Integrated disease information system

Abstract

A system including a set of software based Explorers, and a computer assisted methodology support the development of new medical interventions for diseases. The system includes Explorer modules for discovering proposed interventions, designing clinical trials, performing pharmacoeconomic analysis, and illustrating disease progression for various patients over time including creating disease progression tutorials for patients. The Explorers support a bottom-up or data driven methodology that enables a user, such as medical researcher, to mine data sources of clinical, biologic, expert or other types of data to discover, test, evaluate, and understand a proposed intervention and its impact on disease progression in different patient types. A Target Discovery Explorer assists the user in identifying leverage points in disease progression in relationship to various patient attributes and interventions, thereby identifying a proposed intervention for the desease. A Clinical Trials Explorer assists the user in designing clinical trials based through identification of combinations of patient attributes and intervention attributes that yield efficacious changes in selected disease progression measures. A Pharmacoeconomic Explorer enables the user to determine relative costs-benefits of a proposed intervention for patients, practitioners, and payers, including quality of life results for patients, practice results for practitioners, and financial payment results for payers. A Disease Progression Explorer enables the user to visually project disease progression for specified patient attributes and interventions, in order to better understand and explain the effects of an intervention on a disease for such patients and their practitioners, and to select disease progression tutorials that are directed to the specific patient attributes and their corresponding effect on disease progression over time.

Claims

What is claimed is:

1. A computer assisted method of identifying a proposed intervention for a disease, comprising the computer assisted steps of:

storing a database of biological data relating changes in biological systems to changes in disease progression of the disease, the biological data including:

data relating intercellular, intracellular, and organic changes in a biological system to changes in disease progression for a standard intervention;

patient attributes of a plurality of patient types;

intervention attributes of a plurality of interventions;

biology attributes of cellular changes or cellular attributes associated with each intervention; and

disease progression measures for a disease;

receiving user inputs of biologic parameters of a biological system;

constructing a query from the user inputs;

querying a data source with the query to determine values of disease progression measures associated with the biologic parameters, the disease progression measures selectively including:

cellular behavior parameters;

intermediate biological disease progression measures; and

clinically observable disease progression measures;

displaying the values of the disease progression measures; and

systematically querying of the data source with user input changes in the biologic parameters associated with standard interventions to identify the proposed intervention that produces disease progression measures indicative of an effective alteration of the disease.

2. A computer assisted method of designing a clinical trial for a proposed intervention for a disease, comprising the computer assisted steps of:

receiving data relating disease progression for selected patient types to different interventions with respect to clinical symptoms;

receiving an input of a proposed clinical trial design defined by a user selected of intervention attributes of a proposed intervention and patient attributes that are to be controlled during the proposed clinical trial;

producing a disease progression of values of disease progression measures for each of the proposed interventions for each of a plurality of patient types having different patient attributes by:

receiving patient attributes for a single patient of a selected patient type;

determining values of disease progression measures for the patient as a function of the patient attributes and the proposed intervention and standard interventions; and

displaying the values of the disease progression measures and patient attributes for each of the interventions, to assist in identifying patient attributes for which the proposed intervention provides an efficacious result on the disease progression relative to other interventions.

3. The method of claim 2, wherein the disease progression measures include at least one of:

cellular data descriptive of disease progression;

intermediate biologic data descriptive of the disease progression; and

clinically observable symptoms of the disease progression.

4. The method of claim 2, further comprising:

storing an expert knowledge base relating values of disease progression measures to selected anatomical representations of clinically observable symptoms of disease progression on a human body;

selecting from the knowledge base at least one anatomical representation of the disease progression measures in response to the patient attributes; and

graphically displaying the selected anatomical representation(s) of the disease progression measures.

5. The method of claim 2, wherein determining values of disease progression measures further comprises:

receiving patient attribute data for a plurality of patient attributes;

displaying a plurality of factorial combinations of the patient attributes, each factorial combination representing patient attributes of a single patient type;

for each of the plurality of factorial combinations of patient attributes:

querying a data source to retrieve values of disease progression measures for the combination of patient attributes; and

selectively displaying the values of the disease progression measures for patient attributes.

6. The method of claim 5, further comprising:

displaying correlations between the plurality of patient attributes and the plurality of disease progression data retrieved from the data source.

7. A computer assisted method of determining a disease progression for a proposed intervention for a disease, comprising the computer assisted steps of:

providing a database of a disease progression information relating changes in disease progression measures of changes in subcellular, cellular, organ, anatomical, or clinical attributes with respect to time and to patient attributes;

receiving a user input of specified patient attributes of a patient;

receiving disease progression measures of the disease, including a time period for projecting the disease progression for the patient;

determining from the database, the disease progression for the time period as a function of the patient attributes, and the disease progression measures; and

displaying the disease progression as changes in subcellular, cellular, tissue, or anatomical attributes of the patient.

8. The method of claim 7, wherein displaying the disease progression further comprises:

displaying the disease progression on an anatomical model of the body, showing disease progression over the time period.

9. The method of claim 7, further comprising:

providing a plurality of disease progression tutorials for the disease;

receiving patient attributes for a patient;

receiving intervention data for the proposed intervention;

receiving a user selection of a disease progression tutorial from the plurality of disease progression tutorials; and

displaying the selected disease progression tutorial.

10. The method of claim 7, further comprising:

graphically displaying the disease progression for a patient having the specified patient attributes.

11. A computer assisted method of developing an intervention for a disease, comprising the computer assisted steps of:

storing a database of biologic parameters of biological systems, disease progression measures, patient attributes, and intervention data;

receiving an input of a proposed clinical trial design defined by a user selected plurality of intervention attributes and patient attributes that are to be controlled during the proposed clinical trial;

automatically and successively altering the biologic parameters of selected biological systems and querying the database to identify a proposed intervention that affects or measures the disease progression; and

automatically and successively altering patient attributes, biologic parameters, and intervention attributes from the user selected set, and querying the database to determine a database outcome of the proposed clinical trial in terms of relationships between patient attributes, interventions, and disease progression measures.

12. The method of claim 11, further comprising:

specifying patient attributes and intervention data, and a time period for a disease progression; and

projecting from the database, disease progression measures over the time period as a function of the patient attributes and intervention data.

13. The method of claim 11, further comprising:

receiving at least one of patient attributes, practitioner attributes, and payer attributes; and

determining for a proposed intervention a pharmacoeconomic analysis of economic benefits of the proposed intervention relative to at least one standard intervention.

14. The method of claim 11, wherein identifying from the biological data a proposed intervention, further comprises:

receiving user inputs of biologic parameters of a biological system;

constructing a query from the user inputs;

querying a data source with the query to determine values of disease progression measures associated with the biologic parameters;

displaying the values of the disease progression measures; and

systematically querying of the data source with user input changes in the biologic parameters associated with standard interventions to identify the proposed intervention that produces disease progression measures indicative of an effective alteration of the disease.

15. The method of claim 14, wherein the disease progression measures are displayed as an animated sequence of images.

16. The method of claim 14, further comprising:

inferring disease progression measures associated with the input biologic parameters in response to the input biologic parameters not matching biologic parameters in the data source.

17. A computer assisted method of developing an intervention for a disease, comprising the computer assisted steps of:

identifying, from biological data of a disease progression, a proposed intervention that affects or measures the disease progression;

designing a clinical trial of the proposed intervention by analysis of a factorial combination of intervention attributes and patient attributes to determine a disease progression of the disease for a selected patient type receiving the proposed intervention;

estimating economic costs and benefits of the proposed intervention relative to standard interventions by analysis of economic and non-economic intervention costs and benefits associated with disease progression for the proposed intervention relative to economic and non-economic intervention costs and benefits associated with the disease progression for the standard interventions; and

displaying a disease progression over a specified time period for a selected patient attributes.

18. A computer assisted method of developing an intervention for a disease, comprising the computer assisted steps of:

identifying, from biological data of a disease progression, a proposed intervention that affects or measures the disease progression;

determining clinical trial data of the disease progression of the disease for a selected patient type receiving the proposed intervention by analysis of a factorial combination of intervention attributes of the proposed intervention and patient attributes; and

producing for the proposed intervention a pharmacoeconomic analysis of economic benefits of the proposed intervention relative to other interventions by analysis of economic and non-economic intervention costs and benefits associated with disease progression for the proposed intervention relative to economic and non-economic intervention costs and benefits associated with the disease progression for the standard interventions.

