JP2003522994A - Computer-implemented method and system for estimating costs and benefits associated with disease management - Google Patents

Abstract

  (57) [Summary] A computer-implemented method and system for estimating the costs and benefits incurred in managing a disease provides a graphical user interface that allows a user to identify a treatment or a population of treatment suggestions in a disease management intervention. Enter the group-specific data for. The costs and benefits associated with disease management are calculated for the population over a period of time based on data specified by the user. The calculated costs and benefits are output to the user to facilitate assessing the economic impact of the disease management intervention.

Description

Detailed Description of the Invention

[0001]

【Technical field】

The present invention relates generally to computer implemented methods and systems for estimating the costs and benefits of intervention. More specifically, the present invention provides a computer-implemented method and method that estimates direct medical and productivity costs and cost savings associated with disease management and facilitates the user's assessment of the economic impact of disease management. Regarding the system.

[0002]

[Related fields]

To determine whether to implement disease control interventions such as smoking cessation programs or influenza vaccination programs, employers or health plan providers should be able to accurately and efficiently assess the economic impact of such programs. It is desirable to have a method. For example, in deciding whether to implement smoking cessation programs for employees, employers may want to estimate (evaluate) the direct health care costs, productivity costs, and cost savings of such programs. . Similarly, health plan providers estimate (evaluate) the direct medical costs and cost savings associated with disease management.
I have something I want to do. Assessing the costs and benefits of disease management can involve complex calculations based on insurance statistical data. Due to the complexity of these calculations, employers and health plan providers are generally unable to make these calculations and accurately assess the costs and benefits associated with disease control.

Further, even when selling such a program or product generated by disease management to an employer or a health plan provider, it is desirable to have an efficient method of calculating costs and benefits associated with disease management. Sometimes. For example, a pharmaceutical manufacturer may want to sell a drug associated with disease control to an employer or health plan provider. As part of sales campaigns, manufacturers may want to provide cost benefit analysis for drugs to employers or health plan providers. This analysis may include using employer or health plan provider specific data to calculate the increase in intervention costs and benefits over a period of time. These calculations are complex and generally require an employment contract with an insurance statistics consulting firm.
Employment contracts with an insurance statistics consulting company are expensive and impractical, as it is necessary to employ this company to perform a cost benefit analysis for each employer or health plan provider.

[0004] In addition to the complex calculations involved in assessing a disease management program and the need to repeat the calculations for each employer or health plan provider, another problem was created by insurance statistics companies. That is, the conventional economic models presented may not show the underlying data (source data) for performing the calculations associated with these models. For example, if one model is based on insurance statistics data, the insurance statistics data may be held as the property of an insurance statistics consulting firm. As a result, the employer or health plan provider may not be able to assess the reliability of this calculation.

Accordingly, there is a long-term need for computer-implemented methods and systems for efficiently assessing the costs and benefits of disease management and facilitating the economic impact of user interventions for disease management. Is felt.

[0006]

DISCLOSURE OF THE INVENTION

The present invention is based on user-friendly graphics that allow employers or decision makers in a controlled care organization to quickly and easily consider and investigate the impact of disease management interventions on a given period of health and economic outcomes. Provide computer software.
In one implementation, the software determines cost savings for a cohort of employees or health planning members from the start of the model through a specified age, such as retirement age (eg, 65) or death. "Model" as used herein
The term refers to economic models such as mathematical algorithms and assumptions about the population used to assess disease management interventions. The model can be implemented as a set of computer-executable instructions, such as a program. The program can be embodied in a computer-readable medium such as a magnetic disk, optical disk or tape storage device. The program determines default values for personal and intervention characteristics, which the user can change.

The program provides a variety of outcomes, including the number of cases of the disease, medical costs, and non-medical costs incurred over time with and without health care coverage of the drug product that accompanies disease management. The results are presented graphically and numerically for ease of interpretation. The results can be reviewed or printed on a computer screen. According to an important aspect, the program presents the data source to the user and provides information about program terms and references for the data source.

The program also calculates cost savings data and presents it to the user. As used herein, the term "cost savings data" refers to data indicating the amount of money saved over time as a result of performing disease management interventions. The cost savings data can include direct medical costs saved and indirect costs saved.

Accordingly, it is an object of the present invention to provide a computer implemented method and system for assessing costs and benefits incurred in disease management that facilitates user assessment of the economic impact of disease management interventions. Is to provide.

It is another object of the present invention to provide a graphical user interface that allows a user to enter data specific to the employee or health planning population being processed in a disease management intervention.

Yet another object of the present invention is to provide a user with a computer interface for displaying source information generated in association with disease management.

[0012] Some of the objects of the invention have been set forth above, and other objects will become apparent as the description is made with respect to the accompanying drawings which are best described below.

[0013]

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a flow chart illustrating exemplary steps performed by a computer implemented system for assessing costs and benefits associated with disease management according to one embodiment of the invention. In step ST1, the user defines a model population of disease management interventions. The definition of the model population includes the number of employees and health plan members and the entry into the computer of characteristics associated with the employee or health plan members. These data can be entered using any suitable method, such as a keyboard, mouth, touch screen, microphone, or other suitable input device. In step ST2, the user defines the characteristics of the intervention. Typical properties that can be defined include the cost of the drug product and the level of counseling associated with the intervention. Step ST3 estimates cost and health outcomes for the population with and without intervention. This estimate is based on published studies and data from known populations extracted from insurance statistics databases. The calculations and assumptions involved in performing the estimate are described in further detail below.

In step ST4, the program presents an output on a computer display device showing the cost savings associated with disease management and the data source for the calculations performed by the program. The output is preferably displayed in a manner that highlights the costs and benefits associated with disease management. For example, graphs, tables, or any other type of data presentation format may be used to describe the costs and benefits associated with disease management. Since the user can enter population-specific data into the program, the program estimates cost savings for the population specified by the user. The data sources are visible and the program provides an effective and reusable method of assessing disease management interventions.

Each of the steps performed by this program is described in further detail below. Describe these steps for a smoking cessation program. However, the present invention is not limited to smoking cessation programs. For example, in FIG.
Costs for any disease management interventions such as influenza vaccination programs, migraine programs, heart programs, diabetes programs, asthma programs, rhinitis programs, seizure programs, and irritable bowel programs using the steps shown in You can evaluate the savings.

Population Characteristics As noted above, embodiments of the present invention may preferably define the characteristics of an employed (or health plan) population. These characteristics include the number of employees by age and gender, the number of employees in each of several job categories (eg clerical / management, labor, managers), industry type (eg manufacturing, professional work). Etc.), and geographical areas such as regions or states of the United States (eg, Northeast, Southeast, Midwest, West). Users preferably need to specify the size of their population, type of industry or health plan, and region of the United States. The default value is
It is preferably present in the input fields for the age, gender and job classification breakdown of the working population.

Based on the model's age and gender distribution, tissue type, and displayed geographical region, the model predicts the number of smokers in the working population. These calculations are
It is preferably based on the percentage of smoking found in the cohort grouped by age group, gender, and health plan / job classification. Default values for the number and proportion of smokers in each age / sex group may exist in the model, but the user can enter other values. The model provides default values for most inputs and thus can operate with minimal user input. However, all input values can be changed at the discretion of the user, preferably based on the amount of information available.

After defining a smoking cessation intervention model population, users can specify characteristics of the smoking cessation intervention, such as the percentage of smokers trying to quit, the success rate of the intervention, the level of encouragement to quit, and the cost of quitting smoking. it can. Default values can be included in the model, which can preferably be changed by the user.

Model Outputs: Health Outcomes and Smoking-Related Costs Based on the characteristics of the model cohort and discontinuation interventions, health outcomes for smoking-related costs and populations are estimated with and without smoking cessation aids. The model preferably correlates population characteristics with the incidence of smoking-related diseases over time in current smokers, former smokers and nonsmokers. The model preferably provides unique information about common smoking related diseases such as lung cancer, coronary heart disease, chronic obstructive pulmonary disease, cerebrovascular disease, and pregnancy complications such as low birth weight and miscarriage. . The rate of illness over time between smokers and non-smokers is preferably determined using epidemiologic data on disease incidence from published studies and official gazettes (government research reports). Outcomes of the model include the number of cases of each of these conditions over time, as well as the total number and changes (decrease) of ministerial ordinances when applying smoking cessation aids under health plans. Also given is the number of individuals who have successfully entered smoking cessation with and without smoking cessation aids. The user can view these health outcomes from the beginning of the workforce or health plan until a specified age, such as retirement age, which is the default value of 65, or until death.

The cost is shown as a result of the model. The costs for smoking cessation with and without smoking cessation aids, and the medical care costs for each of the general health care and smoking-related symptoms, and the changes in costs associated with the application of smoking cessation interventions were analyzed over time for the model population. Shown in. These costs can be derived from information in published studies or analysis of government databases. In addition, for the working population cohort, non-health costs are estimated over time for cohorts designed with and without smoking cessation aids. These non-healthcare costs are associated with reduced workplace productivity and increased absenteeism for those who are currently smokers compared to former smokers and nonsmokers. For non-working cohorts such as health plan members, estimates of non-health costs such as lost productivity can be omitted. The model preferably shows the cost from the start of the model to a specified age, such as retirement age (eg, 65 years) or 85 years, as a total cost or health care cost only. This model provides a benefit-cost ratio for the application of smoking cessation interventions and two compromising points: smoking cessation compared to direct medical cost savings alone or direct medical cost savings plus indirect cost savings. Decide on increased investment in intervention.

Graphical Interface As mentioned above, the model for assessing the cost savings associated with disease management, such as smoking cessation programs, is preferably implemented as a computer program.
Desirable functions performed by this program include providing an easy-to-use graphical interface, providing default values for most of the input data fields, and displaying the output in a format that delivers the costs and benefits of disease management to the user. To be FIG. 2 is a block diagram illustrating a typical hierarchy of screens or windows that may be displayed on a computer display device to allow a user to enter data and view output regarding disease management interventions. For example, each of the blocks shown in FIG. 2 is a WINDOWS® 95
, WINDOWS® 98, WINDOWS® 2000, or WINDOWS® NT can represent windows in a windowed computing environment. Alternatively, each of the blocks is a DOS-like Windows (
A screen in a computing environment based on something other than registered trademark can be represented. In the preferred embodiment, each of the blocks shown in FIG. 2 represents a window.

