WO2014169268A1 - System and method for identifying patients most likely to subscribe to a prevention program for type-2 diabetes - Google Patents

Abstract

Certain embodiments are directed to a method and/or system for identifying prediabetes patients most likely to subscribe to a cash-based type 2 diabetes prevention program is described. The present invention provides a system and method for identifying, classifying, and/or selecting patients who are most likely to comply with a pre-diabetic treatment or management program and/or subscribe to a program to prevent or delay the onset of type-2 diabetes. The systems and methods are design to provide preventive care early and reduce long-term cost of healthcare. In certain aspects the systems and methods identify and treat those subjects that will comply with a program to a sufficient degree so that resources are not less effectively used on non-compliant individuals.

Description

SYSTEM AND METHOD FOR IDENTIFYING PATIENTS MOST LIKELY TO SUBSCRIBE TO A PREVENTION PROGRAM FOR TYPE-2 DIABETES

FIELD OF THE INVENTION

[0001] The present invention generally relates to identification of clients who are most likely to subscribe to and/or comply with a managed pre-diabetic treatment program or a cash-based system oriented towards the prevention or delay of onset of type-2 diabetes.

BACKGROUND OF THE INVENTION

[0002] Type-2 diabetes is a significant health problem affecting millions of Americans. The development of type-2 diabetes is usually preceded by a condition known as prediabetes. Pre-diabetes includes metabolic abnormalities, such abnormalities can be detected by measuring biomarker levels, which place an individual at higher risk for the development of type-2 diabetes even though the biomarker levels may be below a currently established cutoff for diagnosis of type-2 diabetes. Current estimates indicate that about 79 million people in the U.S. have pre-diabetes.

[0003] Additionally, the onset of pre-diabetes may damage an individual's body before symptoms of type-2 diabetes appear. This damage may include destruction of blood vessels and nerves leading to vision loss, hearing loss, kidney damage, decreased nerve sensation, and poor circulation. Further, type-2 diabetes increases the risk and development of other diseases including dementia, heart attack, stroke and cancer, especially breast, colon, prostate and pancreatic cancer.

[0004] However, recent research suggests that the development of type-2 diabetes may be halted or even prevented with early intervention and treatment. Therefore, early diagnosis and treatment of this disorder is critical.

[0005] Determining a potential risk for developing type-2 diabetes can be a time- consuming and complex process, relying on clinical expertise, genetic factors, family history, diagnostic testing and a myriad of other factors. Therefore, it is an object of the present invention to develop a system and method to identify individuals at high risk for developing type-2 diabetes, especially individuals with pre-diabetes, and who are most likely to comply with and/or subscribe to a program, e.g., a cash-based program, for ameliorating, preventing, or delaying the onset of type-2 diabetes.

SUMMARY

[0006] The present invention provides a system and method for identifying, classifying, and/or selecting patients who are most likely to comply with a pre-diabetic treatment or management program and/or subscribe to a program to prevent or delay the onset of type-2 diabetes. The systems and methods are design to provide preventive care early and reduce long-term cost of healthcare. In certain aspects the systems and methods identify and treat those subjects that will comply with a program to a sufficient degree so that resources are not less effectively used on non-compliant individuals. In certain aspects the subjects are self- selected, that is the subjects are identified by their efforts to obtain information and their willingness to identify and address their health concerns.

[0007] The systems and methods can use information received from a web-based questionnaire (also referred to as an "intake form"), direct or indirect observations of the subject's behavior, laboratory test results, demographic information, and other factors described throughout this application, to identify, classify, or select individuals having a likelihood or a significant likelihood for developing type-2 diabetes (identifying an at risk or pre-diabetic subject) as well as those individuals identified as at risk or pre-diabetic that have an increased likelihood of complying with a pre-diabetic program or subscribing to a treatment or prevention program (identifying a compliant subject). In other aspects, a subject can be identified as having diabetes. These diabetic subjects are excluded and not considered for selection since they have the disease the program is designed to prevent. These subjects may be referred to a physician specialist or clinic for treatment.

[0008] The methods for identifying a subject at risk of developing diabetes or a pre- diabetic patient can include gathering information on the subject in combination with analyzing a number of metabolic/physiologic measurements. Another aspect of subject selection is the self-selected aspect of the process. The system and method provides for a publically accessible presence on the web that can be identified by any subject that is searching for information or solutions. The system supports a web page that can be identified or found during the subject's search. Once the system makes contact with the subject the system provides information regarding pre-diabetes in general and specifically information describing a pre-diabetes program in which the subject may wish to enroll. The self-selected client will spend time on the web page educating themselves about diabetes and the treatment options available for delaying or preventing the onset of diabetes. The system provides for display of a subject interface so that the subject can access and complete a questionnaire supplied by the system. The results of the questionnaire are then received and analyzed by the system, which uses a predetermined score chart (a listing of possible responses that are associated with a point value for a scoring system) to generate a first risk score to determine if the subject is at risk of developing diabetes. As used herein, a subject at risk of developing diabetes is a subject that has a higher probability than the population generally at developing diabetes during the subject's lifetime.

[0009] The scoring chart or matrix can be developed and the risk of developing diabetes can be determined or calculated by using family history, the subject's current physical condition (height, weight, body mass index, etc.), and/or the subject's current habits (diet, exercise or lack thereof, etc.), each of which can be addressed in the intake form or questionnaire and an associated score chart or matrix. In certain aspects the system sends the resulting score to the subject, e.g., directly by email or indirectly boy alerting a staff member to contact the subject and convey the resulting risk score. Once the subject is determined to be at risk the system queues a personal contact with the subject, e.g., a phone call or email. The self-selected subject provides additional behavioral information by investing the time to interact with staff tasked with contacting at risk subjects, scheduling and attending blood draws and physician consultations, and completing an initial identification and consultation procedure.

[0010] A pre-diabetic subject is a subject that, based on metabolic measurements has not yet developed diabetes but is in the early stages of developing diabetes in that one or more metabolic criteria are not yet at the threshold established for diagnosing diabetes but are elevated as compared to a non-diabetic population. In certain aspects the process or method of identifying a subject at risk of developing diabetes or a pre-diabetic patient/subject includes gathering pertinent health and family information. Subjects identified as being at risk can be further evaluated to identify those at risk subjects that are pre-diabetic subjects. Identifying pre-diabetic subjects can include measuring a number of metabolic/physiologic parameters using biologic samples from the subject.

[0011] In one embodiment, a series of assays or measurements can be performed on body fluids, such as blood. The assays can be performed in a laboratory setting to determine if an individual has metabolic abnormalities falling within the classification of a pre-diabetes syndrome. In certain aspects those subjects identified as being at risk are contacted and scheduled for a blood draw or other sample procurement procedure. In a further embodiment, those subject at risk and identified as having a likelihood of compliance can be contacted and scheduled for sample procurement. In certain aspects the biomarker panel includes one or more of hsCRP, insulin, glucose, HOMA-IR, HbAlc, and/or adiponectin. In a further aspect the biomarker panel includes HbAlc and one or more of HOMA-IR, hsCRP, insulin, glucose, and/or adiponectin. In still a further aspect the biomarker panel includes HOMA-IR and one or more of HbAlc, hsCRP, insulin, glucose, and/or adiponectin. In still a further aspect the biomarker panel includes hsCRP and one or more of HbAlc, HOMA-IR, insulin, glucose, and/or adiponectin. In still a further aspect the biomarker panel includes insulin and one or more of HbAlc, HOMA-IR, hsCRP, glucose, and/or adiponectin. In still a further aspect the biomarker panel includes glucose and one or more of HbAlc, HOMA-IR, hsCRP, insulin, and/or adiponectin. In still a further aspect the biomarker panel includes adiponectin and one or more of HbAlc, HOMA-IR, hsCRP, insulin, and/or glucose. In certain embodiments the biomarker panel is hsCRP, insulin, glucose, HOMA-IR, HbAlc, and adiponectin.

[0012] Subjects can be evaluated using a scoring and management system. The system comprises a scoring engine that can generate one or more risk scores, a probability or compliance score, and/or a buying code score to guide the identification of prospective patients who have a significant likelihood of developing type-2 diabetes.

[0013] In another embodiment, a consultation with a pre-diabetic subject can be scheduled by the system and performed by a healthcare provider associated with the prediabetes treatment program to identify additional factors that may lead to progression of the disease. In certain aspects the system is programmed to alert a staff member that a consultation is recommended. The system may also provide scheduling and location information for a convenient participating physician or healthcare provider. In a further aspect the pre-diabetic subject is enrolled in and provided with a program. In certain aspects the system is configured to monitor the pre-diabetic subject's compliance with the program and may be configured to provide reminders and inspirational messages to help the subject maintain compliance.

