Abstract:
Systems and methods for analytics on viable patient populations are disclosed. One disclosed method includes displaying a user interface control associated with a patient criteria, receiving a selection of a patient criteria through the user interface control, the patient criteria associated with a characteristic of a patient population, retrieving a subset of patient population source data from a patient population data source, the subset of patient population source data based at least in part on the selected patent criteria, and displaying a graphical representation of a patient population based on the retrieved subset of the patient population source data.

Description:
REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims priority to U.S. Provisional Application No. 61/663,219, filed Jun. 22, 2012, entitled “Systems and Methods for Analytics on Viable Patient Populations;” U.S. Provisional Application No. 61/663,292, filed on Jun. 22, 2012, entitled “Method and System to Manipulate Multiple Selections against a Population of Elements;” U.S. Provisional Application No. 61/663,057, filed on Jun. 22, 2012, entitled “Systems and Methods For Predictive Analytics For Site Initiation and Patient Enrollment;” U.S. Provisional Application No. 61/663,299, filed on Jun. 22, 2012, entitled “Methods and Systems for Predictive Clinical Planning and Design and Integrated Execution Services;” U.S. Provisional Application No. 61/663,398, filed on Jun. 22, 2012, entitled “Systems and Methods for Subject Identification (ID) Modeling;” U.S. Provisional Application No. 61/663,357, filed Jun. 22, 2012; entitled “Methods and Systems for a Clinical Trial Development Platform;” U.S. Provisional Application No. 61/663,216, filed Jun. 22, 2012; entitled “Systems and Methods for Data Visualization.” The entirety of all of which is hereby incorporated by reference herein. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates generally to systems and methods for the creation and analysis of clinical trials. The present invention relates more specifically to systems and methods for analytics on viable patient populations. 
       BACKGROUND 
       [0003]    Clinical trials for molecules that may become pharmaceutical products are complex endeavors that often last for years. Because of this, clinical trials are quite expensive and require careful planning to maximize return on investment. One critical aspect of planning is determining optimal inclusion and exclusion criteria for forming a patient population for the clinical trial. 
         [0004]    Existing tools and methods for analyzing and determining optimal patient population criteria suffer from a number of deficiencies. First, current methods of patient analysis are driven by a single question/single answer paradigm. In addition, once a particular query is formed and submitted, results can take anywhere from thirty minutes to hours or days to arrive. Furthermore, current methods provide text-based analysis of data, but do not provide a simple visual way of representing patient populations. Because of these limitations, clinical trial planners are unable to create “what if” scenarios and quickly receive the results of a “what if” scenario in an easily readable visual form. Therefore, existing methods and systems for analyzing patient populations require a substantial amount of time for trial designers to fine tune the criteria for a population such that they are not overly inclusive or exclusive. 
       SUMMARY 
       [0005]    Embodiments of the present invention provide systems and methods for Analytics Viable Patient Populations. In one embodiment, a three-tier system provides a client application to view and modify criteria related to patient populations, a query building engine for dynamically forming queries based on the criteria supplied by the user, and one or more databases for storing medical records, prescription records, clinical trial data, medical claim information, and other patient information analyzed based on exclusion/inclusion criteria provided by a user. The application in such an embodiment receives the user&#39;s criteria selections, dynamically builds complex queries, submits the queries to the one or more databases, and displays the results from the queries in a simple graphical form. The application is able to provide the user with the impact of the user&#39;s selections on the patient population in an easy-to-read graphical format. Based on the results, the user may determine whether the criteria are too inclusive/exclusive and adjust the criteria. This process may be performed iteratively until the criteria producing an optimal patient population are determined. 
         [0006]    In one embodiment, the user is also provided with a query builder interface—an alternative interface through which a user may visually build and submit complex queries applying inclusion/exclusion criteria to form a prospective patient population. Once again, the application is able to provide the user with the impact of the user&#39;s queries on the patient population in an easy-to-read graphical format. Based on the results, the user may determine whether the criteria are too inclusive/exclusive and adjust the criteria. This process may be performed iteratively until the criteria producing an optimal patient population are determined. 