The program may include an initial window 200 to receive input of data and begin producing output. The initial window 200 allows the user to edit existing scenarios or create new ones. As used herein, the term "scenario" refers to a computer file containing user-specified parameters or default parameters associated with the population to be treated in a disease management intervention, and parameters used to model the intervention. .
If the user selects an existing scenario or wants to create a new scenario, the user must select the parameters window 202, the outcomes window 204, the cost-benefit analysis window 206, the break-even analysis window 208, and the data. The source window 210 can be accessed.

The parameter window 202 is adapted to accept user input relating to the population to be processed and to designing the model. The parameters window 202 can have a button or other graphical user interface for displaying the demographic window 212 of the population. The demographics window 212 for this population displays the model population's default age and gender distributions and is adapted to accept user input and change the default values.
Population demographics window 212 may also be adapted to accept user input regarding position and hourly salary for each position. The demographic parameters of the default population can be displayed, and the user can preferably change the default parameters.

As mentioned above, the parameter window 202 preferably allows the user to enter parameters for the model. For example, the parameters window 202 allows the user to select a cohort model or a model with an exchange. The parameters window 202 may also have buttons or other graphical user interfaces that allow the user to access other model parameter windows 214. An additional model parameter window 214 allows users to match and change values for additional absenteeism per day per year, reduced productivity levels, additional direct annual costs, and additional indirect annual costs. Can be made. Additional model parameter windows allow users to view and enter annual discount rates. You can also view and change your annual health care expenditures for those who are currently smoking, those who were previously smoking, and those who have never smoked.

The parameters window 202 may have buttons or other graphical user interfaces that allow the user to access the intervention parameters window 216. The intervention parameter window 216 allows customization of parameters of the disease management intervention, such as level of incentive, success rate, participation rate, and costs associated with the intervention. Other windows can display the default values as well, and the user can preferably change the default values based on the specific parameters of the intervention being performed. For example, one of the costs that the user can specify is the cost of the medication associated with the intervention. This cost can include average wholesale prices, partial burdens, dispensing fees, and so on. These costs can vary between one intervention and another. Therefore, it may be desirable for the user to be able to enter various values.

The intervention parameter window 216 is a smoking addiction rate window (recidivism-
The habitualization-rates window 218 can have a user interface with buttons or other graphics that the user can access. The smoking addiction rate window 218 is specific to smoking cessation programs. Smoking addiction rate window 218
View the annual smoking addiction rate schedule based on the number of years since you quit smoking. Smoking addiction rates are specified with and without the cessation aid health plan. Alternatively, the smoking addiction rate window 218 can be omitted in models used to evaluate other disease management interventions, such as influenza vaccination programs.

The parameters window 202 can have a button or other graphical user interface that allows the user to access the disease-specific cost window 220. The disease-specific cost window 220 displays the costs associated with smoking-related medical complications of the specified condition. For smoking, typical medical symptoms for which costs can be specified include chronic obstructive pulmonary disease, lung cancer, coronary heart disease, ischemic stroke, and pregnancy complications. The default value is preferably displayed and can be changed by the user.

The parameters window 202 can have a button or other graphical user interface to access the smoking status distribution window 222. The smoking status distribution window 222 is a smoking status, that is, a person who has previously smoked,
Those who are currently smokers or who have never smoked (in this specification, "never smokers (never smokers
rs) ”), and shows the population analysis by age group and sex. The default rate for the population is displayed and can be changed by the user.

The results window 204 allows the user to view cost savings data associated with disease management. Results are preferably presented in a graphical or tabular format for easy interpretation by the user. For example, the results window may have a button to access the cost savings graph window 224. The cost savings graph window 224 allows the user to view the cost savings achieved by the change in costs associated with applying smoking cessation adjuncts over a period of time since the implementation of the smoking cessation plan. For example, it may indicate the cost increase due to discontinuation or the amount of dollars consumed when applying smoking cessation aids. It can also indicate health care savings that represent direct medical costs prevented by smoking cessation. Health care and indirect savings can be demonstrated for employee groups. These savings represent direct medical costs that were prevented from occurring and productivity losses and absentee rates that were prevented from occurring by quitting smoking. For health planning populations, the total indirect savings may not be shown, ie only the total health savings.

Another window that can be accessed from the achievements window 204 is:
It is the disease relief graph window 226. The disease relief graph window 226 allows the user to view the cost savings due to smoking-related medical symptoms prevented by the application of smoking cessation aids. This cost savings is preferably demonstrated at regular intervals since the implementation of disease management interventions. For smoking cessation programs, the cost savings are the amount of money saved as a result of reduced coronary heart disease, cerebrovascular disease represented by ischemic stroke, chronic obstructive pulmonary disease, lung cancer, miscarriage and low birth weight. Is mentioned.

Another window that can be accessed via the outcomes window 204 for purposes of viewing the cost savings associated with disease management is the outcomes table window 228. For smoking cessation programs, Outcomes window 228 presents the number of cases of the above symptoms avoided as a result of smoking cessation for the population. For example, the number of coronary heart disease cases prevented from occurring can be shown at 2, 5, 10 and 20 years. Similar output can be displayed for other symptoms.

The cost-benefit analysis window 206 displays costs, benefits, profit-cost ratios, cost increments, and internal rates of return for disease management interventions. Costs include the amount of health plan coverage for drugs such as smoking cessation aids that are associated with population illness management. The profit-cost ratio represents the saved monetary increase for each monetary unit consumed in the disease management intervention. Internal rate of return is defined as the discount rate at which the net present value in cash outflows and inflows is zero. For smoking cessation programs, the internal rate of return is an exactly equal discount rate where increased smoking cessation increased savings from direct costs or indirect costs plus direct costs.

The profit and loss analysis window 208 displays, in graphical and tabular format, the cost savings associated with disease management. For example, the profit / loss analysis window 208 may include a profit / loss graph 230.
May have a first button for displaying. Profit and loss graph 230 displays marginal balance point costs incurred in disease management and cost savings incurred in disease management. The point at which the cost reduction exceeds the marginal balance point cost is the profit and loss point. For the employer planning scenario, cost savings can include direct and indirect cost savings. For health planning scenarios, only direct cost savings can be offered.

The profit / loss analysis window 208 preferably also includes a button for displaying the profit / loss table 232. The profit and loss table 232 displays the result of the profit and loss analysis such as the time when the smoking cessation cost saving exceeds the quitting cost. Like the profit and loss graph, the profit and loss table preferably displays direct and indirect cost savings for the employer scenario and direct cost savings only for the health planning scenario.

To provide confidence in the calculations and outcomes presented by the model, the initial window 200 preferably includes a button or other interface for displaying the data source window 210. Data source window 210 displays parameter type and source citations, such as studies and published reports used to provide values for the parameter type.

Each of the windows described with respect to FIG. 2 will be described in more detail. In the following description, each of the windows comprising a graphical user interface will be described in terms of buttons and input cells. Although the drawings do not show a textual label for each button or input cell, it is understood that each button or input cell may include a textual label corresponding to its functionality described herein. It For example, for FIG. 3, the initial window 20
0 is a parameter button 300, ZYBAN (registered trademark) achievement button 304, profit / loss analysis button
306, and a data source button 308 can be included. Each of these buttons has a “Parameter”, “ZYBAN® Outcome”, “Profit / Cost Analysis”
Can include literal labels such as, "Profit and Loss Analysis", and "Data Source". The respective buttons 300, 302, 304, 306, and 308 allow the user to select the parameters window 202, the results window 204, the cost benefit analysis window 20.
6. Gain / loss analysis window 208 and data source window 210 can be accessed. Buttons 300-308 cannot be activated from the initial window 200 because the user has not yet selected a scenario.

Regarding the ZYBAN® Outcome button 302, ZYBAN® is the product name for the drug (bupropione hydrochloride) used in smoking cessation programs. The model described herein estimates cost savings for smoking cessation programs based on the cost of providing ZYBAN® as a protected health benefit. However, the present invention is not limited to cost savings assessments based on ZYBAN®. For example, other smoking cessation products could be used instead of smoking cessation programs to assess cost savings. For other types of disease management interventions, outcomes for the drug or any other context associated with the intervention may be presented. For example, for an influenza vaccination program, the program can be evaluated based on the costs and benefits of the vaccination program.

Initial window 200 also includes toolbars such as scenario tool 312, mode tool 314, and reset tool 316. The scenario tool 312 allows the user to access the drop down window to create new scenarios or access existing scenarios. Additional features included in the scenario drop-down menu include delete, edit, name, import, export, and exit. You can use the delete function to permanently remove a scenario from your program. The edit scenario function allows the user to edit the current scenario. The export command allows the user to save the scenario under a user-specified name and location.
The import command allows the user to open the exported file containing the scenario. The exit command terminates the program.

The mode tool 314 on the toolbar allows the user to run the program in edit or demo mode. The edit mode allows the user to collate and specify parameters that occur with disease management. You will not be able to access the demo mode until the parameters have been saved in the parameter window using the default edit mode.

The reset button 316 on the toolbar resets the value entered by the user at any point in the program to the default value. The reset button 316 preferably allows the user to select whether to reset all or only some of the parameters. For example, the reset tool 316 may access a drop down menu to allow the user to select the type of parameter to restore to the default value. For smoking cessation programs, parameter types include disease-specific costs, additional model parameters, smoking status distributions, intervention parameters, or population characteristics.

FIG. 4 shows the parameter window 202. Parameter window 2
02 can be displayed when the user selects from the initial window 200 for the purpose of creating a new scenario or editing an existing scenario using the scenario tool 312. When the program displays the parameter window 202, button 3
00-308 and tools 314 and 316 are operational. Buttons 300-308 and 312-3
16 is preferably visible in each of the program windows 200-210, allowing the user to access higher level program functions from any window at the same hierarchical level. In the illustrated embodiment, the parameter window 202 contains input cells that can be collated and specified for population and model related parameters. Each input cell can contain default parameters. Some of the input cells can have pull-down lists of alternative selections. For example, one type of tissue input cell 400 allows the user to select the type of tissue in which to perform disease management interventions. Tissue input cell types include pull-down lists of industry and health plan types. The user preferably selects the type that most closely matches the type of tissue of interest. An example of parameters that the user can enter in cell 400 is `` Mining (mini
ng) ”, indicating a mining organization. The area input cell 402 allows the user to select the geographical area in which the desired organization is located. For the United States, geographic areas provided to users include the Northeast, Midwest, South and West. The state input cell 404 allows the user to match and specify the state performing the intervention. This input cell is optional and can be omitted.