[0014] In still another embodiment, the invention may utilize demographic information to identify prospective clients (e.g., at risk or pre-diabetic subjects) who are most likely to subscribe to a program (e.g., a cash-based system) for prevention or treatment. In further embodiments, sales information may help identify marketing segments of the population who are most likely to subscribe. In still further embodiments, a client grading scale may be implemented to rank individuals into tiers to assist with prioritizing call backs, consultations, and laboratory testing. In certain aspects a three-tier system is implemented (e.g., high, medium, and low). In certain aspects the system provides a method of identifying clients with a high likelihood of developing disease and who are most likely to subscribe to a prevention program (high tier individuals).

[0015] It is expressly understood that such a system and method is not tied to the use of web-based questionnaires, and that other methods of distributing questionnaires or collecting information would also fall within the scope of the present invention as long as the information is ultimately available to the system and scoring engine.

[0016] In another embodiment, the present invention may utilize a marketing and sales based software, which may be part of a fully or partially automated process, to identify high risk clients most likely to subscribe. This software package may include, but is not limited to, commercially available or customized packages. For example, a software package such as Infusionsoft®, Optify®, or Eloqua® may be used. Such software packages are typically geared towards identification of particular categories of consumers through interfacing with web-based research, marketing, and analysis tools.

[0017] The present invention may be embodied as a system for identifying individuals most likely to subscribe to type 2 diabetes prevention program (e.g., a cash-based program) comprising: a computer processor; a first memory or storage containing responses to a web- based questionnaire; and a second memory or storage containing one or more scores generated by a scoring engine that upon execution by the computer processor analyzes the responses to the web-based questionnaire according to a series of scoring criteria, wherein the one or more scores reflect a likelihood that the patient will subscribe to a type-2 diabetes prevention program (e.g., a cash-based program).

[0018] Certain aspects may further comprise a web-based questionnaire that includes a series of questions designed to assess an individual's risk for developing type-2 diabetes and the likelihood that said individual would subscribe to a program to prevent or delay the onset of type-2 diabetes. The scoring criteria may be based upon a series of rules and/or a predetermined scoring matrix or chart. The scores generated by the scoring engine are based upon selected answers from the web-based questionnaire and include: (i) a risk score (in certain aspects a first at risk score and a second pre-diabetic score); (ii) a probability or compliance score; and (iii) a buying code score, in which the risk score is reflective of an individual risk for developing type-2 diabetes, the probability or compliance score is reflective of the probability for developing type-2 diabetes and subscribing to a treatment or prevention program, and the buying code score classifies an individual into a particular marketing category or segment for delivery of customized marketing material.

[0019] In one embodiment, the system comprises a web-based questionnaire that includes a series of questions from one or more of the following categories: (i) genetic factors or family history, (ii) age, (iii) weight, (iv) medical history, (v) overall health, (vi) medical symptoms, and (vii) lifestyle correlating with the onset of diabetes, which are used to determine the risk score. The risk score may be calculated based upon a first set of rules that assign point values to particular answers from the web-based questionnaire, in which answers that represent a lower risk are assigned a lower point value and answers that represent a higher risk are assigned a higher point value.

[0020] In another embodiment, the system may comprise a web-based questionnaire that includes a series of questions from one or more of the following categories: (i) genetic factors or family history, (ii) age, (iii) weight, (iv) medical history, (v) overall health, (vi) medical symptoms correlating with the onset of diabetes, (vii) individual concern and state of mind regarding development of diabetes, (viii) efforts to learn about diabetes, (ix) personality characteristics, and (x) lifestyle, which are used to determine the probability score. The probability score is calculated based upon a second set of design rules that assign point values to particular answers from the web-based questionnaire, in which answers that represent a lower probability score are assigned a lower point value and answers that represent a higher probability score are assigned a higher point value.

[0021] In another embodiment, the system further comprises a buying code score that is determined based upon one or more selected answers from the web-based questionnaire. In certain aspects the buying code is a four digit code reflective of different marketing categories or segments for delivery of customized marketing messages.

[0022] Additional embodiments of the invention include a system having a storage or memory to store laboratory testing results. Laboratory testing results may include blood levels of one or more biomarkers selected from the group consisting of high sensitivity CRP (hsCRP), insulin, glucose, Homa-IR, hemoglobin Ale (HbAlc), and/or adiponectin.

[0023] In other embodiments, biomarkers may be selected from the group consisting of: homocysteine, high sensitivity CRP, thyroid stimulating hormone, T3 (Free), T4 (Free), C- reactive protein, C-peptide, insulin, testosterone (Total), hemoglobin Ale (HbAlc), glucose, vitamin D (25 OH), VAP, and B12.

[0024] In still another embodiment, VAP may comprise one or more of the following tests associated with cholesterol: total LDL, LDL real (LDL-R), lipoprotein (a), IDL cholesterol, total HDL, HDL2, HDL3, total VLDL, VLDL1+2, VLDL3, total cholesterol, triglycerides, non-HDL cholesterol, remnant lipoproteins (IDL + VLDL3), LDL density (Pattern), apo B100 (apo B100), apolipoprotein Al (apo Al), apo B100/apo Al ratio, LDL4, LDL3, LDL2 and LDL1.

[0025] In additional embodiments, the system may comprise a total risk that is determined based upon the risk score, consultation, and laboratory testing results. In still further embodiments, the buying score may be revised based upon actual sales data.

[0026] In other embodiments, a system may comprise a storage or fourth memory containing a tiered ranking of individuals reflecting a likelihood of subscribing to a cash- based type 2 diabetes prevention program, in which individuals in a top tier (high) are more likely to subscribe, individuals in a middle tier (medium) are less likely to subscribe than individuals in the top tier, and individuals in a bottom tier (low) are less likely to subscribe than individuals in the middle tier.

[0027] In still another embodiment, the system may comprise storage or a fifth memory containing demographic information for an individual. The demographic information may include information from one or more of the following categories: (i) home valuations, (ii) credit scores, (iii) household incomes, (iv) residential locations, and (v) age.

[0028] In still other embodiments, the tiered ranking system is based upon one or more of the following: (i) the scores generated by the scoring engine, (ii) demographic information, and (iii) laboratory testing results.

[0029] In still other embodiments, the system further comprises: a software program capable of managing the tiered ranking of individuals; a sixth memory comprising individuals that have previously been contacted; wherein the sixth memory is populated with one or more individuals from the tiered ranking who have previously been contacted, leaving one or more open positions in the tiered ranking, wherein the one or more available positions are filled by (i) a client in a different tier, or (ii) a new prospective client. For example, an individual that has previously been contacted is moved from the tiered ranking system into a separate memory comprising a group of individuals that have previously been contacted. Once an individual has been moved into this group, a corresponding position becomes open in the tiered ranking of individuals.

[0030] The system further comprises a tiered ranking system in which the one or more available positions are filled by a client having a higher likelihood of subscribing to a cash- based type-2 diabetes prevention program.

[0031] In other embodiments, the system further comprises a software program capable of scheduling appointments for an individual in the tiered ranking regarding subscribing to a cash-based type-2 diabetes prevention or treatment program. This system may further comprise a software program capable of resolving overbooking of scheduled appointments, in which the software calculates a number N of overbooked appointments, selects from a group of overbooked individuals the N number of individuals who are the least likely to subscribe to the prevention program and identifies the N individuals for rebooking.

[0032] The system may further comprise software to manage sales and marketing data including the number of attempts to contact an individual. The system may further comprise software providing a series of follow-up materials, including emails, to clients that initially declined to subscribe to a cash-based type-2 diabetes prevention program.

[0033] The present invention also comprises a computer-implemented method for identifying pre-diabetes patients most likely to subscribe to a cash-based type-2 diabetes prevention program comprising: receiving responses to an initial web-based questionnaire; and analyzing responses through the use of a scoring engine, developed by entering customized scoring criteria into a sales and marketing software. This method may further comprise a scoring engine that outputs a risk score, a probability score and a buying code score, in which the risk score is reflective of an individual risk for developing type-2 diabetes, the probability score is reflective of a probability for developing type-2 diabetes and for subscribing to a treatment or prevention program, and the buying code score classifies an individual into a particular marketing category or segment for delivery of customized marketing material.

[0034] In another aspect of the invention, the method further comprises incorporating demographic information into a client model to identify clients most likely to subscribe to a cash-based type-2 diabetes prevention program.

[0035] In still another aspect of the invention, the method further comprises obtaining a tiered ranking of individuals based upon one or more of the following: (i) scores from the scoring engine, (ii) laboratory testing results and (iii) demographic information, in which individuals in a top tier are most likely to convert, individuals in a middle tier are less likely to convert than individuals in the top tier, and individuals in a bottom tier are less likely to convert than the middle tier.