19. The method of claim 18, further comprising:

creating a disease progression that describes a progression of the disease for user specified patient attributes, to assist practitioners in providing the proposed intervention to a patient having the specified patient attributes.

20. A computer assisted method for therapy data analysis and creation comprising:

storing information related to therapy data for therapies and disease data for diseases, the information including one or more biologic parameters related to the therapies and biology changes from disease progression in the diseases in response to the therapies; and

receiving in a therapy discovery explorer one or more biologic parameters and analyzing the biologic parameters to create therapy data for a therapy for a disease, the therapy data relating biology change from the therapy to disease progression in the disease.

21. A computer system for assisting in the development of an intervention for a disease, comprising:

a database storing:

biological data for biological systems related to the disease;

patient type data for patients having the disease;

economic data for standard interventions applied to the disease, and

clinical data of clinical trials of standard interventions;

a target discovery module, coupled to the database to receive the biological data and patient type data, to identify a proposed intervention and produce first intervention data of effects of the proposed intervention on measures of disease progression;

a clinical trials module, coupled to the database and the target discovery module, to receive the patient type data, the first intervention data, and second intervention data of effects of standard interventions on the disease progression, and to produce clinical trial data relating selected patient populations having specific patient attributes, and disease progression for each patient type to identify patient types for which the proposed intervention has a clinically efficacious effect for inclusion in a clinical trial of the proposed intervention;

a pharmacoeconomic module, coupled to the database, and the clinical trials module, to receive the patient type data, and the economic data, to produce a pharmacoeconomic analysis of economic costs and benefits of the proposed intervention for a selected patient type relative to standard interventions; and

a disease progression module, coupled to the database to receive the biological data and patient type data, to produce, for at least one patient type having specified patient attributes, a description of disease progression in the patient type over a user specified time period.

22. The system of claim 21, further comprising:

a results database for storing intermediate result data, including:

patient attributes;

intervention attributes, for both standard interventions and the proposed intervention;

disease progression measures for the standard interventions and the proposed intervention over time, the disease progression measures selectively including cellular data and clinically observable symptoms;

cost data for the standard intervention and the proposed intervention for patients and for payers; and

pharmacoeconomic outcome data for selected patients or patient populations, practitioners, and payers.

23. The system of claim 21, wherein the pharmacoeconomic outcome data further comprises:

estimated cost of future treatments for the standard interventions and the proposed intervention;

quality of life data for the standard interventions and the proposed intervention; and

practice based results to the practitioner for providing either standard interventions or the proposed intervention.

24. A computer system for describing and presenting disease progression measures resulting from a proposed intervention for a disease, comprising:

a database storing:

biological data for biological systems related to the disease;

patient type data for patients having the disease;

disease progression measures for the disease of changes in subcellular, cellular, organ, anatomical, or clinical attributes over time for various interventions as applied to the disease in different types of patients; and

a disease progression module, coupled to the database to receive the biological data, a proposed one of the interventions, and selected patient type data, to produce, for at least one patient type having specified patient attributes, a graphic presentation of a projection of disease progression measures of the disease over time in the selected patient type resulting from the proposed intervention.

25. The system of claim 24, wherein the disease progression module further comprises:

a patient history module for receiving patient attributes of a patient and retrieving values for disease progression measures from the database descriptive of disease progression in the patient.

26. The system of claim 24, wherein the disease progression module further comprises:

a disease progression evaluation module that receives patient attributes for a patient, intervention data for the proposed intervention, and a time period for projecting the disease progression measures in the patient, and that displays the disease progression measures over the time period for the patient based on the patient attributes, the disease progression measures, and the intervention data.

27. The system of claim 26, wherein the disease progression evaluation module displays the disease progression on an anatomical representation of a human body or portion thereof.

28. An apparatus for therapy data analysis and creation comprising:

a data/information source for storing information related to therapy data and disease data, the stored information including one or more biologic parameters related to therapies and biology changes from disease progression in response to the therapies;

a process interface for accessing the data/information source to obtain biologic parameters; and

a therapy discovery explorer for receiving biologic parameters from the process interface and for analyzing the biologic parameters to create therapy data for a therapy, the therapy data relating biology change from the therapy to disease progression.

29. The apparatus according to claim 28, wherein the therapy discovery explorer comprises a biologic manipulation tool for producing one or more profiles of disease progression based on the one or more biologic parameters.

30. The apparatus according to claim 29, wherein the biologic manipulation tool queries the data/information source based on the one or more biologic parameters to produce the one or more profiles of disease progression.

31. The apparatus according to claim 29, wherein the biologic manipulation tool comprises a graphical user interface for entering the biologic parameters.

32. The apparatus according to claim 28, wherein the therapy discovery explorer further comprises a disease progression evaluation facility for analyzing disease progression based at least in part on information retrieved by the biologic manipulation tool from the data/information source and for developing information relating biology change and disease change.

33. The apparatus according to claim 28, wherein the therapy discovery explorer comprises a disease progression evaluation facility for analyzing one or more profiles of disease progression and developing the information relating biology change and disease change based on the profiles.

34. The apparatus according to claim 33, wherein the disease progression evaluation facility comprises target treatment development support for identifying at least one therapy.

35. The apparatus according to claim 33, wherein the disease progression evaluation facility comprises a user interface element for creating a display of the information relating biology change and disease change.

36. The apparatus of claim 28, wherein:

the data/information source further comprises a database storing:

biological data for biological systems related to the disease;

patient type data for patients having the disease; and

the therapy discovery explorer is coupled to the database to receive the biological data and patient type data, to identify a proposed intervention and produce intervention data of effects of a proposed intervention on measures of disease progression.

37. The apparatus of claim 36, wherein the therapy discovery explorer further comprises:

a biological manipulation tool for qualitatively or quantitatively altering biological parameters of a biological system to determine changes in disease progression measures; and

a biological change evaluation tool for displaying relationships between alterations in biological parameters and resulting changes in disease progression measures.

38. The apparatus of claim 37, wherein:

the biological manipulation tool:

receives user inputs of biological parameters of a biological system;

constructs a query from the user inputs;

queries a data source with the query to determine values of the disease progression measures associated with the biological parameters; and

the biological change evaluation tool displays the values of the disease progression measures in relationship to the input biological parameters.

39. The apparatus of claim 38, wherein:

the data/information source comprises a database storing:

biologic parameters for biological systems related to a disease;

disease progression measures for the disease over time;

patient attribute data for various patient types having the disease;

intervention data descriptive of standard interventions and a proposed intervention; and

the clinical trials explorer is coupled to the database, to receive selected patient attributes, selected biologic parameters, and selected intervention data, for querying the database to identify combinations of patient attributes and intervention for which the proposed intervention has a clinically efficacious effect on the disease progression measures, for designing a clinical trial of selected patient attributes, biologic parameters, and intervention data.

40. The apparatus of claim 39, wherein the clinical trials explorer further comprises:

a patient type efficacy module that receives the patient attributes for a single patient type, and outputs the disease progression measures for the patient type as a function of each of a plurality of interventions, including the proposed intervention and other interventions; and

a clinical trial design module that receives a plurality of distinct patient attributes, biological parameters, and intervention data, and queries the database with respect selected combinations of patient attributes, each selected combination of patient attributes representing a patient type, to determine disease progression measures for each patient type and an intervention.

41. The apparatus of claim 40, wherein the patient type efficacy module further comprises:

a clinical visualization module that receives patient attributes for a plurality of patient types and selected disease progression measures, and outputs an anatomical representation of disease progression for the selected ones of the disease progression measures and patient types as a function of the proposed intervention.

42. The apparatus of claim 40, wherein the clinical trial design module further comprises:

a trial analysis tool that receives values of selected disease progression measures, for the plurality of patient attributes, and determines and displays correlations between individual ones of the patient attributes and selected disease progression measures.

43. An apparatus for clinical trial data analysis and creation comprising:

a data/information source for storing information related to therapy data, patient data, and patient type information, the information including one or more biologic parameters related to therapies and biology changes from disease progression in response to the therapies, therapy data, and patient type information;

a process interface for accessing the data/information source to obtain biologic parameters, therapy data and patient type information; and

a clinical trials explorer for receiving biologic parameters, therapy data, and patient type information from the process interface and for analyzing the biologic parameters, therapy data, and patient type information to create clinical trial data including disease progression information from biology changes for the patient type and a therapy.