The present invention is not limited to assessing the costs and benefits incurred in managing disease in the United States. For example, additional input cells may be included to select countries with disease management interventions. If you specify a country, you can select default parameter values for the country.

The employee population input cell 406 allows the user to enter the number of employees or health plan members in the population undergoing disease management interventions. The subgroup input cell 408 allows the user to match and specify the number of dependents associated with each member in the group. The total group input cell 410 includes the sum of the values input to the employee group input cell 406 and the dependent group input cell 408. This value is preferably calculated automatically by the program.

The population demographics button 412 allows the user to access the population demographics window 212. FIG. 5A shows a population demographics window 212. 5A, the population demographics window 212 includes an age / gender folder tab 500 and an occupation folder tab 502. Age / Gender folder tab 50
The 0 contains multiple input cells 504 that allow the user to match and modify population demographics by age and gender. Table 1 below shows typical values that can be entered in the input cell 504.

[0045]

[Table 1]

The values in Table 1 that the user can specify via input cell 504 are the number of employees in each age group. The age group ranges and table titles are some of the distribution items, etc., and can be displayed to the user via the Employee Demographics window 212. The "total" in the bottom row of Table 1 can be calculated and displayed by the program. Reset button 506 restores the value of the input cell to the default value for any selected input cell in window 212. The average family size input cell 508 displays the average size of the group family. A default value of 2.60 can be provided for the average family size. This value can be changed by the user to suit the population to be processed. The annual turnover rate input cell 510 allows the user to specify against the average number of members or employees in the group all year round.

FIG. 5B illustrates the Occupation tab 502 of the Population Demographics window 212.
The occupation tab 502 includes a table 507 that displays job types, a percentage of employees in each job type, and a table 507 that displays average hourly wages associated with each job type. Table 2, shown below, is an example of a table 507 that may be displayed when a user selects the occupation tab 502.

[0048]

[Table 2]

The values in Table 2 can be changed to reflect the values of the target company. Each of these values can be restored to their default values by clicking the reset button 506. Once the user has selected all of these values, the user can click "OK" or "Cancel" to return to the parameter window 202 illustrated in FIG. Clicking "OK" saves the changes made by the user, and clicking "Cancel" does not save the changes made by the user. Occupation tab 502 can be omitted for health planning scenarios.

Referring again to FIG. 4, the parameter window 202 contains input cells that allow the user to collate and specify model characteristics. For example, the model mode input cell 414 allows the user to select a model with exchanges or an age group model. As used herein, the cohort model includes personnel who are part of the starting model population and new individuals do not enter the model because members of the starting population leave or die. In the model with exchanges, new individuals can enter the population and replace members who leave or die. In a typical model, the surrogate individual has the same age and sex as determined by the original model population. The surrogate individual may or may not have the same smoking status as determined by the original model population.

The additional model parameters button 416 allows the user to access the additional model parameters window 214. FIG. 6 illustrates an exemplary embodiment of the additional model parameter window 214. The additional model parameter window 214 allows the user to change values for increased absenteeism per year, reduced productivity levels, additional annual direct costs, and additional annual indirect costs. For example, for additional absenteeism per year, an additional model parameter window 214 allows the user to enter the absenteeism per day per year for those who currently smoke and previously smoked. Input cells that can
Including 600. Input cell 602 allows the user to match the default decrease in productivity values and specify the decrease in productivity values. Input cells 604 and 606 allow the user to match and specify additional annual direct and indirect costs associated with male and female smokers and non-smokers. These additional costs are employer or health plan specific and can include any costs not already included in the model. Input cell 608 allows the user to select an annual discount rate and present the cost and benefits of a smoking cessation program using current monetary value.

The medical expenditure allocation input block 610 allows a user to match and specify medical expenditure allocations by family, gender or smoker status. A suitable textual label
It can be selected by clicking on the circle adjacent to (textual label) (not shown).

Average annual medical expenditure table 612 is displayed in window 214, such as average annual medical expenditure input cell 614, which allows the user to enter average annual medical expenditure by smoker type for the patient population. Table 3 below is an example of an average annual medical expenditure table 612.

[0054]

[Table 3]

In Table 3, average annual medical expenditures are calculated for those who are currently smoking, those who were previously smoking, and those who have never smoked. Spending for each type is calculated for men and women.

By default, the model calculates the average annual medical expenditure for those who previously smoked using a formula based on a weighted average of years since quitting smoking. Alternatively,
The user can specify a single yearly value for a previously smoker by selecting input cell 616 and entering a value in input cell 614. However, this option is less accurate than the formula because the medical costs of previously smokers are not constant. Rather, these values decrease as the number of years since discontinuation increases. Once the user has completed the additional model parameters window 214, the user can select “OK” or “Cancel” to return to the parameters window 202. Clicking "OK" saves the changes made by the user, and clicking "Cancel" does not save the changes made by the user.

Referring again to FIG. 4, the parameter window 202 contains the levels of the counseling input cell 418 from which the appropriate level of counseling that occurs with disease management can be selected. Typical values that can be selected include "high", "low" and "medium" indicating the level of counseling for the program. The intervention parameters button 420 allows the user to match and change parameters associated with the selected level of counseling via the intervention parameters window 216. FIG. 7 illustrates an exemplary embodiment of an intervention parameter window 216 associated with high level counseling. The first group of input cells allows the user to match and specify parameters related to the level of promotion. For example, cost level input cell 700 allows a user to select a cost associated with a promotion of a smoking cessation program. The second input cell 702 allows the user to select whether ZYBAN® is promoted as part of a smoking cessation program. The third input cell 704 allows the user to collate and specify physician visit rates for smoking cessation only. For example, some members may receive counseling regarding disease management interventions during routine physician visits, such as annual physical examinations. The value entered in the input cell 704 indicates the number of visits to the doctor for the purpose of the disease management intervention program only. The input cell 706 allows the user to collate and specify the number of hours away from work due to a doctor's visit.

Table 708 allows a user to match and specify intervention costs and success rates associated with selected intervention products. Table 4 below shows the intervention parameter window.
9 illustrates an example of an intervention cost and success rate table 708 that may be displayed at 216.

[0059]

[Table 4]

In Table 4, the column entitled “Success Rate” shows the success rate for ZYBAN® only, ZYBAN® with nicotine patch, other smoking cessation aids, and no smoking cessation aids. Corresponds to 710, 712, 714 and 715 that can be collated and designated. The column titled "Costs other than ZYBAN (registered trademark)" is an input cell that allows you to collate and specify costs other than ZYBAN (registered trademark) related to each product.
Equivalent to 16, 718, 720 and 722. Non-ZYBAN® costs can include counseling costs, promotional costs, and other costs for each type of smoking cessation aid. The column entitled "Total Cost Per Quit Test" contains the calculated total cost per quit test associated with each of the smoking cessation adjuncts listed in Table 708.

Another table 723 allows users to match and specify ZYBAN® costs. Table 5 below illustrates an example of a ZYBAN® cost table 708 that can be displayed in the intervention parameters window 216.

[0062]

[Table 5]

The input cell 724 of FIG. 7 corresponds to the column labeled “AWP” in Table 5, and the user is ZYBA
The average wholesale price of N (registered trademark) can be collated and specified. Input cell 725 of FIG.
Corresponds to the column labeled “weeks” in Table 5, and the user can check and specify the number of weeks of treatment with ZYBAN (registered trademark). The input cell 726 of FIG.
Corresponding to the column labeled "Fixed out-of-pocket", the user can collate and specify the fixed out-of-pocket paid by members of the group for each ZYBAN® prescription. The input cell 728 in FIG. 7 corresponds to the column labeled “Number of prescriptions” in Table 5, and the user can check and specify the number of prescriptions. The input cell 730 in FIG. 7 corresponds to the column labeled “Discount” in Table 5 and allows the user to collate and specify the discount for each prescription. The input cell 732 of FIG. 7 corresponds to the column labeled “Dispensing fee” in Table 5, and the user can collate and specify the dispensing fee related to the prescription. The calculation cell 734 in FIG. 7 corresponds to the column labeled "Total" in Table 5 and shows the total cost per employee for the number of weeks selected in the input cell labeled "Number of Weeks".

Yet another table 735 includes input cells that allow the user to collate and specify the share percentages with and without ZYBAN® applied. Table 6 below is an example of a distribution rate table 735 that may be displayed in the intervention parameter window 216.

[0065]

[Table 6]

Input cell 736 of FIG. 7 corresponds to the column labeled “Not Applicable” in Table 6,
Allows users to collate and specify the percentage of the population participating in disease management interventions without smoking cessation aids. The input cell 738 in FIG. 7 corresponds to the column labeled “Applicable” in Table 6, and the user can collate and specify the proportion of the population participating in the disease control intervention to which the smoking cessation aid is applied. . In Table 6, the percentage of populations participating in disease management interventions with and without smoking cessation aids was measured by smoking cessation programs or
Equal as there is no ZYBAN® promotion.

Yet another table 739 allows users to look up success rates and costs associated with disease control with and without smoking cessation aids. Table 7 below
Is an example of a summary of the success rate and cost table 739 that may be displayed in the intervention parameters window 216.

[0068]

[Table 7]

The column labeled “Success Rate” in Table 7 corresponds to cells 740 and 741 in FIG. 7 and allows the user to match the success rates for programs with and without smoking cessation aids. The column labeled "Cost per smoking cessation test" in Table 7 corresponds to cells 742, 743, 744, and 746 in Figure 7, where users are employed per smoking cessation test with and without smoking cessation aids applied. Per capita application and total cost can be matched.

The intervention parameter window 216 allows the user to specify the smoking addiction rate window (reci
divism rates window) 218 You can access the `` Differentiated smoking addiction rate (view recidivis
m rate) "button 748 can also be included. FIG. 8 illustrates an exemplary embodiment of a smoking addiction rate window. The smoking addiction rate window 218 allows the user to collate and specify the percentage of group members who quit smoking and then start smoking again. In FIG. 8, as the smoking addiction rate window 218, the input cells 800 and 802 in which the user can specify the annual smoking addiction rate by collating or can use the list of smoking addiction rates for a specified number of years since the discontinuation are used. Including. When the user selects "Use list of smoking addiction rates," the annual smoking addiction rate list displays the percentage of smokers who return to smoking based on a period of time since the discontinuation. Table 8 below illustrates an example of a list of annual smoking addiction rates that can be displayed in window 218.