[0036] As noted above, the present invention provides systems and methods for determining prospective clients most likely to subscribe to a prevention or treatment program regarding type-2 diabetes. [0037] Certain embodiments are directed to a method for providing preventive health care services cost effectively, comprising: providing a publically accessible system interface for receiving data from a subject that is in communication with a preventative health care services assessment system, the system configured for: receiving data related to a subject that has accessed the publically accessible system interface; determining a first risk score based on the data provided by the subject, the first risk score representing the subject's risk of developing diabetes; and identifying a subject at risk of developing diabetes if the first risk score exceeds a predetermined threshold. The method further comprising contacting the subject at risk of developing diabetes to schedule a blood draw for assaying a panel of biomarkers. In certain aspects biomarkers as used to determine if the subject is a pre- diabetic. If a subject is identified as at risk then a compliance score is determined using the received data, a subject is identified as compliant when the compliance score exceeds a predetermined threshold. The method may then qualifying a subject for a pre-diabetes program that is both at risk and compliant.

[0038] Other embodiments of the invention are discussed throughout this application. Any embodiment discussed with respect to one aspect of the invention applies to other aspects of the invention as well and vice versa. Each embodiment described herein is understood to be embodiments of the invention that are applicable to all aspects of the invention. It is contemplated that any embodiment discussed herein can be implemented with respect to any method or composition of the invention, and vice versa. Furthermore, compositions and kits of the invention can be used to achieve methods of the invention.

[0039] The use of the word "a" or "an" when used in conjunction with the term "comprising" in the claims and/or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one."

[0040] Throughout this application, the term "about" is used to indicate that a value includes the standard deviation of error for the device or method being employed to determine the value.

[0041] The use of the term "or" in the claims is used to mean "and/or" unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and "and/or." [0042] As used in this specification and claim(s), the words "comprising" (and any form of comprising, such as "comprise" and "comprises"), "having" (and any form of having, such as "have" and "has"), "including" (and any form of including, such as "includes" and "include") or "containing" (and any form of containing, such as "contains" and "contain") are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0043] Other objects, features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples, while indicating specific 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 from this detailed description.

DESCRIPTION OF THE DRAWINGS

[0044] The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of the specification embodiments presented herein.

[0045] FIG. 1 shows an overview of how an individual's risk score 210, probability score 220, and buying code score 230, may be assessed using a scoring methodology. In another embodiment, a total risk 550 may be generated by factoring in additional types of information into the model, such as demographic information 300, information obtained from a complementary consultation 400 and laboratory test results 500.

[0046] FIG. 2 shows an example of the criteria for a risk score calculation.

[0047] FIG. 3 shows an example of the criteria for a probability score calculation.

[0048] FIG. 4 shows an example of a four (4) digit buying code scoring schema.

[0049] FIG. 5 illustrates how various biomarkers can be assigned scoring values associated with an increased risk of developing pre-diabetes or type-2 diabetes. [0050] FIG. 6 illustrates an embodiment of a tiered ranking system 1000, in which prospective clients are grouped into a top tier 1010, middle tier 1020, or bottom tier 1030 based upon their likelihood of subscribing to a cash-based program of prevention for type-2 diabetes.

DESCRIPTION

[0051] Certain embodiments include methods or systems used to identify subjects that are at risk for developing diabetes or are pre-diabetic, referred to generally as "at risk individuals" or "at risk subjects". The methods or systems may then classify or prioritize those at risk individuals relative to the likelihood that the subject will comply with a pre- diabetic program or subscribe to a prevention program for type-2 diabetes.

[0052] The methods can comprise of receiving or gathering information from a subject. Information can be gathered directly, e.g., answering a series of questions, or indirectly by observing or monitoring the subject, e.g., monitoring interaction parameters on a web-site.

[0053] Intake Form - In certain aspects information is gathered via an intake form. The present invention incorporates an intake form 100 which contains a series of questions aimed to assess an individual's risk and probability for developing type-2 diabetes, as well as whether an individual is likely to subscribe to a prevention or treatment program. In one embodiment, the intake form 100 contains one or more questions directed towards assessing one or more of the following factors: genetic risks or family history of diabetes, body composition or body mass index (BMI), weight, age, level of physical activity, current symptoms correlating with the onset of pre-diabetes or type-2 diabetes, weight changes, level of concern regarding development of diabetes, efforts to learn about prevention or treatment of diabetes, motivation for seeking treatment or information about diabetes, and personality characteristics. The input received from the intake form can then be analyzed using a scoring engine. Certain factors are used by a risk determination engine, a compliance determination engine, or both a risk determination engine and a compliance determination engine to generate a particular score or classification.

[0054] Scoring Engine - Various types of scoring engines can be used. In certain aspects a scoring engine is an algorithm or program that calculates a risk score indicative of the likelihood of developing diabetes or identifying pre-diabetes, or a compliance or probability score that is indicative of the likelihood that an at risk subject will comply with a managed program. Other scoring engines can also be provided, such as scoring engines for monitoring, marketing, and consumer analysis or metrics.

[0055] For example, as shown in FIG. 1, based upon answers to an intake form 100, a risk score 210, probability score 220 and buying code score 230 can be calculated to identify individuals with a high likelihood of developing type-2 diabetes, a higher probability for compliance with a program, and a customer classification. In certain aspects there can be a first risk score generated based on answers received to the intake form and a second risk score that is based on both information received from the subject and the results of testing and consultation. Scores may be calculated using an automated scoring engine 200. In another embodiment, laboratory testing of biomarkers 500 and additional information from a consultation 400, may be used to determine an individual's total risk. In one embodiment, if an individual has a normal biomarker profile, indicating that a person's metabolism is functioning normally, an individual's total risk may be lowered. Additionally, if an individual has a high biomarkers profile, an individual's total risk may be raised. In another embodiment, a buying code score 230 can be revised based upon actual close rates or other relevant information. As more data is collected, rates of subscription for people in a particular buying code classification may be refined, and factored into client models.

[0056] In certain aspects the methods and/or system includes a scoring engine that generates a risk score based upon the information from the subject and metabolic/physiologic measurements (a second risk score). The risk score is reflective of an individual's risk for developing type-2 diabetes. In one embodiment the San Antonio diabetes prediction model is employed (SADPM) to generate a risk score, which is analyzed in combination with 1 hour plasma glucose levels, see Abdul-Ghani et al. Diabetes Care 34:2108-12, 2011. A risk score value of 0.065 can be used as a cut point for screening and selection of high risk individuals. The risk associated with an increase in the SADPM score and a 1 hour plasma glucose has been assessed and an example of confirmation studies is provided in the following table 1.

Table 1.

SAHS I Botnia Study

[0057] In one embodiment, the risk score is determined from information about a subject. In certain aspects the information can be derived in whole or in part from the answers to questions on an intake form questionnaire. Relevant questions or information may include one or more of the following: family history or genetic risk factors, level of exercise, age, history of weight change, metabolic or physiologic measurements, and current clinical symptoms correlating with diabetes. In still another embodiment, each answer of the questionnaire has a point value corresponding to an associated risk for developing diabetes. For example, individuals who are within a certain age range, such as age 41 or above, may have a higher risk for the development of diabetes than individuals in other age ranges, such as below age 30. Thus, the age range of highest risk may be associated with a higher point value than the other age range categories. Based upon this scoring system, factors associated with an increased risk would have a higher point value, and factors associated with a decreased risk would have a lower point value. In other embodiments, a point value may not be unique to a particular answer. That is, two or more answer choices to a particular question, may have the same point value. In another embodiment, point values may be summed to determine a cumulative risk. Accordingly, an individual with a higher cumulative number of points would be considered to have a higher risk of developing type-2 diabetes than an individual with a lower cumulative point value.

[0058] In another embodiment of the present invention, the risk score may range from a value between 1-15, with a value of 1 indicating a low risk and a value of 15 indicating a high risk. Under this scoring methodology, answers to the questionnaire corresponding to factors associated with an increased risk of developing diabetes would have a higher point value. Similarly, answers to the questionnaire corresponding to factors associated with a decreased risk of developing diabetes would have a lower point value.

[0059] For example, FIG. 2 demonstrates one method of calculating a risk score for an individual. In one embodiment of a risk scoring calculation, an individual with one or more family members having diabetes would be assigned 3 points. As another example, an individual who rarely or occasionally exercises would be assigned 3 points. In this scenario, a higher point value would reflect a higher risk of developing diabetes.

[0060] In another aspect a risk score can be based in part on answers to family history, lifestyle (amount of exercise) and age, various point values can be assigned to assess an individual's risk for developing pre-diabetes. For example, individuals in the 51 to 60 year old age group have the highest risk in the probability scoring schema shown above, receiving 10 points for being within this age range. Individuals in other age groups have a lower risk, receiving 1 or 5 points.