44. The apparatus according to claim 43, wherein the clinical trials explorer includes: a patient type efficacy module for receiving patient type information and developing disease progression information based on the patient type information.

45. The apparatus according to claim 43, wherein the clinical trials explorer comprises a visualization component for displaying patient type disease progression information from rules-based analysis.

46. The apparatus according to claim 43, wherein the clinical trials explorer includes, a clinical trial design suite for determining one or more disease outcomes for one or more patient types over a specified period of time with respect to at least one therapy.

47. The apparatus according to claim 46, wherein the clinical trial design suite comprises a study design tool for developing information relating patient types and disease progression based on analysis variables.

48. The apparatus according to claim 46, wherein the clinical trial design suite comprises a trial analysis tool for developing correlations between patient variables and disease outcomes.

49. An apparatus for therapy outcome data analysis and creation comprising:

a data/information source for storing information related to therapy data for therapies, biology change from therapies, and economic and non-economic outcomes of therapies;

a process interface for accessing the data/information source to obtain the stored information; and

a pharmacoeconomic explorer for receiving the stored information from the process interface and for performing an effectiveness analysis on the received information to determine an effectiveness of a therapy as a result of biology changes from the therapy and economic and non-economic outcomes of the therapy.

50. The apparatus according to claim 49, wherein the pharmacoeconomic explorer includes at least one outcome analyzer for comparing a proposed therapy to current standard therapy for a particular constituent.

51. The apparatus according to claim 50, wherein the pharmacoeconomic explorer receives information characterizing clinical trial results to support the outcome analyzer.

52. The apparatus according to claim 49, wherein the pharmacoeconomic explorer includes, at least one outcome analyzer for developing information relating a therapy to a particular constituent outcome.

53. The apparatus according to claim 52, wherein the pharmacoeconomic explorer receives information characterizing clinical trial results to support the outcome analyzer.

54. The apparatus of claim 49 wherein:

the data/information source comprises a database storing:

patient type data for patients having the disease;

economic data for a plurality of standard interventions and the proposed intervention as applied to the disease;

disease progression measures for the disease for the plurality of standard interventions and the proposed intervention as applied to disease; and

the pharmacoeconomic explorer is coupled to the database to receive the patient type data, the economic data, and the disease progression data, to produce a pharmacoeconomic analysis of economic costs and benefits of the proposed intervention for a selected patient type relative to the standard interventions.

55. The apparatus of claim 54, wherein the pharmacoeconomic explorer further comprises:

a patient outcome analysis module that receives patient attributes for a patient type, disease attributes for a disease, and intervention data of the proposed intervention, and determines a patient outcome, including an estimated cost of the proposed intervention to the patient and a quality of life value for the patient type receiving the proposed intervention.

56. The apparatus of claim 54, wherein the pharmacoeconomic explorer further comprises:

a practitioner outcome analysis module that receives practitioner attributes for a practitioner providing the proposed intervention, and patient attributes for a patient type receiving the proposed intervention, and determines a practitioner outcome as a result of providing the proposed intervention to the patient type.

57. The apparatus of 54, wherein the pharmacoeconomic explorer further comprises:

a payer outcome analysis module that receives intervention data of the proposed intervention, future treatment data for the proposed intervention, and payer attributes of a payer providing payment for the proposed intervention, and determines a payer outcome, including an estimated cost of the proposed intervention to the payer.

58. A system for biological data analysis used in developing, testing and evaluating therapies for a disease, comprising:

at least two distinct data/information sources storing information related to at least one disease, various interventions for the at least one disease, including standard interventions, and disease progression information for at least one disease from the interventions; and

a pharmacoeconomic explorer interface integrating data received from the at least two distinct data/information sources to provide an outcome analysis of a proposed one of the interventions for a disease compared to standard interventions for the disease.

59. The system according to claim 58, wherein the at least two distinct data/information sources are chosen from the group consisting of expert knowledge databases, historical databases, clinical trial results, and computer models.

60. The system according to claim 58, wherein results produced by the pharmacoeconomic clinical trials explorer interface are a distinct data/information source which may be used by a second interface in providing reliably appraised data of a desired biological system.

61. An apparatus for therapy analysis and creation comprising:

a process interface for accessing an information source storing biological information of biological systems, the information including therapy data for therapies, biologic parameters related to the therapies and biology changes from disease progression in response to the therapies, patient type information, and economic and non-economic outcomes of the therapies, the process interface communicatively coupled to at least two explorers selected from a group consisting of:

a therapy discovery explorer that receives selected biologic parameters from the information source and develops therapy data relating biology changes to disease changes from a disease by selective alteration of biological parameters;

a clinical trials explorer that receives patient type information and therapy data, and biological parameters from the information source and develops disease progression data for the patient type and therapy therefrom; and

a pharmacoeconomic explorer that develops outcome data of a proposed therapy compared to at least one standard therapy.

62. The apparatus of claim 61, further comprising:

an information source including a plurality of different information sources, each information source providing a different type of information, including at least one of biological information, pharmaceutical information, or clinical information, economic information, or therapy information.

63. The apparatus of claim 62, wherein the plurality of information sources include at least two information sources from the group comprising:

an expert knowledge database;

a historical literature database;

a clinical trials results database; and

a computer model.

64. The apparatus of claim 62, wherein at least one of the explorers receives and processes data from at least two different information sources to develop its data.

65. The apparatus of claim 62, including at least two of the explorers, wherein the data developed by one of the explorers is an information source used by another explorer to develop its respective data.

Description

COPYRIGHT NOTIFICATION

Portions of this patent application contain materials that are subject to copyright protection. The copyright owner has no objection to facsimile reproduction of the patent document or the patent disclosure by anyone, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates to computer-based systems for disease information input, analysis, and output. In particular, the invention relates generally to systems for developing new therapies, tests, devices, regimens, or other interventions for biological systems and more particularly to systems providing integrated management and analysis of multiple data sources of biological, patient, or population data in developing new interventions.

BACKGROUND OF THE INVENTION

New therapy and medical test (hereafter "intervention") development is extremely speculative. In order to bring a new intervention to market, numerous hurdles must be overcome. Each hurdle involves gaining knowledge about how the intervention works, under what situations it works, and whether or not it is safe. The major hurdles in development are discovering a proposed intervention, testing it in a human population, determining whether its effect produces a significant improvement over other interventions for a given disease, and finally, educating practitioners and patients about its benefit and appropriate use. Each of these hurdles requires the generation, collection, and analysis of a large amount of data to test hypotheses about the proposed intervention, i.e., whether or not it is effective; for which patients it is most effective; and whether or not it is an improvement over standard interventions for the same disease. The system described in this application was developed to help researchers achieve each of these major hurdles.

The development of interventions consists of four identifiable stages: target discovery, clinical trial design, pharmacoeconomic assessment, and product distribution/use. Target discovery is the process of finding a biological or cellular mechanism in the biology of the disease process that, if affected or known through testing, alters the course of disease progression. Target discovery identifies both the particular target in the disease biology and the intervention that affects or identifies the target. The pathology of a disease is often so complex that it takes years of research to discover a target leverage point that provides a cure or at least relieves the symptoms. This is clearly one of the most difficult problems facing pharmaceutical research. It is a very labor-intensive and time-consuming stage in which a positive outcome is not assured. It relies on discovering an insight, which happens in due course rather than on a fixed schedule.

Current approaches to target discovery concentrate on standard laboratory experimentation to generate hypotheses and animal trials to further evaluate those hypotheses. These standard approaches are often limited by the knowledge and understanding that the researchers have of the disease biology. Researchers bring to the design of their studies a paradigm or guiding theory that directs the questions they seek to answer. While this top-down approach to target discovery can be very successful if the theory is good, it begs the question of how to develop a theory in the first place. In the absence of a guiding theory, researchers must cull through large bodies of data to develop an initial insight. There are few tools and no standard approaches that support this bottom-up, or data-driven approach to target discovery and the identification of proposed interventions.

The next stage in intervention development involves designing and conducting formal clinical trials of the proposed intervention. Clinical trials typically isolate narrowly on a single variable, e.g., the proposed intervention, and use a control group as a baseline from which the variable is measured. Observations from a clinical trial attempt to draw conclusions from statistical differences between the control and experimental groups. Because of the enormous expense of conducting trials large enough to statistically assess a broad range of variables, these observations often fail to take into account the multivariate, dynamic nature of the patients individually or as a group.