[0071]

[Table 8]

The first column of Table 8 contains values without the application of smoking cessation aids. This column corresponds to the input cell 804 shown in FIG. The second column of Table 8 contains values for the application of smoking cessation aids. This column corresponds to the input cell 806 shown in FIG. Once the user has selected or matched the smoking addiction rate displayed in the smoking addiction rate window 218, the user can return to the intervention parameters window 216 by activating the "OK" or "Cancel" button. After matching or specifying all parameters in the intervention parameters window 216, the user can click "OK" or "Cancel" to return to the parameters window 202.
Clicking "OK" saves the changes made by the user, and clicking "Cancel" does not save the changes made by the user.

Referring again to the parameter window 202 shown in FIG. 4, the intervention availability input cell 422 allows the user to determine whether disease management interventions are available continuously or for a specified period of time. Can be specified. For example, the intervention provides a one-time application of smoking cessation aids or a continuous application of smoking cessation aids. In each case, the costs and outcomes of applying the smoking cessation aid will be compared to the costs and outcomes of applying the smoking cessation aid for the model.

The disease-specific cost button 424 allows the user to access the disease-specific cost window 220. FIG. 9 illustrates an exemplary embodiment of a disease-specific cost window 220. In Figure 9, the disease-specific cost window 220
Includes an input cell that allows the user to display, match and specify the average total and annual cost of treatment for the various conditions associated with the disorder being treated or multiple disorders. For example, input cell 900 includes the annual cost of treating chronic obstructive pulmonary disease. Similarly, input cell 902 displays the lifetime cost of treating lung cancer in a group of individuals. For coronary heart disease, the input cell 904 displays age- and gender-specific costs. Table 9 below shows typical disease-specific costs for coronary heart disease that can be displayed in window 220.

[0075]

[Table 9]

Input cells 904 correspond to the values in Table 9 for each age group / sex. For example, the program preferably displays a default value in the input cell. These values can be determined based on insurance statistical data and are preferably user changeable. For cerebrovascular disease represented by ischemic stroke, input cell 906 displays age and gender specific costs. Table 10 below shows typical disease-specific costs for cerebrovascular disease that can be displayed in window 220.

[0077]

[Table 10]

Input cell 908 displays the costs specific to the manifestation of adverse pregnancy outcome symptoms. The input cell 906 corresponds to the value in Table 10 for each age group / sex. For example, the program preferably displays a default value in the input cell. These values can be determined based on insurance statistical data and are preferably user changeable. Table 11 below shows typical costs for pregnancy complications.

[0079]

[Table 11]

The values in Table 11 are default values for each specific complication and can be determined based on insurance statistics data.

Once the user has completed matching or changing the parameters shown in FIG.
The user selects "OK" or "Cancel" to open the parameter window
Return to 202. Clicking "OK" saves the changes made by the user, and clicking "Cancel" does not save the changes made by the user.

Referring again to FIG. 4, the parameters window 202 can include a smoking characteristics button 426 that allows a user to access the smoking status distribution window 222. FIG. 10 illustrates an exemplary embodiment of the smoking status distribution window 222. In FIG. 10, the smoking status distribution window displays a plurality of values that represent smoking status, that is, a grouping by age group and gender by those who are currently smoking, who have smoked previously, or who have never smoked. Contains 1000 input cells. This distribution applies to the total model population, such as employees or members and adult dependents.
A default value is specified and the user can change the default value by accessing the appropriate cell. Table 12 below shows typical smoking status distribution values that can be displayed in window 222.

[0083]

[Table 12] Once the user has completed matching or changing the values in the smoking status distribution window 222, the user can click "OK" or "Cancel" to return to the parameters window 202. Clicking "OK" saves the changes made by the user, and clicking "Cancel" does not save the changes made by the user.

Referring again to FIG. 4, once the user has completed inputting all desired parameters for the population, the user selects one of the buttons 302, 304 and 306 to trigger the disease management. It is possible to collate the cost saving data. For example, in order to compare the outcomes that occur with disease management, the user may
Can be selected. When the achievement button 302 is selected, the achievement window 204 is displayed by the program. 11 (A) to 11 (C) show the result window 2
4 shows an exemplary embodiment of 04. In FIG. 11A, the cost saving graph window 22
4 is displayed. The cost savings graph window 224 illustrates health care costs and health care savings over a given period of time associated with disease management. For example, in FIG.
In (A), costs and savings are displayed at 2, 5, 10 and 20 year intervals. In FIG. 11A, shading 1103 represents a 2-year model, 1104 represents a 5-year model, 1106 represents a 10-year model, and 1108 represents a 20-year model. Bar 1110 represents an increase in discontinuation costs, bar 1112 represents health care savings, and bar 1114 represents health care and indirect savings. Health care savings include medical costs avoided by quitting smoking. Health care and indirect savings include direct medical costs avoided by quitting smoking, lost productivity lost, and absenteeism avoided. The amount of health care savings is in two model scenarios (with and without smoking cessation aids)
It can be calculated by the difference in health care costs between those who previously smoked and those who currently smoke. The value on the vertical axis in FIG. 11A is the US dollar. FIG. 11 (A)
Then, the "check cost saving graph" button 1100 is activated, and the display of the cost saving graph is selected. To access the disease relief graph, the user can select the "Check disease relief graph" button 1101.

FIG. 11B shows an exemplary embodiment of the disease relief graph window 226. In FIG. 11B, the horizontal axis represents various diseases related to smoking. For example, bar 1116 represents coronary artery disease, bar 1118 represents cerebrovascular disease, bar 1120.
Represents COPD, bar 1122 represents lung cancer, bar 1124 represents miscarriage, and bar 1126 represents low birth weight.

The vertical axis represents the number of dollars saved associated with each disease. FIG. 11 (A)
Similarly, shadings 1103, 1104, 1106, and 1108 are 2, 5,
Represents data for 10 and 20 year models.

The user selects the button 1102 for collating the result table to display the result table window 228.
Can be matched. FIG. 11C shows an exemplary embodiment of the results table window 228. Table 13 below shows a typical outcome table that can be displayed in window 228.

[0088]

[Table 13]

The data presented in Table 13 allow the user to reconcile the improved outcomes achieved with value smoking cessation aids. In the table, outcomes are expressed as the number of cases avoided. For example, the entry in column 1, column 1 is that 3.068 times as many group members discontinue smoking without ZYBAN® as a result of using ZYBAN® as a result. Is shown. Similar data are presented for smoking-related illnesses and the number of deaths postponed by ZYBAN®.

Additional cost savings that can be matched for disease management interventions include cost benefit analysis. The cost benefit analysis can be accessed by the cost benefit analysis button 304. For example, when the user selects the cost / profit analysis button 304, the cost / profit analysis window 206 is displayed. FIG. 12 illustrates an exemplary embodiment of cost benefit analysis window 206. Tables 14, 15 and 16 show typical profit-cost data that can be displayed in window 206.

[0091]

[Table 14]

[0092]

[Table 15]

[0093]

[Table 16]

As shown in Tables 14-16, the cost-benefit analysis window 206 shows per-employee per employee or per-member for expenses, benefit-cost ratios, health plans with and without the application of smoking cessation aids. Show monthly cost increase and internal rate of return. For example, in the title "Cost without the application of smoking cessation aids", $
57,746,150 shows total health care costs for the population without the application of smoking cessation aids. This number is based on a population of 1000 employees with 600 dependents and other parameters entered in Figures 4-10. In "Cost of applying smoking cessation supplement", $ 57,661,119 indicates the total health care cost of applying smoking cessation supplement. In the title "Savings by applying smoking cessation aids",
The $ 85,031 number represents the difference in total health care costs with and without the smoking cessation aid. The profit-cost ratio of 2.76 indicates that $ 2.76 is saved in health care costs for the additional dollars spent to quit smoking. The additional data that can be displayed in window 206 is the increased cost of drug application per employee per year. For the health planning model, the monthly cost increase per member is determined for the first year and incorporates the number of beneficiaries per member as specified in the box characteristic of the population on the parameter screen. The internal rate of return is calculated over 20 years. Similar calculations are made for health care plus overhead costs. Another way in which the cost savings associated with disease management can be demonstrated is through break-even analysis. To display the results of the profit and loss analysis, the user can select the profit and loss analysis button 306. The profit / loss analysis button 306 displays the profit / loss analysis window 208.
13 (A) and 13 (B) illustrate an exemplary embodiment of the P & L analysis window 208. For example, FIG. 13A illustrates the P & L analysis graph window 230 that is displayed when the user selects the "match P & L graph" button 1300. FIG. 13B illustrates the profit and loss table window 232 that is displayed when the “match profit and loss table” button 1302 is selected. Referring to FIG. 13 (A), the profit and loss graph illustrates the number of years until the cost saving exceeds the smoking cessation cost.
The horizontal axis represents time in years. The vertical axis represents costs in dollars. Line 1303 represents smoking cessation costs, averaged over 20 years. Line 1304 represents the savings in health care costs when applying smoking cessation aids. Line 1305 represents the cost savings of health care and indirect costs when applying smoking cessation aids. The intersection of lines 1303 and 1304 shows that the gains and losses on health care cost savings occur in about seven years. The intersection of lines 1303 and 1305 shows that the profit and loss points for health care and indirect cost savings occur in about three years. Specific data points can be viewed by selecting the P & L button 1302.

FIG. 13B illustrates an exemplary embodiment of the P & L table 232.
An example of a profit and loss analysis table that can be displayed in the window 232 is shown below.

[0096]

[Table 17]

In Table 17, each column shows only model years, annual discontinuation costs when applying smoking cessation supplements, cumulative increase in discontinuation costs when applying smoking cessation supplements, cumulative health care savings, and health care costs, respectively. It shows the return on investment, cumulative health care + indirect cost savings, and return on investment for health care + indirect costs. As shown in the table, a positive return on investment for health care costs only occurs around year 7. Positive return on investment for health care and overhead costs occur between 3 and 4 years.