[0061] In still other embodiments, data or other relevant information that may be obtained from one or more of consultations with a health care professional, laboratory testing results or other means may be stored within the same software system or within a different software system having capabilities to track and manage data, and in particular, medical records. A health care profession can include those persons with adequate training to make various health related decisions, such as but not limited to a physician, a physician assistant, a nurse, a nurse practitioner, and the like.

[0062] In certain aspects the methods and/or system includes a scoring engine that generates a probability or a compliance score based upon the information directly or indirectly received from the subject. The probability or compliance score is reflective of an individual's probability of complying with a pre-diabetes program or subscribing to a treatment program. Relevant questions or information may include one or more of the following: motivation for seeking treatment or information about diabetes, family history or genetic risk factors, level of exercise, age, history of weight change, metabolic or physiologic measurements, and current clinical symptoms correlating with diabetes. In addition to the answers provided by a subject other assessments may be included in the probability or compliance score. These additional assessments can be based on psychological and behavioral observations or quantitation of psychological or behavioral observations. For example, response times can be classified and given a score, hovering on a web-page can be detected and scored, indecisiveness or decisiveness of answers can be observed or measured and given a score, etc. [0063] In certain aspects the probability or compliance score can be used in evaluating the classifying value of the pipeline, and for evaluating an individual's propensity to comply with a pre-diabetes program or subscribe to a prevention program. In one embodiment, the probability or compliance score is calculated in part on the answers to intake form questions involving one or more of the following categories: family history or genetic risk factors, level of exercise, age, history of weight change, current clinical symptoms correlating with diabetes, motivation for assessing risk of diabetes, efforts to learn about diabetes, concern regarding development of diabetes, and individual personality characteristics. In another embodiment, answers to intake form questions may have a value in the range of 1 to 10 points. In still other embodiments, the point weighting scheme for determination of the probability or compliance score is different than the point weighting scheme for the risk score. Under one type of scoring scenario, factors associated with an increased probability of compliance would have a higher point value, and factors associated with a decreased probability of compliance would have a lower point value. Other scoring methodologies can also be envisioned along a similar line of reasoning. In another embodiment, point values may be summed to determine a cumulative score. Accordingly, an individual with a higher cumulative number of points would be considered to have a higher probability of compliance than an individual with a lower cumulative point value.

[0064] Once the risk score and compliance score are assessed a number of supplementary assessments can be included to further divide the customer base into segments. It is noted that various segments need not be exclusive and may overlap other segments. In certain aspects the methods and/or system includes a scoring engine that generates a buying code score based upon the information directly or indirectly received. The buying code score classifies an individual into a particular marketing category or segment, for delivery of customized marketing material related to treatment or intervention programs for type-2 diabetes.

[0065] In additional embodiments of the invention, a sales and marketing software may be programmed with specific rules to create a scoring engine. [0066] A scoring engine may be configured with one or more design rules. The design rules govern the calculation of the various scores and are fully customizable by a programmer.

[0067] The buying code score 230 is also calculated based upon answers to questions on the intake form questionnaire 100. The buying code score 230 is derived from answer matches designed to classify individuals into a particular buying segment of the total population, and is used internally to assist with the sales process. In one embodiment, the buying score 230 is determined based upon a subset of questions contained within the intake form 100.

[0068] In another embodiment, the buying code score 230 is based upon a four digit code, four digit code reflective of a particular market segment. The first digit of the code may reflect a motivation for assessing the risk of developing diabetes, such as genetic (G), symptomatic (S), quality of life (Q), or health optimization (O) factors. The second digit of the buying code may reflect individual efforts to learn about diabetes such as personal research (R), prevention or treatment (i.e. solution (S)), consultation with a medical provider (D), or all categories (active). The third digit of the buying code may reflect individual concern regarding development of diabetes, such as high (H), medium (M), or low (L) concern. The fourth digit of the buying code may reflect personality characteristics to help tailor marketing messages to specific personalities. Personality characteristics may include traits such as being big picture oriented (B), results oriented (R), detail oriented (D), or preferring a cooperative environment (C).

[0069] FIG. 4 shows a sample 4 digit buying code scoring schema. For instance, an individual may be asked in the intake form questionnaire their reason for seeking diabetes treatment. Possible answers may include: (i) family history (Genetic (G)); (ii) being overweight and having current risk factors (Symptomatic (S)); (iii) wanting to maintain good health throughout their life (Quality of Life (L)); and (4) achieving optimal health (optimizer (O)). Depending on the answer to this question, an individual would be classified into one of the four columns above - the 1^st digit code. In a similar manner, additional characteristics can be derived using other questions, to classify an individual into a particular row (R, S, D, A) - the 2^nd digit code. The individual can be further classified using a 3^rd digit code to reflect Impulsivity (Low - Moderate - High), and a 4 letter code to reflect personality (B=Big Picture, R=Results, D=Detail, C=Cooperation). Based upon this type of analysis, customized marketing messages can be targeted to specific individuals regarding enrollment in a program. Additionally, client models can be developed based upon this information, which helps predict whether a person will subscribe to the prevention program based upon their buying code classification.

[0070] Once the intake form has been scored and a first risk score generated, at risk individuals will be contacted for subsequent follow-up, including consultation with a health care or medical personnel. Laboratory testing can be ordered if needed. In one embodiment, a second risk score can be determined and an individual's risk may be determined, reflecting additional factors, such as higher than normal levels of particular metabolites or biomarkers, or other factors discovered as part of a medical consultation. In certain aspects the second risk score is determined using the SADPM/1 hour plasma glucose two-step approach. During a client consultation the impact of various lifestyle factors, including but not limited to sleep, stress, relaxation, social networks, and education level may be assessed to determine the impact of these factors on the development of type-2 diabetes. Additionally, during consultation clinical indicators may be discussed and appropriate laboratory tests prescribed.

[0071] Laboratory Testing - Physicians or health care personnel, as appropriate, may choose from a variety of tests to further assess an individual's risk for developing type-2 diabetes. Such tests may include insulin dynamic testing to access pancreatic function and insulin response, hormone balance testing to evaluate levels of hormones that impact metabolism and inflammation, vascular integrity testing to assess vascular damage, and inflammation testing to evaluate levels of biomarkers which promote diabetes and cardiovascular disease. Vascular damage may impact vision, hearing, and cause nerve or kidney damage as well as lead to heart attack or stroke.

[0072] Additionally, a variety of blood tests can be helpful in assessing whether an individual has an abnormal or pre-diabetic metabolism. Commonly prescribed blood-based biomarker tests include hemoglobin AlC and fasting plasma glucose test (FPG). Additional blood tests are provided in FIG. 5 and are described in more detail below. [0073] Demographic Information - In other embodiments, demographic information, collected as part of the intake process or through other means, including location, age, gender, ethnicity, and income, may be factored into the scoring process. For example, diabetes is more common in certain ethnicities, including African Americans, Latinos, Native Americans, and Asian Americans/Pacific Islanders, as well as within the aged population. Thus, individuals in these categories are at increased risk for developing diabetes. Accordingly, in certain embodiments, demographic information may be helpful in assessing the overall likelihood of developing type-2 diabetes.

[0074] In still another embodiment, a software program coupled to a communication interface can collect additional publically available demographic information. This additional demographic information may include, but is not limited to, information available through public records such as credit scores, home valuations, monthly mortgage payments, outstanding mortgage amounts, zip codes, as well as other factors such as distance from a treatment center. This demographic information may overlap in part or fully with the demographic information collected as part of the intake process.

[0075] Lead Management - Ranking and Contact History - In further embodiments, a system has a capability of generating a tiered ranking system based upon level of risk of developing type-2 diabetes. Two or more tiers may be structured to classify subjects according to their overall risk, or their overall likelihood of compliance and/or likelihood of subscribing to a prevention program. In certain aspects, individuals can be classified as high risk, middle risk, or lower risk. This ranking system is based upon the first risk score and/or the second risk score. In certain aspects a first ranking is generated based on the first risk score and a second ranking is generated based on the second risk score. A ranking system is also helpful to ensure that patients at higher risk are given higher priority than patients having lower risk, to help ensure that high risk clients are contacted in an expedient manner. In certain aspects a call center may begin contacting potential clients who have been identified as having the highest risk first.

[0076] In other embodiments, a system can be configured to identify and manage sales leads. Lead management may include generating and maintaining a ranking of subjects having a higher probability or compliance score, which can identify those who are most likely to subscribe to a method of preventing type-2 diabetes. The identification of those likely to subscribe can also include a buying code score and/or demographic data, as well as risk and probability scores.