Clinical trials are very data intensive, time-consuming and costly. The goal is to gather enough evidence to support the claims of the intervention's efficacy and to obtain regulatory approval. A typical cycle for a clinical trial may take several years. For example, designing the trial may take six months, performing the trial may take a year, and analyzing the results may take yet another six months. After years of testing, the results may still be unexpected or difficult to interpret.

The design of a clinical trial is limited by the researchers' knowledge of the underlying disease process, how patient attributes affect it, and how the proposed intervention, the disease biology, and the patient attributes interact. Without this knowledge, designers might test patient types for which the intervention is ineffective or has adverse effects. Additionally, they might design an inappropriate regimen for delivering the proposed intervention. Either of these alternatives could lead the research team to conclude that a proposed intervention has no effect, when in fact it is very effective for the right patients with the correct delivery schedule. Alternatively, without this knowledge, a positive clinical trial might lead the research team to conclude that a proposed intervention has a profound effect without a full understanding of the possible limitations.

Much research is underway to develop tools to support the clinical trial design process. Most of these tools concentrate on analyzing the merit of alternative designs given the researchers' assumptions about the pharmacokinetics and pharmacodynamics of an intervention. Given the appropriate assumptions, these tools help the researchers assess the risks of the clinical trial design, select the appropriate dose requirements, and reveal the statistical characteristics of the proposed study. Thus, researchers must have considerable prior knowledge of the intervention effects, including knowledge of the effects of the intervention on the disease biology and of the efficacy of alternative regimens for delivering the intervention at the biological level. They take the effects of the intervention on different patient types as a given, and proceed to evaluate competing clinical trial designs for their statistical power. Again, this approach is based only on a top-down methodology that does not support clinical trial design by exploration of biological effects of a proposed intervention on patient types. No currently available tools support the development of clinical trial design by a data driven analysis of the patient attributes which are efficaciously effected by a proposed intervention.

The third stage in the development of interventions, pharmacoeconomic analysis, involves analyzing the benefits of the proposed intervention relative to standard, existing interventions. The pharmaceutical industry is still grappling with how to adequately evaluate the pharmacoeconomic benefit of a potential product and there are no established methods for conducting pharmacoeconomic analysis. Many pharmaceutical companies, as well as the FDA, recognize the need to establish standard procedures for generating claims about the relative effectiveness of competing products, but the methodologies that have been used are extremely expensive, involving comparative clinical trials. To date, methods of evaluating relative clinical outcomes and quantifying quality of life differences between competing intervention scenarios have not been rigorously formalized. The computational methodologies that do exist involve mining large databases of clinical use data to find patterns that can support effectiveness claims. However, these are post hoc approaches; there are no standards for estimating pharmacoeconomic value during the intervention development process. As a result, companies may invest a large amount of money bringing a product to market that cannot achieve an adequate market share to justify the development expense.

The final stage in the development of interventions is product distribution and use. This process involves bringing knowledge and information about the new intervention to the practitioners and patients in order to educate them about the processes underlying the disease, the expected changes in the patient's manifestation of the disease over time (i.e., the disease progression), and the effects of alternative interventions in the disease progression and the patient's overall outcome, including the patient's resulting quality of life and cost. This process draws on the data that supported the target discovery, clinical trials, and pharmacoeconomic analyses to help practitioners and patients make informed decisions about the use of the product.

Traditional approaches to product distribution include developing brochures and pamphlets that present the benefits of the new product and discuss its use. These approaches emphasize the new product and seldom offer unbiased comparisons to existing methods and practices. In addition, companies seldom develop materials to support patient education. However, automated support for practitioner education that clearly presents the disease progression over time for specific patient attributes, and further shows the benefits and limitations of a new intervention is not now currently available. Without automated support for this process that combines and synthesizes all sources of existing data into a meaningful clinical interpretation of estimated disease progression for a specific patient over time and that provides a comparison with existing intervention practices, companies are handicapped in their ability to explain the benefits of their new intervention to potential users and to indicate when it is most effective. Thus, it is difficult to bring the new product to the appropriate constituencies.

A need clearly exists to support, speed, and improve the four major stages of developing disease interventions. The present invention overcomes prior limitations by supporting the collection, storage, and analysis of the data targeted at each of the major hurdles in the development process from discovery to commercialization. The outcome achieved by the present invention aids the discovery of proposed interventions to support therapies and/or medical tests, the design of relevant clinical trials for the proposed intervention, a comparison of the benefits of the proposed intervention to existing practices, and the education of patients and practitioners in the appropriate use of the new intervention to support product commercialization.

SUMMARY OF THE INVENTION

The present invention, as embodied in the Integrated Disease Information System, supports, speeds, and improves the four major stages in the development of interventions. Users of the system may reap large financial benefits because the system streamlines the process of searching for a suitable intervention, designing clinical trials, evaluating the potential market and consumer benefits of the proposed intervention over current methods and practices, and designing marketing, sales, and educational aids for practitioners providing the proposed intervention and patients receiving it.

The dynamic, computer-based system of the present invention receives user provided, or database stored data relating to biological parameters, disease measures, patient characteristics, analyzes biological findings and hypotheses, and outputs the results of the analyses to support identification of targets and interventions, the design of clinical trials, the pharmacoeconomic analysis of interventions, and the presentation of disease progression information. The analyses may be based on data generated by models that simulate the disease process at the cellular and subcellular levels. The analyses may also be based on other sources of data, such a legacy databases, clinical trials, and expert knowledge. The present invention provides an interface to assist in identifying proposed interventions, developing a better understanding of key biological mechanisms, assessing the potential for influencing important clinical outcomes, evaluating the pharmacoeconomic benefit of the proposed intervention, and projecting disease outcomes across time under various intervention scenarios with varying risk factors.

The Integrated Disease Information System embodies an architectural framework that supports the entire intervention development process. This framework not only divides the development effort into four discrete steps (target discovery, clinical trial design, pharmacoeconomic analysis, and product commercialization and education), but also provides a unique methodological approach to performing each of the steps.

Target discovery and clinical trial design can both be conceptualized within an experimental paradigm. Experiments enable a researcher to discover a proposed intervention, and a clinical trial is an experiment to test the efficacy of the proposed intervention. Conventionally, researchers approach these tasks in a top-down manner, using a theory or other governing principle to direct the search for an intervention and design a clinical trial. The Integrated Disease Information System inverts the process and replaces it with a bottom-up or data-driven approach that enables the user to efficiently explore and discover complex relationships between patient attributes, biological parameters, disease progression measures, and intervention attributes. The Integrated Disease Information System provides the user a method of examining a large amount of data to support target discovery and clinical trial design.

In one embodiment, the Integrated Disease Information System provides four primary modules, called Explorers, which assist the user in understanding disease progression, identifying interventions, designing clinical trials, and developing disease progression educational information. The system is coupled to various types of data sources, such as expert systems, simulation environments, clinical data, and the like. Each of the Explorers supports a data driven exploration of their respective application areas, allowing a user of the system to explore the relationships between various patient attributes, intervention attributes, biologic parameters, and other data in the data sources. This exploratory approach to intervention development results in reduced costs and development to the intervention developer, along with increased capacity to demonstrate the efficacy of an intervention to various constituents, including payers, practitioners, and patients.

With respect to identification of targets and proposed intervention, the present invention provides a data driven methodology for identification of targets and interventions, and a Target Discovery Explorer that supports the methodology. To perform target discovery, the user of the system need not create any models of disease progression or intervention operation at a cellular or biologic level. Rather, this information is directly or indirectly captured in the underlying data sources, such as through knowledge acquisition from experts in the relevant medical or biological field as embodied in an expert system, literature databases, clinical trial databases, simulation modeling, or the like. The user queries these data sources with variations in patient attributes, biologic parameters, intervention attributes. These various inputs are processed against the various appropriate data sources, to provide to the user outputs indicating resulting changes in disease progression parameters associated with the disease progression. The user explores the biologic parameters, patient attributes, and intervention attributes in this manner to identify a proposed intervention that efficaciously affects the disease progression, or measures the disease progression.

The Target Discovery Explorer supports this methodology by providing a Biologic Manipulation Tool that receives the various user inputs and queries the data sources and determines the resulting changes in the disease progression, and a Biologic Change Evaluation Facility that provides various forms of visualization of the resulting changes in disease progression.

