalized input data using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval. The first mortality risk algorithm is associated with a first potential cause of death. The risk computation engine is arranged in a modular risk computation framework. The modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms. [00129] In another embodiment of the computing device 1500, the personalized input data includes a modifiable data portion and a non-modifiable data portion. In this embodiment, the at least one processor 1504 is configured to select and acquire optimized input data from the one or more input data sources 1506 for the health risk assessment of the assessment candidate. The optimized input data corresponds to the modifiable data portion of the personalized input data. The optimized input data is selected based at least in part on the demographic information for the assessment candidate. The optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate. The at least one processor 1504 is configured to process the target input data using the first mortality risk algorithm of the risk computation engine to determine a first target mortality risk probability for each at least one time interval. The first target mortality risk probability relates to the first current mortality risk probability.

[00130] In a further embodiment of the computing device 1500, the at least one processor 1504 is configured to select and acquire a first average mortality risk probability from the one or more input data sources 1506 for the health risk assessment of the assessment candidate. The first average mortality risk probability is selected based at least in part on the demographic information for the assessment candidate and the first potential cause of death. The first average mortality risk probability relates to the first current mortality risk probability.

[00131 ] In an even further embodiment of the computing device 1500, the at least one processor 1504 is configured to process the demographic information, the first current mortality risk probability, and the first average mortality risk probability using a risk age algorithm to determine a risk-adjusted age for the assessment candidate. The risk-adjusted age relates to an actual age of the assessment candidate.

[00132] In a still further embodiment of the computing device 1500, the at least one processor 1504 is configured to process the demographic information, the first target mortality risk probability, and the first average mortality risk probability using the risk age algorithm to determine a target age for the assessment candidate. The target age relates to the risk-adjusted age of the assessment candidate. [00133] In a still even further embodiment of the computing device 1500, the modifiable portion of the personalized input data includes at least first and second modifiable input data that are attributable to the first current mortality risk probability determined by the first mortality risk algorithm. In this embodiment, the at least one processor 1504 is configured to select first and second reduction input data from the optimized input data selected for the health risk assessment of the assessment candidate. The first reduction input data corresponds to the first modifiable input data and the second reduction input data corresponds to the second modifiable input data. The personalized input data with the first reduction input data substituted for the first modifiable input data forms a first set of risk reduction input data and the personalized input data with the second reduction input data substituted for the second modifiable input data forms a second set of risk reduction input data. The at least one processor 1504 is configured to process the first set of risk reduction input data using the first mortality risk algorithm of the risk computation engine to determine a first risk gain probability for each at least one time interval. The at least one processor 1504 is configured to determine a difference between the first current mortality risk probability and the first risk gain probability to identify a first risk reduction amount. The at least one processor 1504 is configured to process the second set of risk reduction input data using the first mortality risk algorithm of the risk computation engine to determine a second risk gain probability for each at least one time interval. The at least one processor 1504 is configured to determine a difference between the first current mortality risk probability and the second risk gain probability to identify a second risk reduction amount. The at least one processor 1504 is configured to process the first risk reduction amount, the second risk reduction amount, and the first target mortality risk probability using a risk reduction algorithm to identify first and second weighted risk gain amounts. The first weighted risk gain amount is associated with optimizing the first modifiable input data and the second weighted risk gain amount is associated with optimizing the second modifiable input data. The at least one processor 1504 is configured to determine a difference between the risk-adjusted age and the target age to identify a total risk years gained amount for the assessment candidate. The at least one processor 1504 is configured to process the first weighted risk gain amount, the second weighted risk gain amount, and the total risk years gained amount using a risk year gain algorithm to identify first and second risk year gain amounts. The first risk year gain amount is associated with optimizing the first modifiable input data and the second risk year gain amount is associated with optimizing the second modifiable input data.