[0077] Client Grading Scale - Scores can be entered into or received by a client grading scale module. The client grading module can perform the function of identifying individuals identified to be most likely to subscribe to a cash-based program. Individuals having high, medium, or low likelihood of developing type-2 diabetes are identified based upon scoring criteria described herein. In additional embodiments, additional information, such as demographic information, may factor into the client grading scale to output a category of individuals most likely to subscribe ("green group"). In this embodiment, individuals within the Green category may be given priority for scheduling of consultations.

[0078] In another aspect of the invention, a system can apply scores and demographic information to classify individuals into one or more categories based upon a likelihood to comply with or subscribe to a treatment or prevention program. In a preferred embodiment, potential clients are placed into one of three categories: Green leads, Yellow leads, or Red leads, wherein membership in the Green category is influenced by demographic information such as household incomes, credit scores, and home market values, suggesting that the client has the financial means to subscribe to the program. This group may be given priority for follow-up regarding scheduling of consultations. However, members in others groups will also be contacted in a timely manner.

[0079] In another embodiment, the functionality of risk ranking may be integrated with the functionality of the client grading scale such that a risk score, probability score and buying code score along with demographic information are received as inputs into a process capable to generate Green leads, Yellow leads, or Red leads.

[0080] Management of Sales Information - In a further aspect of the invention, the system maintains a contact history detailing the number of times that an attempt has been made to contact a particular lead. If an attempt to contact a lead has been made, but was unsuccessful, the system has the capability to store the attempted contact as part of tracking history, without such attempt affecting the rankings or tiers of the potential leads. The contact history may further include the identity of the person making the call, the call time, the number of call attempts, as well as details regarding the outcome of the call (i.e. whether a lead subscribed to the program or declined to subscribe). In particular, in the event that a lead indicated that the prevention program was not currently of interest, or needed more time to make a decision about subscribing to the prevention program, the software has the capability to store this outcome in the system. This information would trigger a series of nurturing emails and text messages automatically generated and sent by the software to the potential lead, over a period of several months, or even years, for future follow-up.

[0081] Scheduling - In another embodiment, in the event that contact with a lead was made, the software may help manage the next step in the identification of clients who are most likely to subscribe to a system for preventing type-2 diabetes by scheduling appointments, including blood draws for laboratory assessments and consultations with medical personnel. The software has the capability to book appointments in an automated fashion.

[0082] In a further embodiment, the software may also help manage overbooking situations. In cases in which overbooking occurs, the software has the capability to select the optimal subset - the subset of clients most likely to subscribe - for retention, and select the clients least likely to subscribe such that these clients will be contacted for rebooking. Factors for making this determination include the first or second risk score, probability score, and/or buying code, and additional factors such as demographic information, ability to afford treatment, and enrollment in a health insurance plan.

[0083] In a further embodiment, the system may handle scheduling consultations and blood draws at a variety of locations across the country. In other words, the capabilities of the software are not limited by geographic boundaries.

[0084] Sales - Once a subject is brought in for a consultation and meets with a physician or other medical personnel, a recommendation is made as to whether a subject would benefit from enrollment in a type-2 diabetes prevention program. A professional sales agent then discusses with the prospective patient various economic options regarding enrollment and may finalize the sale. [0085] In still other embodiments, the sales and marketing software may be used to track close rates of categories of prospective clients, as well as close rates of individual sales agents.

[0086] In another embodiment, the sales and marketing software may track other statistics associated with the sales process including: (i) number of days from contact to laboratory testing (i.e. blood draw), (ii) number of days from contact to consultation, (iii) number of days from contact to client conversion (i.e. subscription to prevention program).

[0087] In another embodiment, the sales and marketing software may also be configured to track contact to sale statistics including conversion rate at time of consultation and conversion rate post consultation.

[0088] Medical Records - In another aspect of the present invention, system may comprise multiple software packages such as marketing and sales software that may interface with medical records software. The combined software packages (at least one) have the capacity to schedule consultations and laboratory testing, as well as store the results of the laboratory testing and other relevant data. The medical records management software component may also be configured to track enrollment information.

[0089] The marketing and sales software may also interface with a medical records management software program including but not limited to commercially available programs and customizable programs such as eClinicalWorks®, Meditech®, NexGen Healthcare®, Athena Health® or Greenway Medical®.

[0090] In other embodiments, the total risk may be stored and accessible by one or more medical management software programs.

[0091] Client Modeling - Based on the above information, various client models can be developed. In one embodiment, the client model may include one or more of the following: demographic information, first risk score, second risk score, probability score, buying code score, clinical symptoms of diabetes, laboratory biomarker profiles, sales information, and sale statistics. Such client models can be analyzed to refine characteristics of particular buying code score groups to better predict whether a particular buying code score group will subscribe to a type-2 diabetes prevention program.

[0092] Architecture - Many embodiments may be envisioned in regards to system architecture and design. The intake form will typically be available on a website, hosted by a web server. The system will typically comprise a website, a webserver, programming languages to program the web form questionnaire, memory to store the results of the questionnaire, and a scoring engine. In other embodiments, the data may be stored in one or more databases or specialized pieces of software for data analysis and management.

[0093] A variety of programming languages are available for implementing web-based questionnaires. Such programming languages may include, but are not limited to, Java, HTML, XML, AJAX, Perl, PHP, or any other web development language. Additionally, a web server may use a variety of deployment options including UNIX, LINUX, Apache, or ASP.net.

[0094] Additional languages may also include functional, object-oriented, and scripting languages such as C, C++/Java, C#, Perl/Python, and Shell.

[0095] Answers to the intake form may be stored in one or more databases. Such databases may utilize a variety of database management technologies including SQL, Oracle, and MySQL.

[0096] The output of the scoring engine(s) may be stored within the same database as the answers to the intake form or within a different database.

[0097] In a further aspect, the invention may utilize a second system to house laboratory test results as well as a patient's total risk. In a further embodiment, the laboratory test results may be stored within medical records software, compliant with federal regulations regarding the privacy of individual medical information.

[0098] Biomarkers - A biomarker is an organic biomolecule that is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease or condition) as compared with another phenotypic status (e.g., not having the disease or condition). A biomarker is differentially present between different phenotypic statuses if the mean or median expression level of the biomarker in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t- test, ANOVA, Kruskal- Wallis, Wilcoxon, Mann- Whitney and odds ratio. Biomarkers, alone or in combination, provide measures of relative risk that a subject belongs to one phenotypic status or another. As such, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics) and of drug toxicity.

[0099] Biomarkers can include one or more of hemoglobin AIC and fasting glucose, insulin, Cpeptide, HOMA-IR, lipid panel, C-reactive protein, homocysteine, thyrod hormones, testosterone, vitamin D, vitamin B12.

[00100] Hemoglobin AIC and Fasting Blood Glucose are considered the gold standards in the United States when it comes to evaluating diabetes risk. Hemoglobin AIC is the percentage of blood cells that have sugar attached to them (over a 3 month period), while fasting blood glucose is the amount of sugar in your blood at the time of the test. Hemoglobin AIC is a form of hemoglobin that is measured primarily to identify the average plasma glucose concentration over prolonged periods of time. It is formed in a non- enzymatic glycation pathway by hemoglobin's exposure to plasma glucose. Normal levels of glucose produce a normal amount of glycated hemoglobin. As the average amount of plasma glucose increases, the fraction of glycated hemoglobin increases in a predictable way. This serves as a marker for average blood glucose levels over the previous months prior to the measurement. A glucose test is a type of blood test used to determine the amount of glucose in the blood. Patients are instructed not to consume anything but water during the fasting period.

[00101] Insulin is a hormone produced in the pancreas that regulates the amount of glucose in the blood. Insulin is a peptide hormone, produced by beta cells in the pancreas, and is central to regulating carbohydrate and fat metabolism in the body. It causes cells in the skeletal muscles, and fat tissue to absorb glucose from the blood.

[00102] C-peptide is an ideal biomarker for measuring insulin production in your body over the last few months. The connecting peptide, or C-peptide, is a short 31-amino-acid protein that connects insulin's A-chain to its B-chain in the proinsulin molecule. [00103] HOMA-IR is a calculation of insulin resistance. The homeostatic model assessment (HOMA) is a method used to quantify insulin resistance and beta-cell function. Typically HOMA-IR is calculated based on the formula HOMA-IR = (glucose x insulin)/405.

[00104] Lipid profile or lipid panel, is a panel of blood tests that serves as an initial broad medical screening tool for abnormalities in lipids, such as cholesterol and triglycerides. Lipid panel tests measures 5 types of "good" and "bad" cholesterol biomarkers which are critical to cardiovascular health. In certain aspects a lipid panel can include Low-density lipoprotein (LDL), High-density lipoprotein (HDL), Triglycerides, Total cholesterol, and very low- density lipoprotein (VLDL).