In supporting clinical trial design, the present invention differs substantially from conventional approaches in that it helps researchers to develop information about the effects of a proposed intervention at the biological level (e.g., what patient types exhibit the best response to which form of intervention). The present invention enables researchers to evaluate how patient attributes affect the impact of the intervention at the biological level. The biological information is used to develop a proposed clinical trial design in terms of the patient types that should be included in a clinical trial and the attributes of the patient or intervention that need to be controlled for in the clinical trial design. More particularly, the present invention enables the user to select various combinations of biological parameters, patient attributes, and intervention attributes, apply these selections to the underlying data sources to determine which combinations of parameters and attributes are demonstrative of the efficacy of the intervention. In this manner, the user can determine the impact of various clinical trial designs prior to actual implementation of the clinical trial, to determine the likelihood of useful results. In contrast, in conventional clinical trial design, the biological parameters, patient attributes and intervention attributes are assumed, instead of being analyzed as part of the clinical trial design itself. By providing a data driven exploration of the alternative clinical trial design factors, the present invention enables a user to effectively identify attributes for inclusion in a clinical trial, and attributes which are not useful to test. This yields increased value in the clinical trial results, faster clinical trial design, and reduced costs.

The Integrated Disease Information System embodiment of the present invention supports clinical trial design with a Clinical Trials Explorer, including a Patient Type Efficacy module and a Clinical Trial Design module. The Patient Type Efficacy module is for determining the impact of an intervention on a specific patient type. The module receives user inputs of a specific combination of patient attributes, intervention attributes and biologic parameters, queries the data sources, and displays the resulting effects on the disease progression from the combination of factors. The Clinical Trial Design module is for comparing the disease progression for various types of patients that are to be part of a potential clinical design. This module takes as inputs the various combinations of patient attributes to be studied and the proposed intervention, and simulates the disease progression for the various groups over time given the intervention. This enables the user to compare the efficacy of an intervention with existing standard interventions.

The present invention supports pharmacoeconomic analysis on two levels. First, it provides a method for collecting and representing expert knowledge about how to determine the relative benefit of a proposed intervention for three different groups, the patient, the practitioner, and the payer or insurer, and it recognizes that a proposed intervention should be beneficial for each of the groups in order to be successful. Secondly, it provides a method for encoding the expert knowledge and using it to calculate the pharmacoeconomic benefit of the proposed intervention. The Integrated Disease Information System embodiment of the present invention uses these underlying sources of data, and enables the user to determine the relative cost-benefits of a proposed intervention relative to standard interventions, as a function of payer attributes such as patient quality of life and participation factors, and practitioner attributes for practitioners providing the proposed intervention, such as practice type and size, and insurance coverage, and payer attributes, such as intervention costs, and future intervention requirements.

In application, the user provides to the system various inputs for these attributes, and the system determines, either qualitatively or quantitatively, outcomes for these various constituents under both the proposed intervention and under standard inventions. This information enables the user to evaluate the commercialization aspects of a proposed intervention. In the Integrated Disease Information System embodiment, the present invention provides this functionality in a Pharmacoeconomic Explorer that includes a Patient Outcome Analysis module, a Practitioner Outcome Analysis module, and a Payer Outcome Analysis module.

Finally, the Integrated Disease Information System conceptualizes product commercialization as a process of patient and practitioner education of the disease progression, and particularly disease progression as impacted by the proposed intervention, standard interventions, or no intervention at all, and the particular attributes of patient type. This approach differs from traditional approaches employed in marketing materials, which are static and emphasize only a single product, and do so without taking account of specific patient attributes. The approach used by the Integrated Disease Information System is not designed as a sales technique or mere marketing literature. Instead, it enables the creation of a disease progression tutorial of information for practitioners or patients about the effects of the proposed intervention on the disease progression, in terms of the underlying biology of the disease process and a comparison with existing interventions. It also supports the practitioner in developing a good mental model of the relative outcomes of alternative interventions for a specific patient. And, it provides the practitioner with a good understanding of the relative pharmacoeconomic benefit to the patient in terms of cost and overall quality of life. Patients receive a clear understanding of the likely outcomes of alternative courses of intervention. They receive an explanation that supports their decision making process by providing them an appropriate representation of this disease process and how an intervention would affect it. Finally, the Integrated Disease Information System provides patients an understanding of the relative benefits of alternative interventions in terms of cost and quality of life.

The present invention supports these features by enabling a user to input patient attributes for a specific patient type, intervention attributes, and a relevant time period for determining the disease progression. These various attributes are used to query the underlying data source that captures disease progression information for various patient attributes, and at different stages of disease progression. This information is dynamically used to model the disease progression over time for the specific patient, such as showing changes in various disease progression measures, and graphically illustrating, either through charts, plots, or anatomical representations, the disease progression over time. In this way, a practitioner can demonstrate to a patient the disease progression that patient will experience both with and without an intervention, thereby improving the patient's and the practitioner's understanding to the disease progression in that patient.

The Integrated Disease Information System embodiment of the present invention supports creation of disease progression information in this way through a Disease Progression Explorer that includes a Patient History module, a Disease Progression Evaluation module, and Disease Progression Tutorials. The Patient History module receives user inputs of the specific patient attributes, and intervention attributes (if any) to be projected for a disease, and queries the data sources for disease progression measures that result from the specified inputs. The Disease Progression Evaluation module uses the resulting disease progression measures, and projects these measures onto various graphical or anatomical representations, thereby showing the specific effects of the disease on the patient. The Disease Progression Tutorials are used to provide background tutorial information on the biology of a disease and mechanism of an intervention.

The various methodological aspects of the invention, along with their various embodiments in the individual Explorers, can be used in isolation or in various combinations, thereby further increasing their utility to different classes of users. For example, a pharmaceutical company which has a proposed intervention under development, may use just the Clinical Trials module to explore the factors of attributes desirable for inclusion in a clinical trial, without using the Target Discovery Explorer to identify an intervention (since it already has one). Alternatively, the company may have already developed a model of disease biology in the form of an expert system or simulation model, and couple this data source to the Target Discovery Explorer to explore this model to identify a target in the biology and a proposed intervention to effect or measure that target. Alternatively, a company which already has identified a proposed intervention and seeks to market it, may use the Pharmacoeconomic Explorer to demonstrate to payers that the proposed intervention is more cost effective than standard interventions, and thereby should be included in their treatment plans and insurance plans. This type of company may provide the Disease Progression Explorer to practitioners so as to enable them to understand the disease progression as impacted by its proposed intervention, and as compared to standard interventions (or no intervention at all), and also to enable the practitioners to use the Disease Progression Explorer with their individual patients to educate such patients. Thus, the various uses and implementations of present invention can be beneficially employed by themselves, or may be combined into an integrated system and method.

Other objects and advantages of the present invention will become apparent from the following detailed description when viewed in conjunction with the accompanying drawings, which set forth certain embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an architecture that may be used to implement the apparatus and methods defined by the present invention;

FIG. 2 is a block diagram showing the Integrated Disease Information System in accordance with the present invention;

FIG. 3 is a flow chart showing the sequence of events that advantageously utilizes the disclosed Integrated Disease Information System;

FIG. 4 is a block diagram showing a typical Explorer architecture;

FIG. 5 shows a block diagram of the Target Discovery Explorer;

FIG. 6 shows a flow chart of processing performed by the Biologic Manipulation Tool;

FIG. 7 shows a sample user interface for the Biologic Manipulation Tool;

FIG. 8 is a flow chart of processing performed by the Biologic Change Evaluation Facility;

FIG. 9 shows an example user interface for the Biologic Change Evaluation Facility;

FIG. 10 is a block diagram showing the Clinical Trials Explorer;

FIG. 11 shows a flow chart of the overall processing of the Patient Type Efficacy Module;

FIG. 12a and FIG. 12b show examples of the graphical user interface for the Patient Type Efficacy Module;

FIG. 13 is an example graphical user interface produced by the Patient Results Tool;

FIG. 14 is an example graphical user interface of the Clinical Visualization Tool;

FIG. 15a and FIG. 15b together form a flow chart showing the processing performed by the Clinical Trial Design Suite;

FIG. 16 shows an example graphical user interface for the Study Design Tool;

FIG. 17 shows an example user interface showing study results from the Trial Analysis Tool;