[00134] In yet another embodiment of the computing device 1500, the processing of the personalized input data includes using each of multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple current mortality risk probabilities for each at least one time interval. The multiple mortality risk algorithms including the first mortality risk algorithm. Each mortality risk algorithm and corresponding current mortality risk probability being associated with a different potential cause of death. Each mortality risk algorithm re-uses the modular risk computation framework for the risk computation engine. In this embodiment, the at least one processor 1504 is configured to determine a composite current mortality risk probability for the assessment candidate from the multiple current mortality risk probabilities resulting from processing the personalized input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

[00135] In a further embodiment of the computing device 1500, the personalized input data includes a modifiable data portion and a non-modifiable data portion. In this embodiment, the at least one processor 1504 is configured to select and acquire optimized input data from the one or more input data sources 1506 for the health risk assessment of the assessment candidate. The optimized input data corresponds to the modifiable data portion of the personalized input data. The optimized input data is selected based at least in part on the demographic information for the assessment candidate. The optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate. The at least one processor 1504 is configured to process the target input data using each of the multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple target mortality risk probabilities for each at least one time interval. The multiple target mortality risk probabilities correspond to the multiple current mortality risk probabilities. The at least one processor 1504 is configured to determine a composite target mortality risk probability for the assessment candidate from the multiple target mortality risk probabilities resulting from processing the target input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

[00136] In an even further embodiment of the computing device 1500, the at least one processor 1504 is configured to select and acquire multiple average mortality risk probabilities from the one or more input data sources 1506 for the health risk assessment of the assessment candidate. The multiple average mortality risk probabilities are selected based at least in part on the demographic information for the assessment candidate and the corresponding multiple potential causes of death. The at least one processor 1504 is configured to determine a composite average mortality risk probability for the assessment candidate from the multiple average mortality risk probabilities associated with the multiple potential causes of death. The at least one processor 1504 is configured to process the demographic information, the composite current mortality risk probability, and the composite average mortality risk probability using a risk age algorithm to determine a risk-adjusted age for the assessment candidate. The risk-adjusted age relates to an actual age of the assessment candidate. The at least one processor 1504 is configured to process the demographic information, the composite target mortality risk probability, and the composite average mortality risk probability using the risk age algorithm to determine a target age for the assessment candidate. The target age relates to the risk-adjusted age of the assessment candidate.

[00137] With reference to FIGs. 14 and 15, an exemplary embodiment of a non- transitory computer-readable medium storing program instructions that, when executed by a processor, cause a computing device 1500 to perform a process 1400 for performing a health risk assessment. In one exemplary embodiment, the process 1400 begins at 1402 where personalized input data is received at a computing device 1500 from one or more input data sources 1506 for a health risk assessment of an assessment candidate. The personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate. At 1404, the personalized input data is pre-processed at the computing device 1500 to select at least one time interval for the health risk assessment. Selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate. Next, the personalized input data is processed at the computing device 1500 using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device 1500. The first mortality risk algorithm is associated with a first potential cause of death. The risk computation engine is arranged in a modular risk computation framework. The modular risk computation framework is configured for reuse by each of a plurality of mortality risk algorithms.

[00138] In various additional embodiments, the instructions stored in the non- transitory computer-readable memory, when executed by the processor, may cause the computing device 1500 to perform various combinations of functions associated with the processes for performing a health risk assessment. In other words, the various features described above may be implemented in any suitable combination by the program instructions stored in the non-transitory computer-readable medium. Any suitable components of the computing device 1500 described above may include the corresponding processor 1504 and non-transitory computer-readable medium associated with the corresponding program instructions. Alternatively, the corresponding processor 1504 and non-transitory computer-readable medium associated with the corresponding program instructions may be individual or combined components that are in operative communication with any suitable combination of components of the computing device 1500 described above.

[00139] The above description merely provides a disclosure of particular embodiments of the invention and is not intended for the purposes of limiting the same thereto. As such, the invention is not limited to only the above-described embodiments. Rather, it is recognized that one skilled in the art could conceive alternative embodiments that fall within the scope of the invention.

Claims

We claim:

1 . A method for performing a health risk assessment, comprising:

receiving personalized input data at a computing device from one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate;

pre-processing the personalized input data at the computing device to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; and

processing the personalized input data at the computing device using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.