According to an important aspect of the invention, this program preferably allows the user to match the data sources associated with the model. To access the data source window 210, the user selects the data source button 308. FIG. 14 illustrates an exemplary embodiment of the data source window 210. In the illustrated embodiment, the parameter category input cell 1400 allows the user to
The user can select the category of parameters for which he wants to see the source information. The category selected by the user controls the display of the data source from which the parameters and default values for the parameters are derived. Area 1404 displays a table containing a list of parameters and a source of default values for each parameter. Table 18 below is an example of a data source table that can be displayed in area 1402.

[0099]

[Table 18]

Area 1404 contains references to the parameter categories selected using area 1400. For example, if disease cost is selected as the parameter category, low birth weight cost is one of the parameters in the disease cost category. The full reference from which default values for low birth weight parameters are derived is Area 14
Displayed on 04. An example of the cited document that can be displayed in the area 1404 is as follows. Halpern et al., 1996: Halpern et al., Cost-Efficacy (Cost-Ef) of premature counseling in the United States.
fectiveness of Preconception Counseling in the United States). Report to the Centers for Disease Control and Prevention, 1996. Therefore, the user can access the source from which the default parameter values were derived, so that the user can evaluate the reliability of the model calculation.

According to another aspect of the invention, the parameter window 210 allows the user to select and view definitions associated with the model. For example, the parameter category input cell 1400 can include definition options. If the user selects the definition option, the definition for the model parameter can be displayed in the position occupied by the cited reference in the illustrated embodiment. The user can access and view the definitions, making it easier to interpret the model outcomes.

Model Inputs Table 19 below shows typical model inputs and data sources that can be used to estimate costs and benefits for smoking cessation programs. Typical model input parameters are shown in the left column of the table. The right column of the table shows typical sources of default values for model parameters. `` Glaxo Wellcome Data
The source of "" was derived from the data collected by Glaxo Wellcome, Inc. The source representing "Towers Prrin Data" was derived from the data collected by Towers Perrin. Full references for each of the sources listed in the right column are included in the references section below.

[0103]

[Table 19]

Table 20 below shows additional model inputs by category. The left column shows the types of parameters. In the middle column, the inputs associated with each parameter type show the balance. The right column shows typical sources of default values for each parameter. Full references for each of the remaining sources of default parameters are provided in the references section below.

[0105]

[Table 20]

Assumptions Assumptions about model input parameters obtained from government databases will be described in more detail.

Population Distribution by Age, Gender, and Smoking Status Information on this parameter can be found in the 1994 National Health Interview Surve
y Extracted from the database. These data reflect each of the four districts in the country and the specific groups / categories for the type of industry / health program.

Population Distribution by Occupation This information comes from the 1997 Current Population Survey and is unique to each of the four districts in the country and to the type of industry.

Adult Dependents This model assumes that there are 0.6 adult dependents for each employee / health plan member (based on values supplied by Towers Perrin). Adult dependents assume that they are the same age and gender as the employee / health plan member. The smoking status distribution for adult dependents is the same as that specified for smoking status in the current population for employees / health planning members of the specified age group and gender. Adult dependents bear no overhead costs and can change smoking status independently of the employee / health plan member.

Average hourly wages by occupation This information comes from the 1997 Current Population Survey and is unique for each of the four districts or states (if indicated) of this country.

For models with a workforce or health plan turnover exchange, the turnover rate has a default value of 10% per year, which incorporates terminations and retirements. For the cohort model, a table of turnover rates with years of continuous employment is used. Values for both the model with exchange and the cohort model were supplied by Towers Perrin. The following is a list of turnover rates for the cohort model.

[0112]

[Table 21]

In the model with exchanges, the program assumes that an employee / health plan member who left the job before age 65 will be replaced by a new employee / health plan member of the same age and gender. . The smoking status of replacement employees / health plan members is determined based on their distribution from the original model population (ie age, gender,
Occupation mix, and smoking status distribution in general population based on region of the country), not based on current distribution of workforce or health plan when employee / health plan member leaves . When an employee / health plan member leaves a job, a proportional number of adult dependents also leave (based on the ratio of employee / health plan member to adult dependent). Adult Dependents who are out
They are the same age as the health plan members, but opposite in gender. The smoking status of deviant adult dependents is generally based on the ratio of non-smoking / previously smoked / currently smoked in the working population / health planning for this gender and age group. Employees who die before the age of 65 are replaced in the same way.

Probability of using smoking cessation aids Based on Towers Perrin data, 14% of smokers attempting to quit smoking ZYBAN® (alone) when smoking cessation aids were applied by employers or health plans. Or with a nicotine patch) and the remaining 86% with or without other smoking cessation aids (eg nicotine replacement therapy alone). If smoking cessation aids are applied and ZYBAN ® is encouraged, 17% use ZYBAN ® and the remaining 83% use other smoking cessation aids or use auxiliaries. Not in. If no smoking aids are applied, only 7% of smokers attempting to quit use ZYBAN (R) and the remaining 93% use other smoking cessation aids or no smoking cessation aids .

Of smokers using ZYBAN® (with or without application), 97% use only ZYBAN® and 3% use ZYBAN® and nicotine patch . Among smokers not using ZYBAN® (also applied or not), 30% use other auxiliaries and 70% do not use smoking cessation aids.

Efficacy of smoking cessation interventions depending on the level of counseling The discontinuation rate for ZYBAN® alone and ZYBAN® and nicotine patches was based on data from a Glaxo Wellcome clinical trial with short counseling for 5 minutes. Was there. Efficacy of other smoking cessation aids was based on the proportion of Tonnsen et al. The efficiency of the option without smoking cessation aid was based on Cromwell et al.
The change in efficacy of each of these smoking cessation adjuncts with low or no / minimal levels of counseling was evaluated using the data of Cromwell et al. (1997). For example, Cromwell et al., Comparing sufficient discontinuation with nicotine patch therapy with short-term counseling (equivalent to low-level counseling in the model) with sufficient counseling (equivalent to high-level counseling in the model), The effectiveness is reported to be reduced by 7.6%. Thus, this model goes from full counseling to low-level counseling clinical trial data, with efficacy on ZYBAN® alone, ZYBAN® and nicotine patches, or other smoking cessation aids. It is assumed to decrease by 6%. Similarly, well-counseled nicotine patch therapy and minimal counseling (
(Equivalent to the level without counseling in the model)
Cromwell et al. Report that the efficacy of discontinuation is reduced by 9.3%. For smokers attempting to quit without smoking aids, Cromwell et al. Found that the success rate decreased by 4.34% from adequate counseling to short counseling (no adjuvant), It is reported that there is a decrease of 6.2% when moving from normal counseling to no counseling (no auxiliary agent).

Cost of smoking cessation interventions according to level of counseling It was hypothesized that individuals using any smoking cessation aid to quit smoking would have at least minimal counseling associated with the smoking cessation aid. Thus, the costs for discontinuation with smoking cessation aid (s) in the non-counseling model include the costs reported by Cromwell et al. (1997) for minimal counseling. The costs associated with low-level counseling models include:
Cromwell et al. Costs for short-term counseling and model costs for high-level counseling include Cromwell et al.'S costs for sufficient counseling. Smokers in the non-smoking model do not have the cost of discontinuation in the absence of counseling and the short and full counseling costs for low and high levels, respectively.

Physician Costs for Smoking Cessation with ZYBAN® 9% of ZYBAN® prescriptions for individuals seeking to quit with ZYBAN® when a smoking cessation aid is applied Get a doctor's visit to get the treatment. Another 91% of individuals who are trying to quit using ZYBAN® are prescribed at the doctor's visit, primarily for other reasons. This 9%
The doctor's cost for and the time to go to the doctor (4 hours) are included in the cost of the discontinuation study applied. The 9% and 4 hour values were supplied by Towers Perrin.

Length of treatment and dose for ZYBAN® The efficacy rate for ZYBAN® was determined by administering ZYBAN® 150 mg twice daily for 9 weeks in the clinical trials utilized for this model. Based on the patient. However, pricing was based on 7 weeks of treatment.

ZYBAN® Pricing ZYBAN® starting values are based on the average wholesale price (AWP) for 7 weeks of treatment. In this model, pharmacies charge 13% of AWP and a dispensing fee of $ 2.50
It is supposed to be repaid by the payer. Smokers who are trying to quit with ZYBAN® (alone or with a nicotine patch) receive two drugs with a partial contribution of $ 8.00 each.

In this model, pharmacies offer a 10% discount and AWP for Medicaid plans.
It is assumed that a rebate of 12.3% from 1 to 3 will be repaid by the third party payer. There are two prescription charges of $ 1.00 each and two dispensing charges of $ 4.00 each. Next, a federal match (federal) of 50% of the total cost
match), resulting in a condition involving the remaining 50%.

Smoking Cessation Promotion Employers and health plans applying smoking cessation aids may optionally elect to promote cessation. Neither promotion incurs additional costs,
The percentage of those who are trying to quit smoking is 34%, which is equal to the participation rate without smoking cessation aid. The low promotion costs $ 200, and the increase in the rate of trying to discontinue is calculated by multiplying the rate without promotion by 1.5% and adding the product to the rate without promotion. Therefore, the percentage of people who quit smoking with low promotion is 34.5%. Mid-promotion costs $ 500, and an increase in the rate of attempting to discontinue is obtained by multiplying the non-participation rate by 3% and adding this product to the non-promotion rate. Compared to 34% without promotion, the percentage of people who try to quit smoking with medium promotion is 35%.
． It is 02%. A high promotion costs $ 1000 and the incremental rate to quit smoking is obtained by multiplying the non-promotion rate by 4.5% and adding this product to the non-promotion rate. The ratio of non-promotions, which is 34%, is higher than that of high-promotions.
It is 5.53%. The cost and effectiveness of the promotion is determined by Towers Perrin and G
Obtained from data supplied by laxo Wellcome.

Risk of Disease and Mortality by Age, Gender and Smoking Status The effects of smoking status on disease risk and overall mortality were assessed by coronary artery disease (C
AD) (International Disease Classification, 9th edition (ICD-9) code 410-414), cerebrovascular disease (CVD) (430-438), chronic obstructive pulmonary disease (COPD) (496), And mortality, morbidity and economic costs from smoking for lung cancer (162) (Smokin
g-Attributable Mortality, Morbidity, and Economic Costs; SAMMEC) II, and the American Cancer Society Cancer Prevention Study-2 for general mortality.
iety Cancer Prevention Study-2) (Current Population Surve
y; CPS) -2) was evaluated using relative risk values from two sources. "SAMMEC II
Is computer software developed by the Office of the Department of Smoking and Public Health to promptly calculate smoking-related deaths, years of potential life loss, direct health care costs, indirect mortality costs, and illness costs. is there. The SAMMEC relative risk value is for mortality rather than morbidity, but other age / sex / smoking morbidity or relative risk values could not be established. Following consultation by numerous experts in this field, Applicants decided to use the SAMMEC values for disease values.