FIG. 18 is a graphic interface generated by the Trial Analysis Tool showing correlations between patient variables and disease outcomes;

FIG. 19 is a block diagram of the components of the Pharmacoeconomic Explorer;

FIG. 20a and FIG. 20b together form a flow chart of the process of producing a pharmacoeconomic analysis;

FIG. 21 provides an example user interface that receives data/information about the patient and practitioner to support the pharmacoeconomic analysis;

FIG. 22 is an example of the user interface generated by the Pharmacoeconomic Explorer and showing a summary of the pharmacoeconomic analysis for the patient, practitioner, and payer in a report format;

FIG. 23 is a flow chart showing a sequence of analyses designed to determine a categorical designation based on the patient's presenting symptoms and history in order to determine what the standard treatment regimen is for the specified patient;

FIG. 24 shows examples of an influence diagram used to analyze information the patient outcome;

FIG. 25a is an example of an influence diagram used to analyze information the practitioner outcome;

FIG. 25b is an example of an influence diagram used to analyze information the payer outcome;

FIG. 26 is a block diagram of the components of the Disease Progression Explorer;

FIG. 27 is a flow chart of the processing of the Patient History Tool;

FIG. 28 is a flow chart of the processing of the Disease Progression Evaluation Facility;

FIG. 29 is a sample user interface to the Patient History Tool and Disease Progression Evaluation Facility;

FIG. 30 is a sample user interface to the Disease Progression Tutorials; and

FIG. 31 is a block diagram showing possible components of the Data/Information Source.

FIG. 32 depicts the functional layout of Integrated Disease Information System 10.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

A detailed description of a preferred embodiment of the present invention is disclosed herein. It should be understood, however, that the disclosed embodiment is merely exemplary of the invention, which may be embodied in various forms and functions. Therefore, the details disclosed herein are not to be interpreted as limiting, but merely as the basis for teaching one embodiment of the invention.

It should be understood that, as used herein, the meaning of the term "interface" is not limited to a graphical user interface. Rather, the term, as used herein, is intended to broadly refer to an interface between a user and other computer system components, as well as between such components and other applications. Such an interface may include a graphical user interface as well as software and hardware components internal to the system that do not directly interact with the user.

OVERVIEW

The Integrated Disease Information System, described in detail below, assists a user in overcoming four principle hurdles in the intervention development process: 1) drug, device, regimen, or test (hereafter collectively referred to as "interventions") discovery; 2) clinical trial design; 3) pharmacoeconomic analysis; and 4) disease progression analysis in support of practitioner and patient education, and product commercialization. These functions are critical steps in the process of bringing a new product to market.

Intervention discovery addresses the issue of identifying key manipulation points in the biology of a disease that will halt or alter its progression for the purpose of developing a proposed test, device, regimen, drug, or other therapeutic regimen. A clinical trial answers the question of whether the proposed intervention has an effect on a selected disease process. The answer to this question may result in a qualified "yes," in which the intervention has varied levels of efficacy for different types of patients. Pharmacoeconomic analysis compares the proposed intervention to current standard practice for treating the disease or testing for risk factors in order to determine whether there is any advantage to the new intervention in terms of improved disease progression and/or cost and patient quality of life. Pharmacoeconomic evaluation in accordance with the present invention uses expert knowledge to evaluate a proposed intervention in relation to existing products or procedures to determine outcomes for medical providers, patients, and insurance providers. Disease progression analysis draws on data to project the course of the disease over time for different combinations of patient risk factors and/or various therapeutic regimens. This analysis helps practitioners and patients understand the best use of the new intervention for the patient. The Integrated Disease Information System disclosed herein includes a set of tools that support target discovery, clinical trial design, pharmacoeconomic analysis, and disease progression analysis.

INTEGRATED DISEASE INFORMATION SYSTEM ARCHITECTURE

FIG. 2 is a block diagram showing the Integrated Disease Information System 10 in accordance with the present invention. The Integrated Disease Information System integrates data received from Input/Output 18, Data/Information Source 20, and Results Database 22 to provide reliably appraised data of a desired biological system. Integrated Disease Information System 10 is comprised of four system components: Target Discovery Explorer 12, Clinical Trials Explorer 14, Pharmacoeconomic (PE) Explorer 16, and Disease Progression Explorer 17. These system components communicate with each other. They exchange data and other information, as indicated by the bidirectional arrows. The particular implementation of communication between software components will vary considerably depending on the software and hardware utilized with specific applications of the present invention.

Target Discovery Explorer 12 processes data and information from Data/Information Source 20 and Input/Output 18 to support discovery of a potential intervention. It stores the results of the analysis in Results Database 22, making it available to Clinical Trials Explorer 14 for analysis. Alternatively, it could send the results directly to Clinical Trials Explorer 14. Clinical Trials Explorer 14, using the data developed by Target Discovery Explorer 12 and additional data as necessary and available, develops data to compare the proposed intervention in patients with different attributes and risk factors under different intervention regimens. The proposed intervention is compared to results achieved through no therapy (placebo) or alternative interventions and across different patient risk factor profiles. The patient information developed by Target Discovery Explorer 12 and Clinical Trials Explorer 14 is made available to PE Explorer 16, either directly or via Results Database 22, to develop further data and assess differences between a proposed intervention and current standard treatments or testing practices. The information developed by Target Discovery Explorer 12, Clinical Trials Explorer 14, and PE Explorer 16 is transferred to Disease Progression Explorer 17 to project the course of the disease across time on an appropriate graphical representation for various patient risk attributes and alternative interventions.

It should be understood that a person is involved in each step of the process provided by the present invention. The system does not function entirely by itself; that is, decisions regarding a variety of parameters are input by an individual and the system uses the parameters to generate and display data and information to facilitate the decision making process of the individual. The user interacts with each software component to select variables of interest for analysis. The Explorers convert the user's input into an appropriate query of the data source(s) and retrieve the results of the query. The user then selects how to view the retrieved information from a set of predefined options. These options are specific to each Explorer and support the user in the corresponding task. Each Explorer manipulates and integrates the data and displays it to the user in the selected fashion. The user makes conceptual judgments regarding substantive issues and provides parameters to the system to retrieve, compare, and analyze the results. Users can select variables and views any number of times in support of their decision making task. One of the benefits the invention provides is a fully integrated system for extracting, manipulating, and analyzing data from various sources in support of each of the steps of intervention development.

It is also contemplated that Explorers 12, 14, 16, and 17 could communicate data and information by buffering it in either Data/Information Source 20 or Results Database 22 as well as directly. Each Explorer develops a particular type of data that can in turn be used by a system user or another Explorer. Each Explorer can also obtain the data necessary for operation from an appropriate data source in addition to or instead of from another Explorer. It should also be kept in mind that FIG. 2 is intended to show a broad overview of the Integrated Disease Information System and that the specifics of the system may be varied without departing from the spirit of the present invention.

FIG. 1 shows an example computer architecture on which the Integrated Disease Information System 10 may be implemented. The Integrated Disease Information System may be implemented on any standard computing platform that includes a processor 82, input/output device 18, display device 92, random access memory 88, and memory storage 96. It is implemented as a program that runs on standard system software, including an operating system. It can be implemented using a variety of standard computer languages, development tools, database tools, and graphical user interface development tools as needed to implement the functions disclosed herein.

The components of Integrated Disease Information System 10 communicate with Input/Output 18. Input/Output 18 may include, but is not limited to, video displays, mice, modems, keyboards, light pens, joysticks, and communication adapters. Input/Output 18 represents any device capable of exchanging information between a user and Integrated Disease Information System 10. Each of Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and Disease Progression Explorer 17 receives information from and sends information to Input/Output 18.

The components of Integrated Disease Information System 10 also communicate with Data/Information Source 20. Data/Information Source 20 (discussed in detail below) stores the data and information used by Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and Disease Progression Explorer 17 in creating and analyzing biological data and information. Data/Information Source 20 stores "source" data and information. "Source" data and information are collected by the user of the system or another outside source. This data may include, but is not limited to, experimental data, standard clinical trial data, practice-based data, expert opinion/knowledge, simulation results, or other sources of relevant data and/or information.

Finally, the components of Integrated Disease Information System 10 communicate with Results Database 22. Results Database 22 (discussed in detail below) stores "developed" data and information. "Developed" data and information are created by the components of Integrated Disease Information System 10 as the components receive, analyze, and generate data and information. It should be noted, however, that intermediate, "developed" data could also be buffered or stored, in whole or in part, by Data/Information Source 20. The data and information developed by Integrated Disease Information System 10 and stored in Results Database 22 is that which the user requested the system to generate.