[00105] C-reactive protein (CRP) is an indicator of inflammation in the body, a risk factor for diabetes. CRP is an annular (ring-shaped), pentameric protein found in the blood plasma, the levels of which rise in response to inflammation (i.e., C-reactive protein is an acute -phase protein). Its physiological role is to bind to phosphocholine expressed on the surface of dead or dying cells (and some types of bacteria) in order to activate the complement system via the C1Q complex.

[00106] Elevated levels of homocysteine are related to the early development of heart disease, stroke and other diseases. Homocysteine is a non-protein a-amino acid. It is a homologue of the amino acid cysteine, differing by an additional methylene bridge (-CH2-). It is biosynthesized from methionine by the removal of its terminal methyl group. Homocysteine can be recycled into methionine or converted into cysteine with the aid of B- vitamins.

[00107] Thyroid hormones play a role in carbohydrate metabolism and are linked to diabetes. The thyroid hormones, triiodothyronine (T3) and thyroxine (T4), are tyrosine -based hormones produced by the thyroid gland that are primarily responsible for regulation of metabolism. Iodine is necessary for the production of T3 and T4.

[00108] Testosterone levels may be reduced in men with diabetes. Testosterone is a steroid hormone from the androgen group and is found in mammals, reptiles, birds, and other vertebrates. In mammals, testosterone is secreted primarily in the testicles of males and the ovaries of females, although small amounts are also secreted by the adrenal glands. It is the principal male sex hormone and an anabolic steroid. In men, testosterone plays a key role in the development of male reproductive tissues such as the testis and prostate as well as promoting secondary sexual characteristics such as increased muscle, bone mass, and the growth of body hair. In addition, testosterone is essential for health and well-being as well as the prevention of osteoporosis.

[00109] Obesity combined with vitamin D deficiency puts people at higher risk for insulin resistance. Vitamin D is a group of fat-soluble secosteroids responsible for enhancing intestinal absorption of calcium, iron, magnesium, phosphate and zinc. In humans, the most important compounds in this group are vitamin D3 (also known as cholecalciferol) and vitamin D2 (ergocalciferol). Cholecalciferol and ergocalciferol can be ingested from the diet and from supplements. The body can also synthesize vitamin D (specifically cholecalciferol) in the skin, from cholesterol, when sun exposure is adequate.

[00110] Vitamin B12 helps protect against nerve damage, a complication of diabetes. Vitamin B12, vitamin B12 or vitamin B-12, also called cobalamin, is a water-soluble vitamin with a key role in the normal functioning of the brain and nervous system, and for the formation of blood. It is one of the eight B vitamins. It is normally involved in the metabolism of every cell of the human body, especially affecting DNA synthesis and regulation, but also fatty acid synthesis (especially odd chain fatty acids) and energy production.

[00111] FIG. 5 illustrates how various biomarkers can be assigned scoring values associated with an increased risk for developing type-2 diabetes. For instance, a person with glucose levels below 90 mg/dL would be considered to have low risk, and would be assigned zero points. A person with glucose levels above 125 mg/dL would be considered to have high risk, and would be assigned 25 points. In this scoring scheme, a higher number of points reflects a higher risk. Additionally, different biomarkers can receive different weighting to reflect which biomarkers may indicate a greater risk. For example, HbAlc (40 points) has a higher point value than a biomarker such as total LDL (15 points), indicating that high levels of HbAlc is a more significant risk factor than total LDL in regards to development and progression of this disease. [00112] As described previously, biomarker testing can be an important component for determining an individual's risk of developing type-2 diabetes. Frequently, biomarker levels may be elevated over normal levels, but still below the diabetic threshold, to indicate an alteration in a normal metabolic profile. Testing for such levels via blood tests can be an important component in regards to identifying individuals at high risk for developing type-2 diabetes.

[00113] In one embodiment, biomarkers may be selected from the group consisting of: Homocysteine, High Sensitivity CRP, Thyroid Stimulating Hormone, T3 (Free), T4 (Free), C-reactive protein, C-Peptide, Insulin, Testosterone (Total), Hemoglobin Ale, Glucose, Vitamin D (25 OH), VAP™ lipid panel, and B12. A VAP lipid panel or a lipid panel may comprise one or more of the following tests associated with cholesterol: Total LDL, LDL Real (LDL-R), Lipoprotein (a), IDL Cholesterol Total HDL, HDL2, HDL3, Total VLDL, VLDLl+2, VLDL3, Total Cholesterol, Triglycerides, Non-HDL Cholesterol, Remnant Lipoproteins (IDL + VLDL3), LDL Density (Pattern), Apo B100 (Apo B100), Apolipoprotein Al (Apo Al), Apo B100/Apo Al Ratio, LDL4, LDL3, LDL2 and LDL1.

[00114] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about." Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present invention. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. [00115] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[00116] Specific embodiments disclosed herein may be further limited in the claims using consisting of or consisting essentially of language. When used in the claims, whether as filed or added per amendment, the transition term "consisting of excludes any element, step, or ingredient not specified in the claims. The transition term "consisting essentially of limits the scope of a claim to the specified materials or steps and those that do not materially affect the basic and novel characteristic(s). Embodiments of the invention so claimed are inherently or expressly described and enabled herein.

I. Examples

[00117] The following examples as well as the figures are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples or figures represent techniques discovered by the inventors to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

[00118] Example 1 - A potential client is browsing the Internet, and discovers an advertising link to the cash-based program for prevention of type-2 diabetes. The advertising link may include language asking a potential client if he or she knows his or her individual risk for developing type-2 diabetes. By clicking on this advertising link, the potential client is brought to a webpage with the Intake Form 100 questionnaire. [00119] The Intake Form 100 is launched, and the client is asked to fill out a series of questions regarding one or more of the following categories: family members and relatives having type-2 diabetes, present level of physical activity, age, weight changes, attitude and effort to learn about prevention of type-2 diabetes, current medical health, and personality characteristics. In some embodiments, the Intake Form 100 may consist of about 9 questions.

[00120] Upon completion of the Intake Form 100, the client is asked for various contact information, including, but not limited to, a first and last name, a telephone number, an address, and an email address. Once the client has provided required contact information, the client clicks a link to submit the Intake Form 100 questionnaire for analysis by the scoring engine 200.

[00121] Once the Intake Form 100 is submitted, the scoring engine 200 analyzes the selected answers to calculate a risk score 210, a probability score 220 and a buying code score 230. One embodiment of the invention is shown in FIG. 1.

[00122] A risk score 210 typically is calculated from a subset of the questions on the Intake Form 100, with a particular point value assigned to each answer. The risk score may be assessed based upon particular questions regarding family history, level of exercise, age, weight change over a period of time, as well as symptoms correlating with the onset or progression of type-2 diabetes. This score is calculated by assigning point values to selected answers to questions of the Intake Form 100 and summing up the point values to arrive at a total point value. In one embodiment, a higher point value, or score, indicates a higher probability. Multiple answers to a question on the Intake Form 100 may be assigned the same point value. In another embodiment, a risk score 210 may be assigned a score of between 1-15 points based upon the answers to the Intake Form 100. An embodiment of a risk score calculation is shown in FIG. 2.

[00123] A probability score 220 is calculated from all or almost all of the questions of the

Intake Form 100. Questions include family history, level of exercise, age, weight changes over a period of time, state of mind, as well as symptoms correlating with the onset or progression of type-2 diabetes, level of concern about becoming diabetic, efforts to learn about diabetes, and personality characteristics. The questions used to determine the probability score 220 may overlap, in part or in whole, with the questions used to determine the risk score and the buying code score. This score is also calculated by assigning point values to selected answers to questions of the Intake Form 100 and summing up the point values to arrive at a total point value. In one embodiment, a higher point value, or score, indicates a higher probability. Multiple answers to a question on the Intake Form 100 may be assigned the same point value. An embodiment of a probability score calculation is shown in FIG. 3.

[00124] A buying code score 230 is used to divide up a population of potential clients into particular categories or segments to deliver customized marketing materials to particular groups of customers. This is also known as marketing segmentation. The buying code score 230 is an n-digit code, where n is an integer greater than or equal to 2. In one embodiment, the buying code score 230 is a 4-digit code, as described in FIG. 4. The buying code score 230 is determined from a subset of questions of the Intake Form 100. In another embodiment, the buying code score 230 is a 4-digit score based upon four questions from the Intake Form 100, which generates up to 256 possible marketing categories. The buying code score 230 is used to help determine the likelihood that a prospective client will subscribe to the system. As additional client data is collected, and sales rates are correlated with a particular marketing segmentation, the buying code score 230 may be revised and factored into the analysis.

[00125] In another embodiment, as soon as the potential client submits the selected answers of the Intake Form 100, client contact information (name, address, email, telephone number) is used to gather additional demographic information 800 regarding a prospective client. In one embodiment, this is accomplished by supplying client contact information to a 3^rd party software 700 with the capability to communicate with one or more databases containing publically available information such as household incomes, credit scores, home values, monthly rent payments and other demographic information. An embodiment of this aspect of the invention is shown in FIG. 6.