Separate relative risk values were used for current and former smokers (compared to those who had never smoked), and for males versus females in each smoking status group. In addition, another relative risk value for CAD and CVD of current and former smokers aged 35-64 vs. 65 and older was used. For those under the age of 35, the relative risk values from the 35-64 age group were also used.

To determine the annual incidence of CAD, CVD and CDPD according to age, sex, and smoking status, the general population size and population morbidity were 18-24, 25-34, 35-44, 45-54, 55
Determined using the 1993 National Health Interview Survey (NHIS) for men and women in six age groups (populations) of -64 and 65+. The ratio of nonsmokers, currently smokers, and former smokers in each of these age / sex groups was determined using the 1993 NHIS 2000 Addendum. Next, the morbidity rate R of non-smokers in a given age / sex group was determined as follows.

[0126]

[Equation 1] Where NUM = total number of individuals in the age / gender stratum that develop the specified disease, or = morbidity in the age / gender stratum multiplied by the total number of individuals in this stratum. NS = number of nonsmokers in age / gender. CS = current number of smokers in age / sex category. RRcs = Relative risk to disease status among currently smokers. FS = number of previous smokers in age / gender. RRfs = Relative risk to disease status among previously smokers.

To determine the morbidity rate among those who smoked previously or now, the morbidity rate of non-smokers was multiplied by the appropriate relative risk value. Similar calculations were performed for lung cancer. However, lung cancer rates in men and women by age group were obtained from the National Cancer Institute's Cancer Statistics Review. Similar calculations are made in 1993 National Center for Health Statistic's Vital Statistics o
Overall mortality was also performed using age and sex annual mortality from f the United States.

For the assessment of pregnancy complications, the percentage of spontaneous abortions of smokers vs. non-smokers (previously smokers + never smokers) was obtained from DiFranza and Lew, 1995, and low birth weight. Infant rates were obtained from Marks et al., 1990. We assumed that those who smoked previously and those who never smoked experience the same proportion of pregnancy complications. The annual pregnancy rate according to age was determined using 1993 NHIS and it was assumed that no pregnancy occurs after 44 years. M
Based on arks et al., 1990, Applicants also found that 20% of female smokers temporarily stopped smoking when pregnant, thus experiencing the same rate of pregnancy complications as nonsmokers. Assuming that these women resumed smoking after finishing pregnancy. The age-specific rate of abortion (abortion) was calculated from the actual number of live births and the low birth weight (
It was also calculated to determine the ratio of live births (depending on smoking status).

A person who previously smoked appears medically to be a non-smoker by the length of time after smoking cessation . Based on Towers Perrinm data, the overall health care costs of former smokers are equal to those of nonsmokers 16 years after cessation. However, the risk of previously smokers for certain symptoms assessed in models (CDPD, CAD, CVD and lung cancer) and the risk of excessive mortality are relative to these symptoms and to overall mortality. Risk remains based on the comparative data of all previously smokers versus nonsmokers, and thus remains at a high risk for nonsmokers in the model.

Annual Medical Care Costs for Current Smokers and Nonsmokers The annual medical care costs were derived from the values reported by Hodgson (1992). Hodgso
n presented costs over several years, and presented another cost for smokers / nonsmokers who lived or died during the designated period. Applicants are currently assessing the ratio of survivors to deaths in each period in each age / sex / smoking cohort and the number of years of survival in the population that died in each period. A weighted average annual cost for smokers and nonsmokers was determined. These costs are then calculated using the CPI medical care management component.
Inflated to the 1997 value. The overall annual medical care costs for male current smokers, male non-smokers, female current smokers, and female non-smokers are grouped by proportion of population in each age group. Determined by weighing the annual costs in the age / sex / smoking cohort (eg, the annual health care cost of a male smoker is its age multiplied by the annual health care cost of a male smoker in each age group). Determined by summing the ratio of male smokers in the population (population)).

Annual medical care costs for former smokers The value obtained per year by former smokers based on the number of years since smoking cessation is the percentage of excess medical costs incurred by current smokers. Powered by Towers Perrin. After 16 years of non-smoking, former smokers have the same costs as non-smokers. However, the data provided by Towers Perrin is a step function (discrete annual values) and results in miscalculations (eg, the cost for an individual who quits smoking 10.99 years ago is for an individual who quits 11 years ago). It was substantially different from the cost). To round these numbers and avoid such anomalies, Applicants used curve fitting software to determine a function that fits the Towers Perrin data. This function was created using a quadratic curve fit, and the rate of increase in total health care cost for smokers applicable to previously smokers X years after quitting is (0
． ^{87075-0.09084804 * X + 0.00247549 * X 2} ) equally, for X ≧ 18 equal to 0. The R-squared value for the quadratic curve fit of the Towers Perrin data is 0.9933143. The years since smoking cessation used in the regression equation is the weighted average of all previously smokers in each age / sex cohort (population). The number of years since quitting for individuals who were previously smoking at the time the model was started (ie, quit before the model was started),
Data from NHANES, 1988-1994 were used to determine for each age / sex age group.

A typical list of annual smoking addiction rates used by the smoking addiction rate model is as follows.

[0133]

[Table 22]

The source of values in the list of smoking addiction rates is the 1990 Surgeon General's Report.
This is a data smoothing function.

[0135] The average loss of productivity in the production of employers chart, (is not in dependents) was calculated for the lifetime guaranteed employees only. Smokers spend an additional 1.4% of their total salary because they spend more time stopping and leaving the workplace for the purpose of smoking.

Quit Smoking Success Rate The success rate with ZYBAN® is based on clinical trial results after one year. Towers Perrin assumes that if smokers do not quit for a full year, no medical or productivity savings will occur to the plan or employer. Except for treatment with ZYBAN®, success rate is the weighted average success rate without nicotine replacement therapy and smoking cessation aids. 52-week continuous smoking cessation data was used. Continuous quit rate is defined as the percentage of patients who quit consecutively from the date of quitting to the date of follow-up.

[0137]

[Table 23]

References Full references for each of the above data sources are as follows: CDC. Smoking cessation during the previous year in adults-United States, 1990-1991. MMWR .1994; Vol. 42, No. 50., pp. 925-930. Cromwell J, Bartosch WJ, Fiore MC, Hasselblad V, Baker T.
Cost-effectiveness of clinical practice proposals in AHCPR guidelines. JAMA . 1997; 278: 1759-1766. DiFranza JR, Lew RA. Effects of pregnant woman smoking on complications of pregnancy and sudden infant death syndrome. ... J Fam Pract 1995; 40: 385-394 Gold MR, Siegel JE, Russell LB, Weinstein MC costs in health and drug - efficacy (Cost-effectiveness in Health and Medicine ), New York, NY
: Oxford University Press; 1996. Halpem M, Sorenson S, Elixhauser A, Haddix A, Hatziandru E, Tanzi V, Shi
kiar R. Cost-Effectivenes of Premature Counseling in the United States
s of Preconception Counseling in the United States). Report to the Centers for Disease Control and Prevention, 1996. Hodgson TA. Cigarette smoking and lifetime medica
Millbank Quarterly . 1992; 70 (1): 81-125. Marks JS, Koplan JP, Hogue CJ, Dalmat ME. Smoking cessation costs for pregnant women-Profit / Cost-Effectiveness analysis (A cost-benefit / cost-effectiveness analysis of smoking
Amcession of pregnant woman). Am J Prev Med . 1990; 6: 282-289. Oster G, Colditz GA, Kelly NL. The Economic Costs of Smoking and Benefits of Quitting . Lexington. , Massachusetts: Lexington Books; 1984. Riley GF, Potosky AL, Lubitz JD, Kessler LG. Medicare payments from diagnosis to d in elderly cancer patients by stage of diagnosis.
eath for elderly cancer patients by stage of diagnosis). Medical Care .1
995; 33: 828-841. Strauss MJ, Conrad D, LoGerfo JP, Hudson LD, Bergner M. Costs and outcomes of care for patients with chronic obstructive pulmonary disease. Analysis by a specialist doctor (Cost and outcome of care for
patients with chronic obstructive lung disease. Analysis by physician sp
ecialty) .Medical Care . October 1986; 24 (10): 915-924. Talor TN, Davis PH, Torner CJ, Holmes J. Meyer JW, Jacobson MF. Lifetime cost of stroke in the United States). Stro ke . 1996; 27: 1459-1466. Tonneson P, Norregaard J, Simonsten K, Sawe U. A double-blind trial of a 16-hour transdermal nicotine patch in smoking cessation. -hour transde
rmal nicotine patch in smoking cessation) .N Engl J Med. 1991; 325: 311-3
15. Warner KE, Smith RJ, Smith DG, Fries BE. Health and economic relevance of workplace smoking cessation programs: Simulation analysis (Health and economic implicati
on Occasion of a work-site smoking cessation program: a simulation analysis). J Occup Environ Med .1996 ; 38 (10): 981-992. 1997 Physicians Fee & Coding Guide .HealthCare Consultants of America, I
nc .; 1997.1998 Drug Topics Red Book .Medical Economics Company; 1998.

The contents of each of the above cited sources are incorporated herein as part of their disclosure.

The present invention is not limited to the sources described above for default parameters.
For example, as new sources are published, the values for model default parameters can be updated to reflect data from the new sources. In addition, the model allows the user to change the default parameters, so that the user can use sources of knowledge they have, enter parameters from these sources into the model and calculate cost savings based on these sources. can do. The model allows the user to change the default parameter values, allowing the model to adapt to changes in the population performing disease management interventions.

Calculations In the software according to the present invention, the user can select the number of employees / health plan members, the number of adult dependents, the type of organization (work or health plan), geographical area, and model style (model pair with exchange. Group model). Allocation of combined employee / health planning member and adult dependent populations to male or female populations in six age groups based on organization type and geographical region (18-24, 25-34, 35- Four
4, 45-54, 55-64, 65+). The population of people of this model in each age / sex group is determined using parameters from NHIS.