Integrated Disease Information System 10 may be implemented with a programming language like C++, or by using available graphical user interface and application design software packages. For example, Integrated Disease Information System 10 could be implemented using Microsoft Visual Basic.RTM. and Microsoft Access.RTM.. The principle interactive components of the software are the user interfaces, which display the results of Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and Disease Progression Explorer 17, as well as provide a window into Data/Information Source 20 and Results Database 22. These and other features are discussed in detail below.

Integrated Disease Information System 10 is shown for illustrative purposes only as comprising, in a unitary manner, Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and Disease Progression Explorer 17. It is also contemplated, however, that each of the components could stand alone, individually occupying the position now occupied by Integrated Disease Information System 10. That is, one Explorer does not necessarily require the others, but certain capabilities are provided by a system in which each of the components shown interacts and exchanges information with other components. For example, a system could include Data/Information Source 20 containing information typically generated by Target Discovery Explorer 12 as well as additional information as necessary and available. Such a system would support a stand alone Clinical Trials Explorer 14 which interacts with Input/Output 18, Data/Information Source 20, and Results Database 22.

FIG. 32 depicts the functional layout of Integrated Disease Information System 10. Essentially, Integrated Disease Information System 10 receives input 266 from the user, queries 268 a data source in response to the user input, analyzes 270 the data retrieved from the data source, and displays 272 the data to the user in a variety of formats in order to support 274 the user's high level functional requirements. The user inputs biologic parameters of the biological process or system under consideration, patient attributes, and attributes of a proposed intervention that may alter disease progression in the patient, given the patient's attributes. Integrated Disease Information System 10 translates this information into an appropriate query of the source data stored in Data/Information Source 20, submits the query, and retrieves the results. The source data contains information about the disease process under study in one or more of a variety of electronic formats. The retrieved data depict disease progression over time on the relevant disease progression measures. Integrated Disease Information System 10 analyzes the retrieved data in a number of ways. The retrieved data may be manipulated to infer disease progression in the patient, mapped to clinically-significant signs and symptoms of the disease, and/or interpreted to estimate the pharmacoeconomic benefit of the proposed intervention. After Integrated Disease Information System 10 analyzes the data, it formats the data for presentation in a user-selected format. The available formats span a wide variety of options that are suited to supporting the user's functional needs. Different presentation formats support different functional requirements and the formats available are specific to each Explorer. Overall, Integrated Disease Information System 10 supports the four principle functions in intervention development: target discovery, clinical trial design, pharmacoeconomic analysis, and patient and practitioner education in support of product commercialization.

OVERVIEW OF OPERATION

FIG. 3 is a flow chart showing a scenario of events advantageously utilizing Integrated Disease Information System 10. Based on the user's input of various biological parameter values related to the disease process and patient type data, Integrated Disease Information System 10 queries 30 Data/Information Source 20 to produce 32 an estimate of disease progression. The estimate of disease progression is a model of the disease outcome over time within the supplied biological parameters. This model reflects the average course of disease progression in a given patient type (i.e., patients sharing common attributes) under the conditions specified by the user. Once the estimate of disease progression has been established, a user continues to interact with Integrated Disease Information System 10 to identify 33 target leverage points in the disease biology that alter the course of disease progression.

This first process of the Integrated Disease Information System 10, implemented by Target Discovery Explorer 12, provides a user with methods of altering biological parameters in the disease process to identify a proposed intervention. For example, if directed by the user to do so, Target Discovery Explorer 12 queries 30 Data/Information Source 20 and develops information showing dynamic changes in the progression of a disease if a certain cytokine (i.e., protein produced and released by cells that signals other cells) or set of cytokines is blocked. This assists a user in gathering information about combinations of biological changes that might yield a good disease prognosis. An alternative method for using Target Discovery Explorer 12 to identify an appropriate intervention is to start with the known effects of an intervention on human biology. The user then enters the known biological effects of various interventions into Target Discovery Explorer 12, which then queries 30 the data source to see which intervention provides an efficacious disease progression. Target Discovery Explorer 12 may alternatively assist a user in understanding markers of accelerated disease that would reliably screen patients for the disease. These approaches to using Target Discovery Explorer 12 help a user generate hypotheses about potential interventions for a given disease.

It should be kept in mind that the data and information in Data/Information Source 20 reflects the analyses being performed by the components of Integrated Disease Information System 10 (discussed in more detail below). For example, Data/Information Source 20 may include a simulation model of biological processes or other processes related to the analyses performed by the components of Integrated Disease Information System 10. Such a simulation model would receive particular model parameters from the user or system, and run several simulations to derive an estimate of disease progression based on different input parameters representing different patient types. Data/Information Source 20 may include any and all available sources of data to support the analyses performed by the different Explorers.

Once an intervention has been identified 33 by the user with the assistance of Target Discovery Explorer 12, Integrated Disease Information System 10 helps the user design 34 an appropriate clinical trial or trials via Clinical Trials Explorer 14. Clinical Trials Explorer 14 uses the locus of change information (i.e., the biological changes that yield efficacious disease progression) and/or intervention information to simulate clinical trials to search for patient attributes that might impact the intervention effect, either weakening or strengthening it. For example, a user might hypothesize that an intervention, while effective in the general population, will have limited effect on types of patients, such as those with diabetes mellitus. Clinical Trials Explorer 14 receives user input regarding a proposed intervention and particular patient types, and uses the input to simulate 36 and test a variety of patient types by querying Data/Information Source 20 to evaluate the disease progression for patients receiving the experimental intervention compared to patients receiving a placebo, or an alternative intervention, for each relevant patient type. Clinical Trials Explorer 14 provides the user with information about ranges of possible intervention effects, including patient types for which the intervention might have no effect or result in a poor effect, i.e., more rapid disease progression.

Clinical Trials Explorer 14 then sends the results of the clinical trial simulation(s) to Results Database 22 or directly to PE Explorer 16 for the next step, pharmacoeconomic analysis. Pharmacoeconomic analysis 38 compares a proposed intervention to current standard practice(s) for each patient type. Based on expert rules collected and implemented as part of PE Explorer 16 during development of Integrated Disease Information System 10, pharmacoeconomic analysis evaluates the outcome for a particular type of patient, with a particular version of the proposed intervention, in terms of cost and quality of life, and yields an evaluation of overall patient satisfaction. Pharmacoeconomic analysis also evaluates outcomes for the medical practitioner (i.e., the provider) and the insurance carrier (i.e., the payer), using rules supplied by human experts in the disease domain under investigation and implemented in PE Explorer 16 code.

Disease Progression Explorer 17 receives the data and information developed by Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and/or other data sources either directly or via Results Database 22, or perhaps Data/Information Source 20, to produce an estimate of disease progression. A medical practitioner can input information about the patient attributes, such as patient history, risk factors, and intervention options, and Disease Progression Explorer 17 graphically projects 40 the course of the disease for the patient under various scenarios. A textual explanation of the basis and meaning for the projection is provided to clarify the results for the practitioner and patient and to support practitioner and patient education.

In summary, the preferred embodiment of the Integrated Disease Information System, in accordance with the present invention, includes four distinct components: Target Discovery Explorer 12, Clinical Trials Explorer 14, PE Explorer 16, and Disease Progression Explorer 17. Input/Output 18 receives input from a user, Data/Information Source 20, Results Database 22, and each Explorer; and displays results to the user in the form of graphical and textual information. Data supplied by Data/Information Source 20 and Results Database 22 is also analyzed by the four components of Integrated Disease Information System 10. Target Discovery Explorer 12 helps the user identify a potential intervention. Clinical Trials Explorer 14 supports analysis of the results of the proposed intervention across patient types. PE Explorer 16 compares the benefits of the proposed intervention to current standard practice. Finally, Disease Progression Explorer 17 projects the course of the disease across time in an effort to educate the patient and practitioner to the relative merit of alternative intervention regimens. The results of these analyses are stored in Results Database 22. The following sections discuss these components of the invention in detail.