         [0007]    This embodiment is mentioned not to limit or define the invention, but to provide an example of an embodiment of the invention to aid understanding thereof. Embodiments are discussed in the Detailed Description, and further description of the invention is provided there. Advantages offered by the various embodiments of the present invention may be further understood by examining this specification. 
     
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         [0008]    These and other features, aspects, and advantages of the present invention are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein: 
           [0009]      FIG. 1  is a block diagram illustrating an exemplary environment for implementation of one embodiment of the present invention; 
           [0010]      FIG. 2  is a screen shot of a patient population editor view according to one embodiment of the present invention; 
           [0011]      FIG. 3  is a flowchart illustrating a method for performing patient population analysis and manipulation according to one embodiment of the present invention; 
           [0012]      FIG. 4  is screen shot of a patient population query editor view according to one embodiment of the present invention; 
           [0013]      FIG. 5  is a is a screenshot of an interface for manipulating a patient population criteria element according to one embodiment of the present invention; 
           [0014]      FIG. 6  is a screen shot of a patient population query editor view according to one embodiment of the present invention; 
           [0015]      FIG. 7  is a flowchart illustrating one method for performing patient population analysis and manipulation according to one embodiment of the present invention; 
           [0016]      FIG. 8  screen shot of a patient population query editor view according to one embodiment of the present invention; and 
           [0017]      FIG. 9  is a screen shot of a patient population query editor view according to one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    Embodiments of the present invention provide systems and methods for analytics on viable patient populations. 
       Illustrative Embodiment of the Present Invention 
       [0019]    One illustrative embodiment of the present invention comprises an application for selecting patient population source data and utilizing adjustable criteria to include or exclude patient segments to create a patient population for a clinical trial. The embodiment allows a user to access an application that presents a variety of patient-related adjustable criteria for manipulating the patient population source data to create a prospective patient population. These parameters may include, for example, the gender, age, ethnicity, height, weight, body mass index, lab results, and more. 
         [0020]    Once the user has set the parameters, the user is presented with a graphical representation of the patient population that allows the user to determine whether the selected criteria were overly exclusive or inclusive. For example, the graphical display may include a line chart showing a breakdown of age and gender across the resulting patient population, a pie chart demonstrating a breakdown of ethnicity for the prospective patient population, a thermometer showing the overall amount of patients remaining after the criteria are applied, and/or a Venn Bar diagram showing overlaps between populations to help the user understand the individual impact of each selected criteria on the prospective patient population. 
         [0021]    The process is iterative; the user is able to change the selected criteria to determine the most appropriate criteria for forming an optimal patient population for a given clinical trial. 
         [0022]    This illustrative embodiment neither limits nor defines the invention. Rather, the illustrative embodiment is meant to provide an example of how the present invention may be implemented. 
       Illustrative Environment 
       [0023]    Referring now to the drawings, in which like numerals indicate like elements throughout the several figures,  FIG. 1  is a block diagram illustrating an exemplary environment for implementation of one embodiment of the present invention. The embodiment shown in  FIG. 1  includes a client  100  that allows a user to interface with an application server  200 , web server  300 , and/or database  400  via a network  500 . 
         [0024]    The client  100  may be, for example, a personal computer (PC), such as a laptop or desktop computer, which includes a processor and a computer-readable media. The client  100  also includes user input devices, such as a keyboard and mouse or touch screen, and one or more output devices, such as a display. In some embodiments of the invention, the user of client  100  accesses an application or applications specific to one embodiment of the invention. In other embodiments, the user accesses a standard application, such as a web browser on client  100 , to access applications running on a server such as application server  200 , web server  300 , or database  400 . For example, in one embodiment, in the memory of client  100  are stored applications including a design studio application for planning and designing clinical trials. The client  100  may also be referred to as a terminal in some embodiments of the present invention. 