Each age / sex population is also classified using the NHIS data into non-smokers, those who are currently smoking, or those who have previously smoked. For models with workplace programs, the working population (number of employees) is divided into 12 different occupations, based on the type of employer and geographical area specified using the Current Population Survey data. Each job category will also be assigned an average hourly wage based on geographic region and employer type using data from the Current Population Survey. The overall average hourly wage is determined as the weighted average of the hourly wage for each occupation multiplied by the percentage of the workforce in that occupation.

Separate calculations are made for each of the six age groups to determine cost and performance for this model. However, these calculations are identical for each population group, only the parameters used for the calculations vary. Within each age group, separate calculations are made with the smoking cessation aid (ie, ZYBAN®) applied and without the smoking cessation aid. The calculations with and without ZYBAN® are the same, only the parameters related to smoking cessation change. Within each age group, male never smoked, male previously smoked, male currently smoked, female never smoked, female previously smoked,
And for the 6 groups of women who currently smoke. Separate calculations are performed for each year of the model (until the age group reaches 85 years). Calculations for each age group begin by assigning the average age of the population. For example, 25-
For the 34 year old population, the average age of the first year of the model is assigned as 29. It increases by one year each year after the model until age 85.

For men who have never smoked in a given population, the calculation begins by determining the number of men who have never smoked in that population in the model.
For the first year of the model, this is simply the ratio of the general model population (employee / health plan members + adult dependents) in the specified age / sex / smoking status group. For each subsequent year of the model, subtract the number of men who had never smoked in the age group who died or left the company / health plan in the previous year to indicate that new men in this age group have never smoked The number of people. Multiply the number of never-smokers with coronary artery disease each year by the age-gender-smoker-specific incidence of coronary artery disease times the number of never-smokers in men in this age group. Determined by. The cost attributable to each year's coronary artery disease is determined by multiplying the number of men who have never smoked who develop coronary artery disease by the appropriate cost per case of coronary artery disease. Costs incurred after the first year of the model are discounted at an annual default rate of 3%. Parallel calculations are performed to determine the number and associated costs of cerebrovascular disease, chronic obstructive pulmonary disease and lung cancer for men who have never smoked in each age group.

The total annual health care costs for male non-smokers in each age group were calculated as the number of male non-smokers at each age of each year for that age-sex-smoking status group. Determined by multiplying the average total health care cost. Costs incurred after the first year of the model are discounted at an annual default rate of 3%. When model users specify additional direct or indirect costs, these costs are summed and discounted for each model year. The number of deaths in male non-smokers each year is determined by multiplying the number of male non-smokers in each age group by the mortality rate specific to age-sex smoking status. For models with workplaces, the number of men who have never smoked who leave the company (resigned or retired) or health plan (deregister) is the number of employees in the age group of those who have never smoked. By the turnover rate (a fixed value provided by the user or a set of default values based on the year of employment / health planning membership). Summing up the number of male non-smoking employees who die or stop company / health plans each year, the proportional number of female dependents of the same age group is also removed from the model. In the cohort model, individuals removed from the model are not replaced. In the model with exchanges, replace the total number of men (employee / health plan members + adult dependents) removed from the model each year with new men of the same age group. Exchange smoking status is determined using the initial smoking status analysis of the model population (based on values standardized by age / sex population geographic area) rather than the smoking status of individuals removed from the model.

Calculations for male former smokers are similar. For the first model year, the number of previously smoked males in each age group was compared to the general model population (employee / health plan members + adult subjects) in the specified age / sex / smoking status group. Dependent) ratio. The number of previously smoked men in each age group of each year differed by the number of previously smoked men of that age group who died or left the company / health program in the previous year. Subtracted and determined by adding the number of previously smoked men in exchange. In addition, we add the number of men who are currently smoking who quit smoking in the previous year and subtract the number of those who previously smoked (habituated) who smoked in the previous year. The average age of smoking cessation is determined annually for each age group of previously smokers. For the first year of the model, this age is National Hea
Assigned for each age group using data from the lth and Nutrition Examination Survey (NHANES). In each subsequent year, the age of smoking cessation was (1) the number of previously smoked males in the model from the previous year multiplied by the average age at the time of discontinuation in the previous year, and (2) the new age. It is determined as a weighted average using the two components of the number of males who previously smoked (those who quit smoking in the previous model year) times the age at which they quit. Next, the number of years after smoking cessation for male former smokers is calculated as the current age of that age group minus the weighted average age of non-smoking children.

The annual number of cases of coronary artery disease, cerebrovascular disease, chronic obstructive pulmonary disease and lung cancer, and the costs resulting from them in each age group, are the same as those described for men who have never smoked. The method was to determine the male former smoker. Total annual health care costs and additional annual direct or indirect costs were also determined in the same manner as described for men who never smoked. For those who have previously smoked, total annual health care costs are based on age, sex, and years since smoking cessation. Additional annual costs for male former smokers due to increased absenteeism or reduced productivity due to the condition of male former smokers are also calculated annually. The annual absentee cost increased the number of working days lost by men's former smokers compared to men's never-smokers, as well as the men's former-smoked employment in each age group. Calculated as the average daily wage multiplied by the number of employees (average hourly wage multiplied by 8 hours / day). Annual productivity costs are the average annual salary (average hourly wage x 8 hours / day x 5 days / week x 50 weeks / Years) and multiplied by the number of previously smoked employees of men in each age group. Absence rates and productivity costs are calculated only for work-based models, not for health planning models, and are discounted annually. With default model parameters, absenteeism and productivity costs by former smokers are zero.

The number of previously smoked men who die or leave the company (resign or retire) or health plans in their respective age groups is determined in the same way as for men who have never smoked. . For men who have never smoked, determine the number of previously smoked men who die or leave the company / health plan, and the proportional number of female dependents in the same age group also leave the model . In the model with exchange mode, the former smokers of men leaving the model were exchanged for the same number of new men in the same age group, and the smoking status for these new men was determined by their smoking experience. Make decisions as described for those who do not. In addition, each year, a percentage of men who previously smoked resume smoking. Probability of addiction is based on years after quitting smoking. The number of former smokers of men who resume smoking is subtracted from the previous smoker population of men in the model year of the following year and added to the current smoker population of men of the following year .

The calculations for male current smokers in each age cohort are similar to those for males who have never smoked and previously smoked. For the first model year, the number of males currently smoking in each age group is based on the overall model population (employee / health plan members + Adult dependents). The number of males currently smoking in each age group of each subsequent year is subtracted from the number of males currently smoking in that age group who died or left the company / health program in the previous year. , Exchange men determined by adding the number of current smokers. Further, subtract the number of men who currently smoke from smoking the previous year and add the number of men who previously smoked (habituated) to smoking earlier in the previous year.

The annual number of cases of coronary artery disease, cerebrovascular disease, chronic obstructive pulmonary disease and lung cancer, and the costs resulting from them in each age group, are the same as those described for men who have never smoked. How to determine a male's current smoker. Total annual health care costs and additional annual direct or indirect costs are also determined in the same manner as described for men who have never smoked. The additional annual costs for male currently smokers due to increased absenteeism or reduced productivity due to currently smokers are also noted for each age group, as noted for previously smokers. Is calculated every year at.

The number of smoking cessation trials conducted by male current smokers in each age group per year is the product of the number of male current smokers each year times the smoking cessation probability. It is determined. This probability is different in the calculations performed with and without the aid of smoking cessation aids and without the promotion of smoking cessation. The number of successful smoking cessation trials is determined as the number of smoking cessation trials times the average success rate. The annual cost for quitting smoking is determined as the number of quitting tests times the average cost per quitting test and is discounted annually. Quit cessation rates and costs per smoking cessation trial differ with and without smoking cessation aids and when ZYBAN® is included in the model.

The number of currently smoking men who die or leave the company (resign or retire) or health plans in their respective age groups is determined in a similar manner as for men who have never smoked. . For men who have never smoked, the number of males currently smoking who die or leave the company / health plan is determined, and the proportional number of female dependents in the same age group is also modeled. To be deleted. The model with exchange mode replaces the current smokers of men leaving the model with the same number of new men of the same age group, and the smoking status for these new men is It is determined by the method described for the person. The number of successful smokers who currently smoke is subtracted from the number of men who currently smoke in the next model year and added to the number of men who previously smoked.

Calculations for women never smoked, previously smoked, and currently smoked, except that outcomes and costs related to pregnancy and the effect of smoking status on pregnancy were included. , The same as the calculation for smoking status in men. For women who have never smoked in their respective age groups, the annual number of pregnancies is determined by multiplying the number of women who have never smoked by the age-specific pregnancy rate.
Pregnancy after age 44 is assumed to be zero. The annual number of miscarriage (spontaneous miscarriage) for those who have never smoked is determined by multiplying the annual number of pregnancies by the miscarriage rate for women who have never smoked. The annual cost of a miscarriage of a non-smoker is the number of miscarriage of a non-smoker and the average cost / miscarriage. The annual number of abortions in those who have never smoked is also determined as the product of the number of annual pregnancies and the age-specific abortion rate. The annual number of live births for women who have never smoked is the number of pregnancies minus the number of abortions and abortions. The annual number of low birth weight infants who have never smoked is the product of the annual number of live births and the low birth weight rate. The annual cost for low birth weight infants of those who have never smoked is the product of the number of years of low birth weight multiplied by the average cost / low birth weight infant. The costs of miscarriage and low birth weight in smokers are discounted annually. Pregnancy outcomes and costs for previously smokers and currently smokers are those who have never smoked using the incidence rates applicable to previously smokers and currently smokers, respectively. It is determined by the method described for the person.

A similar calculation is performed for each age group with and without smoking cessation aids. The only differences in these calculations using the default parameters are the success rate of smoking cessation and the cost per smoking cessation trial. The difference in smoking cessation success rates results in the difference in the number of former and current smokers in each age group, and thus in the number and cost of smoking-related incidents.

The number of incidents and costs is summed separately over each of the 6 age groups (male or female in the three smoking states) separately for each age group, with and without smoking cessation aid calculated. To do. The total number and costs of incidents for men, women, and overall were also determined separately for each age group up to retirement age (model departure to 65 years) and full model duration (model departure to 85 years). Calculate with and without smoking cessation aids. Next, the difference between cases and costs of applying smoking cessation aids and not applying them is determined for each age group. The number of incidents and costs are summed separately over the six age groups and calculations are made with and without a smoking cessation aid to determine the general difference between the two sets of calculations.