2. The method of claim 1 wherein the personalized input data includes a modifiable data portion and a non-modifiable data portion, the method further comprising:

selecting and acquiring optimized input data at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the optimized input data corresponds to the modifiable data portion of the personalized input data, wherein the optimized input data is selected based at least in part on the demographic information for the assessment candidate, wherein the optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate; and processing the target input data at the computing device using the first mortality risk algorithm of the risk computation engine to determine a first target mortality risk probability for each at least one time interval, wherein the first target mortality risk probability relates to the first current mortality risk probability.

3. The method of claim 2, further comprising:

selecting and acquiring a first average mortality risk probability at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the first average mortality risk probability is selected based at least in part on the demographic information for the assessment candidate and the first potential cause of death, wherein the first average mortality risk probability relates to the first current mortality risk probability.

4. The method of claim 3, further comprising:

processing the demographic information, the first current mortality risk probability, and the first average mortality risk probability at the computing device using a risk age algorithm to determine a risk-adjusted age for the assessment candidate, wherein the risk-adjusted age relates to an actual age of the assessment candidate.

5. The method of claim 4, further comprising:

processing the demographic information, the first target mortality risk probability, and the first average mortality risk probability at the computing device using the risk age algorithm to determine a target age for the assessment candidate, wherein the target age relates to the risk-adjusted age of the assessment candidate.

6. The method of claim 5 wherein the modifiable portion of the personalized input data includes at least first and second modifiable input data that are attributable to the first current mortality risk probability determined by the first mortality risk algorithm, the method further comprising:

selecting first and second reduction input data at the computing device from the optimized input data selected for the health risk assessment of the assessment candidate, wherein the first reduction input data corresponds to the first modifiable input data and the second reduction input data corresponds to the second modifiable input data, wherein the personalized input data with the first reduction input data substituted for the first modifiable input data forms a first set of risk reduction input data and the personalized input data with the second reduction input data substituted for the second modifiable input data forms a second set of risk reduction input data;

processing the first set of risk reduction input data at the computing device using the first mortality risk algorithm of the risk computation engine to determine a first risk gain probability for each at least one time interval;

determining a difference between the first current mortality risk probability and the first risk gain probability to identify a first risk reduction amount;

processing the second set of risk reduction input data at the computing device using the first mortality risk algorithm of the risk computation engine to determine a second risk gain probability for each at least one time interval;

determining a difference between the first current mortality risk probability and the second risk gain probability to identify a second risk reduction amount; and

processing the first risk reduction amount, the second risk reduction amount, and the first target mortality risk probability at the computing device using a risk reduction algorithm to identify first and second weighted risk gain amounts, wherein the first weighted risk gain amount is associated with optimizing the first modifiable input data and the second weighted risk gain amount is associated with optimizing the second modifiable input data.

7. The method of claim 6, further comprising:

determining a difference between the risk-adjusted age and the target age to identify a total risk years gained amount for the assessment candidate; and

processing the first weighted risk gain amount, the second weighted risk gain amount, and the total risk years gained amount at the computing device using a risk year gain algorithm to identify first and second risk year gain amounts, wherein the first risk year gain amount is associated with optimizing the first modifiable input data and the second risk year gain amount is associated with optimizing the second modifiable input data.

8. The method of claim 1 wherein the processing of the personalized input data includes using each of multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple current mortality risk probabilities for each at least one time interval, the multiple mortality risk algorithms including the first mortality risk algorithm, each mortality risk algorithm and corresponding current mortality risk probability being associated with a different potential cause of death, wherein each mortality risk algorithm re-uses the modular risk computation framework for the risk computation engine, the method further comprising:

determining a composite current mortality risk probability at the computing device for the assessment candidate from the multiple current mortality risk probabilities resulting from processing the personalized input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

9. The method of claim 8 wherein the personalized input data includes a modifiable data portion and a non-modifiable data portion, the method further comprising:

selecting and acquiring optimized input data at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the optimized input data corresponds to the modifiable data portion of the personalized input data, wherein the optimized input data is selected based at least in part on the demographic information for the assessment candidate, wherein the optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate;

processing the target input data at the computing device using each of the multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple target mortality risk probabilities for each at least one time interval, wherein the multiple target mortality risk probabilities correspond to the multiple current mortality risk probabilities; and

determining a composite target mortality risk probability at the computing device for the assessment candidate from the multiple target mortality risk probabilities resulting from processing the target input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