[00126] A risk score 210 is emailed to the potential client. At risk clients may also be contacted via telephone to discuss the results.

[00127] The scores from the scoring engine 200 are used to classify individuals into two or more categories. In one embodiment, an individual is classified into three categories, indicating high, intermediate, or low likelihood of developing diabetes. In one embodiment, individuals with high likelihood are given priority in regards to other groups for follow-up appointments (consultations and blood draws).

[00128] This classification may be further refined by incorporating demographic information to identify at-risk clients most likely to subscribe. This information may be factored into a client grading scale 1000 to help identify clients that are most likely to subscribe to a treatment or prevention plan. Based on a client grading scale 1000, at-risk individuals can be classified into three categories or tiers: (i) those most likely to subscribe (Green) 1010; (ii) those in a middle tier who are neutral (Yellow) 1020; and (iii) those in a lower tier who are considered less likely to subscribe (Red) 1030. In a preferred embodiment, individuals with high risk are given priority in regards to other groups for scheduling follow-up appointments, such as setting up a complementary consultation with a medical professional and testing for particular biomarkers associated with an increased risk of pre-diabetes. In another preferred embodiment, the Green tier 1010 is populated with individuals at high risk who are most likely to subscribe; these individuals are given priority over other tiers for consultations and testing.

[00129] Patients determined to be at risk are contacted for a complementary consultation and possibly scheduling of laboratory testing 1100. The system can be designed to schedule consultations as well as book appointments for a blood draw for biomarker testing.

[00130] In one embodiment, an individual is brought in for a complementary consultation. During such a consultation, additional information may be uncovered which may affect a client's risk score.

[00131] The individual may decide that based upon his risk score and other factors, that he or she is interested in subscribing to the program. This individual is contacted by a sales agent to discuss various treatment or prevention plans. The individual chooses a plan and subscribes upon the initial consultation.

[00132] Example 2 - Example 2 is similar to Example 1 up through the initiation of client contact. The potential client agrees to be brought in for a consultation, but instead of subscribing immediately, decides to pursue biometric testing before making a decision. The prospective client is referred for a blood draw to perform a biomarker analysis. A prospective client's blood is analyzed to determine the concentrations of various molecules within the blood. Concentrations of a particular biomarker falling within a specified range are assigned a point value correlating to a risk of developing type-2 diabetes. For example, concentrations of glucose below a value of 90 mg/dL are considered to fall within normal ranges, and therefore, are assigned 0 points. A glucose level of 90 to 100 mg/dL, while still falling below a threshold traditionally used to indicate diabetic disease, is considered to be slightly elevated under the criteria of the present invention and is consequently assigned a point value of 10. Higher glucose concentrations are assigned even higher point values; for instance, a glucose concentration of 100 to 109 mg/dL is assigned 15 points, and so forth. A variety of biomarker profiles are illustrated in FIG. 5. As is evident from this profile, ranges have been established such that biomarker ranges approaching a traditional threshold are assigned corresponding point values indicating increased risk. Such a methodology differs from traditional screening in that a graded point system is used, rather that a binary system (i.e. a value above a single threshold), which is traditionally used to diagnose diabetes. Point values for the various biomarker levels may be summed to establish an individual's risk level. Such data may then be combined with the risk score to establish a total risk. A client is then contacted for subsequent follow-up to discuss his or her total risk in view of the laboratory data results.

[00133] Based upon the risk assessment, a client then decides to subscribe to the program. A client is contacted by a sales agent to discuss various plans. The system is able to keep track of various sales statistics, such as time from initial contact to sale, etc., as defined elsewhere in this specification.

[00134] Example 3 - Example 3 is similar to Example 2 up through the discussion of the risk assessment with the client. However, the individual decides to postpone subscription to the program until a future point in time.

[00135] In this scenario, the individual scores are stored within the system, and the prospective client is selected to receive marketing materials on a periodic basis regarding subscription to the program. [00136] As mentioned above, based upon a subset of questions from the Intake Form 100, the population of prospective clients may be divided up into particular categories to receive specific marketing material. For example, a prospective client may be classified as being "big picture", "results-oriented", "detail-oriented", or "cooperative". Based upon this type of classification, marketing material may be specifically designed for and targeted to a particular class. A person who is "detail-oriented" may be targeted for delivery of marketing materials containing highly detailed information about type-2 diabetes and available treatment programs, while someone who is "big picture" oriented may be presented with a high level overview of essentially the same information.

[00137] Based upon customized marketing materials, risk factors, and information, an individual may decide to subscribe to the prevention or treatment program. The client is then contacted by a sales representative to discuss various treatment and prevention plans, and the client subscribes. The system is able to track various sales related statistics regarding this process.

[00138] Example 4 - Managing Overbooking Appointments. The system also has the capability of managing client overbooking. Based upon the scores from the scoring engine 200, and optionally, demographic data 800, the system will automatically select a number of patients needed for rebooking. In some embodiments, the system will give priority to patients most likely to subscribe, and will select patients least likely to subscribe for rebooking. In other embodiments, the system will retain patients with the highest risk, and select patients with lower relative risk for rebooking. In still other embodiments, the system will assign positions based upon both risk and likelihood of subscribing.

[00139] Example 5 - Example 5 provides examples of the identification of subjects at risk of developing diabetes and identification of pre-diabetic subjects.

[00140] SADPM Results. In one example San Antonio Disease Prevention Model (SADPM) risk scores were calculated for 678 patients participating in a PreDiabetes Centers Complimentary screening profile. Of those, 93 were below the risk cutoff of 0.065 and 585 were at or above the cutoff (see table 2).

Table 2 Program

SADPM Total Status Number Percentage

Enrolled 18 19.4%

<0.065 93

Not Enrolled 75 80.6%

Enrolled 233 39.8%

>=0.065 585

Not Enrolled 352 60.2%

[00141] Of those that were below the risk cutoff, 18 of 93 (19.4%) enrolled in the program(s). Of those at or above the risk cutoff, 233 (39.8%) - more than double the percentage - of 585 enrolled in the pre-diabetes prevention program(s).

[00142] Additional PreDiabetes Risk Score Determinations. A pre-diabetes risk score can also be derived from biomarkers and patient demographics including, but not limited to, those in the table below. From the observed or measured variables, a numerical value is assigned from the appropriate look-up tables and the sum of these assigned values becomes the "Risk Score" (see table 3).

Table 3

[00143] The risk score is used to define a risk of pre-diabetes - "PreD Risk" - with a score of "<10" indicating a "Low Risk" (L), scores between 10 and 20 indicating "Moderate Risk" (M) and scores greater than 20 indicating "High Risk" (H) for having and/or developing a pre-diabetic state. As an example, the table above contains results, risk scores and risk interpretation for 10 patients. [00144] Table 4 summarizes results based on risk score for at risk of developing diabetes. Table 5 summarize results based on a probability score. Table 6 summarizes results based on compliance score and biomarker levels.

Table 4

Conversion to Blood Conversion to Client

Draw from Lead from Blood Draw

Risk Score Count Percentage Count Percentage

16

1 26.2% 7 43.8%

(n = 61)

3

2 5.9% 0 0.0%

(n = 51)

34

3 4.9% 11 32.4%

(n = 691)

0

4 0.0% 0 0.0%

(n = 0)

19

5 4.2% 2 10.5%

(n = 454)

95

6 4.8% 24 25.3%

(n = 1,978)

0

7 0.0% 0 0.0%

(n = 0)

47

8 5.5% 7 14.9%

(n = 860)

151

9 5.5% 64 42.4%

(n = 2,726)

0

10 0.0% 0 0.0%

(n = l)

46

11 7.0% 16 34.8%

(n = 657)

113

12 5.9% 48 42.5%

(n = 1,917)

0

13 0.0% 0 0.0%

(n = 0)

21

14 9.4% 10 47.6%

(n = 224)

37

15 5.0% 15 40.5%

(n = 738) Table 5

Conversion to Blood Conversion to Client

Draw from Lead from Blood Draw

Probability

Percentage Score Count Percentage Count

0

10-19 0.0% 0 0.0%

(n = 0)

0

20-29 0.0% 0 0.0%

(n = 48)

13

30-39 2.8% 4 30.8%

(n = 459)

70

40-49 5.3% 18 25.7%

(n = 1,333)

119

50-59 5.6% 38 31.9%

(n = 2,135)

132

60-69 5.5% 42 31.8%

(n = 2,409)

113

70-79 6.1% 51 45.1%

(n = 1,861)

71

80-89 5.8% 30 42.3%

(n = 1,222)

35

90-99 5.6% 11 31.4%

(n = 622)

11

100 - 109 5.0% 2 18.2%

(n = 220)

0

110-120 0.0% 0 0.0%

(n = 49)

Table 6

Program

Com liance

Claims

1. A method of evaluating a potential pre-diabetic subject for enrollment in a managed pre-diabetes program comprising:

receiving answers to a screening questionnaire from a subject;

calculating a first risk score based on the answers received;

identifying a subject at risk of developing diabetes when the first risk score exceeds a predetermined value;

calculating a compliance score based on the answers received and identifying a subject at risk of developing diabetes that has an increased likelihood for complying with a pre-diabetes program; and

classifying a subject in rank order for highest priority including a subject at risk having a high likelihood of compliance and a lowest priority including a subject having a low risk for developing diabetes and least likely to comply.