The results of the above calculations are used to present the output to the model. Outcomes sum the differences in costs, specific disease cases, smoking cessation, and death over 2, 5, 10 and 20 model years, and include calculations with and without a smoking cessation aid. Cost-benefit analysis presents health care costs, indirect costs (absence and productivity) and smoking cessation costs up to ages 65 and 85, respectively, and calculates with and without smoking cessation aids Determine the difference between the two sets of calculations. The profit-cost ratio is calculated as the health care cost, or the savings in health care and indirect costs with the smoking cessation aid divided by the increase in smoking cessation costs with the smoking cessation aid. For the workplace model, the per-employee per year (PEPY) smoking cessation supplemental cost increase is related to the increase in smoking cessation expense when applying the smoking cessation supplement in the first model year by the model population for the first model year. It's divided. For the health planning model, the monthly cost per member (PMPM) increase is calculated by dividing the increase in smoking cessation costs when applying smoking cessation adjuncts in the first model year by the model population for the first model year. Is divided by 12. Internal rate of return (IRR) is a calculation performed using the function supplied in the WINDOWS® application, which is the average annual discount rate that would result in the same costs with and without smoking cessation aids. decide. The profit and loss analysis shows the cumulative increase in smoking cessation costs per year from application of smoking cessation aids compared to no application. Cumulative savings in health care costs alone or health care and indirect costs due to the application of smoking cessation aids are also shown each year. The return on investment (ROI) calculated by this analysis is the cumulative cumulative real cost savings of applying smoking cessation aids (health care savings,
Or health care and indirect cost savings less smoking cessation increase) divided by cumulative smoking cessation. ROI is negative until cumulative savings outweigh the cumulative increase in spending on smoking cessation.

It will be appreciated that various details of the invention may be changed without departing from the scope of the invention. Furthermore, the above description is merely for purposes of illustration and is not intended to limit the invention as defined by the claims.

[Brief description of drawings]

FIG. 1 is a flow chart illustrating exemplary steps performed by a computer implemented system for assessing costs and benefits associated with disease management according to one embodiment of the invention.

FIG. 2 is a block diagram illustrating a typical hierarchy of screens or windows that may be displayed on a computer display device to allow a user to enter data and view output regarding disease management interventions.

FIG. 3 is an initial window of a computer implemented system for assessing the costs and benefits incurred in managing disease according to one embodiment of the invention.

FIG. 4 is a parameter window in which a user can input parameters generated by disease management.

FIG. 5A is an Age / Gender folder tab of a population demographics window according to one embodiment of the invention.

FIG. 5B   The Occupations Folder tab of the Population Demographics window, according to one embodiment of the invention.

[Figure 6]   An additional model parameter window according to an embodiment of the present invention.

[Figure 7]   An intervention parameter window according to an embodiment of the present invention.

[Figure 8]   A smoking addiction rate window according to one embodiment of the present invention.

[Figure 9]   A disease-specific cost window according to one embodiment of the invention.

[Figure 10]   A smoking status distribution window according to an embodiment of the present invention.

FIG. 11A   7 is a cost saving graph window according to an embodiment of the present invention.

FIG. 11B   7 is a disease relief graph window according to an embodiment of the present invention.

FIG. 11C   A results table window according to an embodiment of the present invention.

[Fig. 12]   A cost benefit analysis window according to one embodiment of the present invention.

FIG. 13A   A P & L graph window according to one embodiment of the invention.

FIG. 13B   A P & L analysis window according to one embodiment of the invention.

FIG. 14   A data source window according to an embodiment of the present invention.

─────────────────────────────────────────────────── ─── Continued front page    (81) Designated countries EP (AT, BE, CH, CY, DE, DK, ES, FI, FR, GB, GR, IE, I T, LU, MC, NL, PT, SE), OA (BF, BJ , CF, CG, CI, CM, GA, GN, GW, ML, MR, NE, SN, TD, TG), AP (GH, GM, K E, LS, MW, MZ, SD, SL, SZ, TZ, UG , ZW), EA (AM, AZ, BY, KG, KZ, MD, RU, TJ, TM), AE, AG, AL, AM, AT, AU, AZ, BA, BB, BG, BR, BY, BZ, C A, CH, CN, CR, CU, CZ, DE, DK, DM , DZ, EE, ES, FI, GB, GD, GE, GH, GM, HR, HU, ID, IL, IN, IS, JP, K E, KG, KP, KR, KZ, LC, LK, LR, LS , LT, LU, LV, MA, MD, MG, MK, MN, MW, MX, MZ, NO, NZ, PL, PT, RO, R U, SD, SE, SG, SI, SK, SL, TJ, TM , TR, TT, TZ, UA, UG, US, UZ, VN, YU, ZA, ZW (72) Inventor Ziva, Mohammad, Khan             Lisa, North Carolina, United States             Reach, triangle, park, pee. Oh             -. Box, 13398, Five, Moore,             Drive, inside the GlaxoSmithKline (72) Inventor Peggy, Sue, Olson             Lisa, North Carolina, United States             Reach, triangle, park, pee. Oh             -. Box, 13398, Five, Moore,             Drive, inside the GlaxoSmithKline (72) Inventor Teresa, Lin, Young             Lisa, North Carolina, United States             Reach, triangle, park, pee. Oh             -. Box, 13398, Five, Moore,             Drive, inside the GlaxoSmithKline (72) Inventor Michael, Tee. Halpan             Annandei, Virginia, United States             Le, Canterbury, Drive, 8614

Claims (33)

[Claims] 1. A computer-implemented system for estimating costs and benefits associated with disease management, comprising: (A) a first graphical representation for accepting population-specific data for a population that has been offered treatment for disease management. Providing a user interface on a computer display device, (B) calculating costs and benefits associated with disease management for a certain period of time based on the group-specific data, and (C) costs and benefits And outputting data indicative of, and a system comprising computer-executable instructions recorded on a computer-readable medium for performing. 2. The system of claim 1, wherein providing a first graphical user interface includes displaying a first window that includes a plurality of population-related input cells for selecting population-specific data. . 3. The system of claim 2, wherein the first window includes a plurality of model-related input cells for selecting model data for modeling disease management interventions. 4. The system of claim 2, wherein at least some of the population related input cells include default values for the population. 5. Displaying the first window includes displaying a plurality of control buttons activated by an input device for displaying cost savings data.
The system described in. 6. The system of claim 1, wherein calculating costs and benefits comprises calculating direct medical cost savings. 7. The system of claim 1, wherein calculating costs and benefits includes calculating direct and indirect cost savings. 8. The method of calculating costs and benefits includes calculating savings in illness costs.
The system described in. 9. The system of claim 1, wherein displaying the data indicative of costs and benefits comprises displaying cost savings in a tabular format. 10. The system of claim 1, wherein displaying the data indicative of costs and benefits comprises displaying the cost savings in a graphical format. 11. The system of claim 1, including performing a benefit analysis of costs incurred with disease management and displaying the results of the benefit analysis of the costs. 12. The method of claim 11, wherein performing a cost-benefit analysis comprises estimating direct medical costs for the population with and without disease management interventions. System. 13. The system of claim 1, including performing a profit and loss analysis that occurs with disease management and displaying the results of the profit and loss analysis. 14. The system of claim 13, wherein performing a P & L analysis comprises determining a period in which the estimated savings or gains associated with disease management exceed the estimated costs. 15. Performing a P & L analysis comprises estimating a percentage of health care cost savings associated with illness management in each of a plurality of time intervals measured since performing the illness management intervention. The system described in. 16. The system of claim 13, wherein displaying the results of the cost benefit analysis comprises displaying the results in a tabular format. 17. The system of claim 13, wherein displaying the results of the cost benefit analysis comprises displaying the results in a graphical format. 18.   The system of claim 1, wherein the disease management intervention is a smoking cessation program. 19. The system of claim 1, wherein the disease management intervention is an influenza vaccination program. 20. A method of estimating costs and benefits incurred in disease management, the method comprising: (A) providing a graphical user interface for entering population-specific data for a population undergoing treatment associated with disease management. , (B) a step of calculating costs and benefits incurred due to disease management for a certain period based on the group-specific data, and (C) a step of outputting data showing the costs and benefits Method. 21. The method of claim 20, wherein entering population-specific data comprises entering the number of employees receiving treatment associated with disease control. 22. The method of claim 20, wherein entering population-specific data comprises entering the number of health plan members to be treated in a disease management intervention. 23. The method of claim 21, wherein populating population-specific data comprises populating the salary and job category of the employee being treated in a disease management intervention. 24. A computer-implemented system for estimating costs and benefits associated with disease management, comprising: (A) a computer display device for treatment or a proposal for treatment associated with disease management. Means for displaying the default parameters for (B) and (B) means for changing at least some of the default parameters to reflect the population-specific data entered by the user, and (C) the default parameters and the population-specific parameters entered by the user. And a means for estimating the costs and benefits of disease management based on 25. The system of claim 24, wherein the means for estimating costs and benefits is calculating an internal rate of return for a disease management intervention. 26. The system of claim 24, including means for displaying to a user a data source of at least some of the default parameters. 27. The system of claim 26, wherein the means for displaying the data source is for displaying the cited references of the data source. 28. The system of claim 24, including means for displaying to a user definitions associated with disease management interventions. 29. (A) displaying a plurality of windows on a computer display device, at least some of these windows being suitable for entering population-specific data regarding the population to be treated in a disease management intervention; ) Performing the steps of calculating the costs and benefits incurred by controlling the disease for a certain period based on the group-specific data, and (C) outputting the data showing the costs and benefits to the user. A computer program product comprising computer-executable instructions recorded on a computer-readable medium for performing. 30. The computer program product of claim 29, wherein displaying the cost and benefit data comprises printing the data using a printing device. 31. The computer program product of claim 29, wherein displaying the data indicative of costs and benefits comprises displaying the data on a computer display device. 32. The computer program product of claim 29, wherein calculating the fixed period costs and benefits comprises calculating cost savings data for disease management interventions. 33. The computer program product of claim 29, wherein calculating the fixed period costs and benefits comprises calculating an internal rate of return associated with disease management.