10. The method of claim 9, further comprising:

selecting and acquiring multiple average mortality risk probabilities at the computing device from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the multiple average mortality risk probabilities are selected based at least in part on the demographic information for the assessment candidate and the corresponding multiple potential causes of death;

determining a composite average mortality risk probability at the computing device for the assessment candidate from the multiple average mortality risk probabilities associated with the multiple potential causes of death;

processing the demographic information, the composite current mortality risk probability, and the composite average mortality risk probability at the computing device using a risk age algorithm to determine a risk-adjusted age for the assessment candidate, wherein the risk-adjusted age relates to an actual age of the assessment candidate; and

processing the demographic information, the composite target mortality risk probability, and the composite average mortality risk probability at the computing device using the risk age algorithm to determine a target age for the assessment candidate, wherein the target age relates to the risk-adjusted age of the assessment candidate.

1 1 . An apparatus for performing a health risk assessment, comprising:

one or more input data source interfaces configured to receive personalized input data from a corresponding one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate; and

at least one processor configured to pre-process the personalized input data to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; wherein the at least one processor is configured to process the personalized input data using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.

12. The apparatus of claim 1 1 wherein the personalized input data includes a modifiable data portion and a non-modifiable data portion;

wherein the at least one processor is configured to select and acquire optimized input data from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the optimized input data corresponds to the modifiable data portion of the personalized input data, wherein the optimized input data is selected based at least in part on the demographic information for the assessment candidate, wherein the optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate;

wherein the at least one processor is configured to process the target input data using the first mortality risk algorithm of the risk computation engine to determine a first target mortality risk probability for each at least one time interval, wherein the first target mortality risk probability relates to the first current mortality risk probability.

13. The apparatus of claim 12 wherein the at least one processor is configured to select and acquire a first average mortality risk probability from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the first average mortality risk probability is selected based at least in part on the demographic information for the assessment candidate and the first potential cause of death, wherein the first average mortality risk probability relates to the first current mortality risk probability.

14. The apparatus of claim 13 wherein the at least one processor is configured to process the demographic information, the first current mortality risk probability, and the first average mortality risk probability using a risk age algorithm to determine a risk- adjusted age for the assessment candidate, wherein the risk-adjusted age relates to an actual age of the assessment candidate.

15. The apparatus of claim 14 wherein the at least one processor is configured to process the demographic information, the first target mortality risk probability, and the first average mortality risk probability using the risk age algorithm to determine a target age for the assessment candidate, wherein the target age relates to the risk-adjusted age of the assessment candidate.

16. The apparatus of claim 15 wherein the modifiable portion of the personalized input data includes at least first and second modifiable input data that are attributable to the first current mortality risk probability determined by the first mortality risk algorithm; wherein the at least one processor is configured to select first and second reduction input data from the optimized input data selected for the health risk assessment of the assessment candidate, wherein the first reduction input data corresponds to the first modifiable input data and the second reduction input data corresponds to the second modifiable input data, wherein the personalized input data with the first reduction input data substituted for the first modifiable input data forms a first set of risk reduction input data and the personalized input data with the second reduction input data substituted for the second modifiable input data forms a second set of risk reduction input data;

wherein the at least one processor is configured to process the first set of risk reduction input data using the first mortality risk algorithm of the risk computation engine to determine a first risk gain probability for each at least one time interval;

wherein the at least one processor is configured to determine a difference between the first current mortality risk probability and the first risk gain probability to identify a first risk reduction amount; wherein the at least one processor is configured to process the second set of risk reduction input data using the first mortality risk algorithm of the risk computation engine to determine a second risk gain probability for each at least one time interval;

wherein the at least one processor is configured to determine a difference between the first current mortality risk probability and the second risk gain probability to identify a second risk reduction amount;