EXPLORER ARCHITECTURE

FIG. 4 is a block diagram showing the architecture of a typical explorer. Each Explorer 224 is primarily comprised of Query Processor 226 and Results Synthesizer 228. Each of Query Processor 226 and Results Synthesizer 228 may communicate with other Explorers via Communication Path 230. Alternatively, each of Query Processor 226 and Results Synthesizer 228 may communicate with other Explorers by selectively buffering data and/or information in Data/Information Source 20 or Results Database 22.

Query Processor 226 interacts with a user to receive a variety of data and/or information related to an Explorer. Query Processor 226 receives user input and translates it into one or more queries of Data/Information Source 20 and/or Results Database 22. The number and nature of the queries depends on the sources of data/information in Data/Information Source 20. For example, if Source 1 is a relational database, a relational query would be formed. If Source n is a simulation model, the model would be run with the input from the user required to set up the parameter values for the simulation. If the data/information from the user is not in the proper form for directly formulating a query for Data/Information Source 20, Query Processor 226 infers a query to match as closely as possible the format and content of the Source being queried. The details of how this is accomplished depend on the implementation and the characteristics of the data source. In general, in order to do this, the software code for Query Processor 226 contains algorithms for translating the user's input into a query language appropriate for the data source. A query is generated dynamically by the code and the query is submitted to the data/information source. Queries may also include or use data and information from another Explorer, receiving data/information via 230, and/or retrieving data/information from Results Database 22.

In order to perform its functions, Query Processor 226 includes four software components: user interface, query generation, query submission, and results collection. User input is accomplished through the user interface. The interface is designed and constructed such that all necessary information for a query is collected from the user. The query generation component dynamically constructs a query from the collected information in the form required by the data source. Methods of constructing queries dynamically for a variety of types of data sources are well known in the art. The query submission component sends the query to the appropriate data source and directs the data source to conduct the relevant search. Finally, results collection component receives and stores the results of the query returned by the data source.

Once Query Processor 226 receives the requested data and/or information from Data/Information Source 20, the data and/or information are sent to Results Synthesizer 228 for further processing. Results Synthesizer 228 is responsible for synthesizing the results from the data source(s) into a presentation to the user in the format requested by the user from the options available in the particular Results Synthesizer 228. Results Synthesizer 228 has two software components: a data analysis component and a presentation component. The data analysis component manipulates the data in a manner specific to each Explorer and these analyses are described in the following sections. The presentation component is again specific to the Explorer and includes one or more methods of displaying the data to the user in a format that supports the user's decision making process.

DISEASE EXAMPLE

An example from osteoporosis illustrates one possible use of the system disclosed herein. Osteoporosis is a life long disease process but most often becomes clinically evident in post-menopausal women. The cause of osteoporosis can be traced to changes in the bone remodeling process that result from decreased estrogen, decreased mechanical loading on the bone, and a variety of other factors that combine to reduce the density of the bone and increase the likelihood of bone fractures. Treatments of the condition include estrogen replacement or bisphosphonate therapy.

Bone is a living structure that undergoes constant remodeling throughout the life of an individual. The principle cells involved in bone remodeling are osteoblasts, that build bone, and osteoclasts, that break it down. The action of these two cells is tightly coupled in a normal individual to maintain healthy bone, including optimal remodeling rates and bone mineral density levels. Any uncoupling of the action of these two cell types can cause suboptimal bone mineral density and weaken the bone, making fracture more likely.

Estrogen is related to osteoclast activity, such that decreases in estrogen increase bone breakdown rates, leading to weakened bone. Estrogen supplements reestablish more optimal bone remodeling patterns and can be started at menopause to prevent osteoporosis. However, estrogen supplements have decided drawbacks, including increased risk of breast and uterine cancer. These insights have emerged through years of studying estrogen supplements. Alternatively, bisphosphonate therapy can be used for patients who have contraindications for estrogen replacement therapy, e.g., a family history of breast cancer. However, bisphosphonate therapy is only prescribed once the disease is clinically evident. Clearly, a therapy that has fewer potential side effects and can be used to prevent the disease would be desirable.

Integrated Disease Information System 10 could help a user discover and validate an intervention for osteoporosis in a systematic fashion. Target Discovery Explorer 12 enables the user to identify a locus in the biology of bone remodeling or more generally in the osteoporosis process that would retard bone density loss. Once the target is identified, Clinical Trial Explorer 14 enables the user to test a proposed intervention that affects that target in different patient types to identify those patients for whom the proposed intervention would be most effective. PE Explorer 16 enables the user to compare the effectiveness of the proposed intervention against estrogen replacement and bisphosphonate therapy for patients, practitioners, and payers. Finally, Disease Progression Explorer 17 assists in describing the clinical benefit of the new intervention (e.g., reduced fracture risk) to patients and practitioners in order to facilitate commercialization of the new intervention.

This osteoporosis disease example will be discussed in further detail to elucidate the functions of each Explorer in the following sections.

DATA MODEL

Each of the Explorers supports the user's decision making process at a particular stage of the target development process. The data developed by one Explorer to support the user's decision making can then be used by another Explorer. Table 1 lists the type of decision support and developed data provided by each Explorer.

                  TABLE 1
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    Explorer Data Relationships
    Explorer Decision Support
                             Developed Data
    ______________________________________
    Target   Supports finding a
                             Produces data relating any
    Discovery
             biological target for a
                             proposed intervention to
             potential intervention that
                             measures of disease
             influences the disease in a
                             progression that show the
             positive manner.
                             effect of the intervention.
    Clinical Trials
             Supports the design of a
                             Produces data relating the
             clinical trial for the
                             response of patient types to
             experimental target.
                             the proposed intervention
                             showing which have good
                             outcomes and which have
                             no change or poor disease
                             progression.
    Pharmaco-
             Supports an understanding
                             Produces data pertaining to
    economic of the merit of the proposed
                             patient satisfaction,
             intervention in relation to
                             practitioner satisfaction,
             existing practices.
                             and relative cost
                             information for payer
                             evaluation.
    Disease  Supports patient and
                             Produces data relating
    Progression
             practitioner visualization
                             patient risk factors for the
             and understanding of the
                             disease to clinically
             clinical impact of using a
                             relevant signs and
             new intervention on the
                             symptoms.
             disease progression over
             time.
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                  TABLE 2
    ______________________________________
    Explorer Data Types
    Explorer
            Data Type
    ______________________________________
    Target  Data that links changes in biology, e.g., changes in cytokine
    Discovery
            output by a cell type or changes in patient attributes, to
            changes in disease progression, as measured by selected
            disease progression measures. These data are at a very low
            level of granularity, describing how the cell types involved
            in the disease process function, what they produce, and how
            they affect the disease progression at the cellular on up to
            the clinical level.
    Clinical
            Data that links patient attributes, intervention effects, and
    Trials  disease progression, including data that describes how a
            proposed intervention affects the course of the disease.
            These data are also at a low level of granularity, describing
            how interventions affect the disease at the cellular level and
            how that ultimately affects the disease progression over
            time.
    Pharmaco-
            Data that describes a disease progression for a particular
    economic
            type of patient on different types of interventions: the
            standard practice for that patient and the proposed
            intervention; and data about the cost of therapy, how cost is
            influenced by insurance coverage, and practitioner needs
            and preferences for therapy/test prescriptions. These data
            are at a high level of granularity, describing how different
            patient types respond to different interventions at the level
            of disease signs and symptoms and the resulting quality of
            life
    Disease Data that links patient attributes, intervention effects, and
    Progression
            disease progression to clinically relevant outcomes of the
            disease, for example changes in fracture rates in
            osteoporosis. These data are at several levels of granularity
            and must be sufficient to educate both patients and
            practitioners about the disease process and the key attributes
            that will influence it for the given patient.
    ______________________________________

                  TABLE 3
    ______________________________________
    Example of Raw Data for Target Discovery Explorer
    ______________________________________
    Research Report
              Blocking IL-1 and TNF.alpha. by 10% reduces bone
    #1:       density loss in mice by 40%
    Research Report
              Blocking TNF.alpha. alone does not reduce bone density
    #2:       loss in mice
    Research Report
              IL-6 knockout mice show no bone density changes
    #3:       after ovariectomy
    Research Report
              IL-6 receptor antagonists reduce osteoclast activity in
    #4:       vitro
    Expert Opinion
              Blocking TNF.alpha. and moderately augmenting the
    #1:       growth factors will reduce bone density loss by 25%
    Simulation
              Blocking mast cell degranulation reduces IL-4 thereby
    Result:   reducing bone loss by 10%
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