2. The method further comprising contacting a subject likely to comply with a managed pre-diabetes program.

3. A system for identifying pre-diabetes patients most likely to subscribe to a cash- based type-2 diabetes prevention program comprising:

a computer processor; a first memory containing responses to an initial web-based questionnaire; a second memory containing one or more scores generated by a scoring engine that upon execution by the computer processor analyzes the responses to the web-based questionnaire according to a series of scoring criteria, wherein the one or more scores reflect a likelihood that the patient will subscribe to a cash-based type 2 diabetes prevention program.

4. The system of claim 3 in which the web-based questionnaire includes a series of questions from one or more of the following categories: (i) genetic factors or family history, (ii) age, (iii) weight, (iv) medical history, and (v) overall health.

5. The system of claim 3 in which the scoring criteria is based upon a series of design rules.

6. The system of claim 5 in which the scores generated by the scoring engine are based upon selected answers from the web-based questionnaire and include: (i) a risk score; (ii) a probability score; and (iii) a buying code score, in which the risk score is reflective of an individual risk for developing type-2 diabetes, the probability score is reflective of a probability of developing type-2 diabetes and subscribing to a treatment or prevention program, and the buying code score classifies an individual into a particular marketing category or segment for delivery of customized marketing material.

7. The system of claim 6 in which the web-based questionnaire includes a series of questions from one or more of the following categories: (i) family history or genetic factors, (ii) age, (iii) lifestyle, (iv) weight, and (v) medical symptoms correlating with the onset of diabetes, which are used to determine the risk score.

8. The system of claim 7 in which the risk score is calculated based upon a first set of design rules that assign point values to particular answers from the web-based questionnaire, in which answers that represent a lower risk are assigned a lower point value and answers that represent a higher risk are assigned a higher point value.

9. The system of claim 6 in which the web-based questionnaire includes a series of questions from one or more of the following categories: (i) family history or genetic factors, (ii) lifestyle, (iii) age, (iv) weight, (v) medical symptoms correlating with the onset of diabetes, (vi) individual concern and state of mind regarding development of diabetes, (vii) efforts to learn about diabetes, and (viii) personality characteristics, which are used to determine the probability score.

10. The system of claim 9 in which the probability score is calculated based upon a second set of design rules that assign point values to particular answers from the web-based questionnaire, in which answers that represent a lower probability score are assigned a lower point value and answers that represent a higher probability score are assigned a higher point value.

11. The system of claim 6 in which the buying code score is determined based upon one or more selected answers from the web-based questionnaire, in which the buying code is a four digit code reflective of different marketing categories or segments for delivery of customized marketing messages.

12. The system of claim 6 further comprising a third memory containing laboratory testing results.

13. The system of claim 12 in which the laboratory testing results include blood levels of one or more biomarkers selected from the group consisting of adiponectin, SFRP4 protein, homocysteine, high sensitivity CRP, thyroid stimulating hormone, C-reactive protein, C-Peptide, Insulin, Testosterone, Hemoglobin Ale, Glucose, Vitamin D, VAP, and B12.

14. The system of claim 12 in which a total risk may be determined at least in part based upon the risk score and laboratory testing results.

15. The system of claim 11 in which a buying code score may be adjusted based upon actual sales data.

16. The system of claim 14 further comprising a fourth memory containing a tiered ranking of individuals reflecting a likelihood of subscribing to a cash-based type 2 diabetes prevention program, in which individuals in a top tier are more likely to subscribe, individuals in a middle tier are less likely to subscribe than individuals in the top tier, and individuals in a bottom tier are less likely to subscribe than individuals in the middle tier.

17. The system of claim 16 further comprising a fifth memory containing demographic information for an individual.

18. The system of claim 17 in which the demographic information includes information from one or more of the following categories: (i) home valuations, (ii) credit scores, (iii) household income, (iv) residential location and (v) age.

19. The tiered ranking system of claim 17 in which the individual tiers are based upon one or more of the following: (i) the scores generated by the scoring engine, (ii) demographic information, and (iii) laboratory testing results.

20. The system of claim 19 further comprising: a software program capable of managing the tiered ranking of individuals; a sixth memory comprising individuals that have previously been contacted; wherein the sixth memory is populated with one or more individuals from the tiered ranking who have previously been contacted, leaving one or more available positions in the tiered ranking, wherein the one or more available positions are filled by (i) a client in a different tier, or (ii) a new prospective client.

21. The system of claim 20 in which the one or more available positions are filled by a client having a higher likelihood of subscribing to a cash-based type-2 diabetes prevention program.

22. The system of claim 18 further comprising a software program capable of scheduling appointments for an individual in the tiered ranking regarding subscribing to a cash-based type-2 diabetes prevention program.

23. The system of claim 22 further comprising a software program capable of resolving overbooking of scheduled appointments, in which the software calculates a number N of overbooked appointments, selects from a group of overbooked individuals the N number of individuals who are the least likely to subscribe to the prevention program and identifies the N individuals for rebooking.

24. The system of claim 20 further comprising software to manage sales and marketing data including the number of attempts to contact an individual.

25. The system of claim 24 further comprising software to provide a series of follow- up materials including emails to clients that initially declined to accept an appointment or declined to subscribe to a cash-based type-2 diabetes prevention program.

26. The system of claim 5, in which the design rules further comprise a weighting scheme that assigns a point value to each answer of the web-based questionnaire.

27. The system of claim 26, in which the weighting scheme assigns higher point values to answers of the web-based questionnaire with an increased risk of developing type-2 diabetes.

28. The system of claim 26, in which the weighting scheme assigns higher point values to answers of the web-based questionnaire with increased probability of subscribing to the cash-based type 2 diabetes prevention program.

29. The system of claim 12 in which the laboratory testing results include blood levels of one or more biomarkers selected from the group consisting of hsCRP, C-Peptide, Insulin, HbAlc, glucose, Total LDL, Total HDL, Total Cholesterol and Triglycerides.

30. The system of claim 29 further comprising a weighting scheme that assigns a point value to a specific range of a biomarker level.

31. The system of claim 30, in which the weighting scheme assigns higher point values to ranges of a biomarker level correlating with increased risk of developing type-2 diabetes.

32. A computer-implemented method for identifying pre-diabetes patients most likely to subscribe to a cash-based type-2 diabetes prevention program comprising:

receiving responses to an initial web-based questionnaire; and analyzing responses through the use of a scoring engine, developed by entering customized scoring criteria into a sales and marketing software.

33. The method of claim 32 in which the scoring engine outputs a risk score, a probability score and a buying code score, in which the risk score is reflective of an individual risk for developing type-2 diabetes, the probability score is reflective of a probability for developing type-2 diabetes and for subscribing to a treatment or prevention program, and the buying code score classifies an individual into a particular marketing category or segment for delivery of customized marketing material.

34. The method of claim 33 which further comprises determining a total risk based at least upon the risk score and laboratory testing results.

35. The method of claim 34 which further comprises incorporating demographic information into a client model to identify clients most likely to subscribe to a cash-based type-2 diabetes prevention program.

36. The method of claim 35 which further comprises obtaining a tiered ranking of individuals based upon one or more of the following: (i) scores from the scoring engine, (ii) laboratory testing results and (iii) demographic information, in which individuals in a top tier are most likely to convert, individuals in a middle tier are less likely to convert than individuals in the top tier, and individuals in a bottom tier are less likely to convert than the middle tier.

37. A method for providing preventive health care services cost effectively, comprising: providing a publically accessible system interface for receiving data from a subject that is in communication with a preventative health care services assessment system, the system configured for:

receiving data related to a subject that has accessed the publically accessible system interface;

determining a first score based on the data provided by the subject, the first risk score representing the subject's risk of developing diabetes;

identifying a subject at risk of developing diabetes if the first risk score exceeds a predetermined threshold;

if a subject is identified as at risk then a compliance score is determined using the received data;

identifying a subject as compliant when the compliance score exceeds a predetermined threshold; and

qualifying a subject for a pre-diabetes program that is both at risk and compliant.