wherein the at least one processor is configured to process the first risk reduction amount, the second risk reduction amount, and the first target mortality risk probability using a risk reduction algorithm to identify first and second weighted risk gain amounts, wherein the first weighted risk gain amount is associated with optimizing the first modifiable input data and the second weighted risk gain amount is associated with optimizing the second modifiable input data;

wherein the at least one processor is configured to determine a difference between the risk-adjusted age and the target age to identify a total risk years gained amount for the assessment candidate;

wherein the at least one processor is configured to process the first weighted risk gain amount, the second weighted risk gain amount, and the total risk years gained amount using a risk year gain algorithm to identify first and second risk year gain amounts, wherein the first risk year gain amount is associated with optimizing the first modifiable input data and the second risk year gain amount is associated with optimizing the second modifiable input data.

17. The apparatus of claim 1 1 wherein the processing of the personalized input data includes using each of multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple current mortality risk probabilities for each at least one time interval, the multiple mortality risk algorithms including the first mortality risk algorithm, each mortality risk algorithm and corresponding current mortality risk probability being associated with a different potential cause of death, wherein each mortality risk algorithm re-uses the modular risk computation framework for the risk computation engine; wherein the at least one processor is configured to determine a composite current mortality risk probability for the assessment candidate from the multiple current mortality risk probabilities resulting from processing the personalized input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

18. The apparatus of claim 17 wherein the personalized input data includes a modifiable data portion and a non-modifiable data portion;

wherein the at least one processor is configured to select and acquire optimized input data from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the optimized input data corresponds to the modifiable data portion of the personalized input data, wherein the optimized input data is selected based at least in part on the demographic information for the assessment candidate, wherein the optimized input data and the non-modifiable data portion of the personalized input data form target input data for the health risk assessment of the assessment candidate;

wherein the at least one processor is configured to process the target input data using each of the multiple mortality risk algorithms of the risk computation engine to determine a corresponding multiple target mortality risk probabilities for each at least one time interval, wherein the multiple target mortality risk probabilities correspond to the multiple current mortality risk probabilities;

wherein the at least one processor is configured to determine a composite target mortality risk probability for the assessment candidate from the multiple target mortality risk probabilities resulting from processing the target input data using the multiple mortality risk algorithms associated with the multiple potential causes of death.

19. The apparatus of claim 18 wherein the at least one processor is configured to select and acquire multiple average mortality risk probabilities from the one or more input data sources for the health risk assessment of the assessment candidate, wherein the multiple average mortality risk probabilities are selected based at least in part on the demographic information for the assessment candidate and the corresponding multiple potential causes of death;

wherein the at least one processor is configured to determine a composite average mortality risk probability for the assessment candidate from the multiple average mortality risk probabilities associated with the multiple potential causes of death;

wherein the at least one processor is configured to process the demographic information, the composite current mortality risk probability, and the composite average mortality risk probability using a risk age algorithm to determine a risk-adjusted age for the assessment candidate, wherein the risk-adjusted age relates to an actual age of the assessment candidate;

wherein the at least one processor is configured to process the demographic information, the composite target mortality risk probability, and the composite average mortality risk probability using the risk age algorithm to determine a target age for the assessment candidate, wherein the target age relates to the risk-adjusted age of the assessment candidate.

20. A non-transitory computer-readable medium storing program instructions that, when executed by a processor, cause a computing device to perform a method for performing a health risk assessment, the method comprising:

receiving personalized input data at a computing device from one or more input data sources for a health risk assessment of an assessment candidate, wherein the personalized input data includes demographic, medical, behavioral, and lifestyle information for the assessment candidate;

pre-processing the personalized input data at the computing device to select at least one time interval for the health risk assessment, wherein selection of the at least one time interval is based at least in part on the demographic information for the assessment candidate; and

processing the personalized input data at the computing device using a first mortality risk algorithm of a risk computation engine to determine a first current mortality risk probability for each at least one time interval selected by the computing device, wherein the first mortality risk algorithm is associated with a first potential cause of death, wherein the risk computation engine is arranged in a modular risk computation framework, wherein the modular risk computation framework is configured for re-use by each of a plurality of mortality risk algorithms.