         [0025]    Such applications may be resident in any suitable computer-readable medium and executable on any suitable processor. Such processors may comprise, for example, a microprocessor, an ASIC, a state machine, or other processor, and can be any of a number of computer processors, such as processors from Intel Corporation, Advanced Micro Devices Incorporated, and Motorola Corporation. The computer-readable media stores instructions that, when executed by the processor, cause the processor to perform the steps described herein. 
         [0026]    The client  100  provides a software layer, which is the interface through which the user interacts with the system by receiving and displaying data to and from the user. In one embodiment, the software layer is implemented in the programming language C# (also referred to as C Sharp). In other embodiments, the software layer can be implemented in other languages such as Java or C++. The software layer may be graphical in nature, using visual representations of data to communicate said data to one or more users. The visual representations of data may also be used to receive additional data from one or more users. 
         [0027]    Embodiments of computer-readable media comprise, but are not limited to, an electronic, optical, magnetic, or other storage device, transmission device, or other device that comprises some type of storage and that is capable of providing a processor with computer-readable instructions. Other examples of suitable media comprise, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, PROM, EPROM, EEPROM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may be embedded in devices that may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript. Furthermore the instructions may be comprise code capable of interfacing with Microsoft Windows Presentation Foundation subsystem for rendering a graphical user interface. 
         [0028]    The application server  200  also comprises a processor and a memory. The application server may execute business logic or other shared processes. The application server may be, for example, a Microsoft Windows Server operating in a .NET framework, an IBM Weblogic server, or a Java Enterprise Edition (J2E) server. While the application server  200  is shown as a single server, the application server  200 , and the other servers  300 ,  400  shown may be combined or may include multiple servers operating together to perform various processes. In such embodiments, techniques such as clustering or high availability clustering may be used. Benefits to architectures such as these include redundancy and performance, among others. 
         [0029]    In the embodiment shown in  FIG. 1 , the application server  200  is in communication with a web server  300  via a network connection  250 . The web server  300  also comprises a processor and a memory. In the memory are stored applications including web server software. Examples of web server software include Microsoft Internet Information Services (IIS), Apache Web Server, and Sun Java System Web Server from Oracle, among others. 
         [0030]    In the embodiment shown in  FIG. 1 , the web server  300  is in communication with a database  400  via a network connection  350  and a network connection  450 . The web server  300  provides a web service layer that, together or separate from application server  200 , acts as middleware between a database  400  and the software layer, represented by the client  100 . The web server  300  communicates with the database  400  to send and retrieve data to and from the database  400 . 
         [0031]    The network  500  may be any of a number of public or private networks, including, for example, the Internet, a local area network (“LAN”), or a wide area network (“WAN”). The network connections  150 ,  250 ,  350 , and  450  may be wired or wireless networks and may use any known protocol or standard, including TCP/IP, UDP, multicast, 802.11b, 802.11g, 802.11n, or any other known protocol or standard. Further, the network  100  may represent a single network or different networks. As would be clear to one of skill in the art, the client  100 , servers  200 ,  300 , and database  400  may be in communication with each other over the network or directly with one another. 
         [0032]    The database  400  may be one or a plurality of databases that store electronically encoded information comprising the data required to plan, design, and execute a clinical trial. In one embodiment, the data comprises one or more design elements corresponding to the various elements related to one or more clinical trials. The database  400  may be implemented as any known database, including an SQL database or an object database. Further, the database software may be any known database software, such as Microsoft SQL Server, Oracle Database, MySQL, Sybase, or others. 
         [0033]    In addition either the database  400  or the web server  300  may execute search server software, such as Apache SOLR. Using a relational database management system (“RDBMS”) software, such as SQLServer 2008, in conjunction with a search server product, such as Apache SOLR, that provides indexing functionality, provides fast responses to queries across exceptionally large sets of data. 
       Patient Population Editor 
       [0034]    One embodiment of the present invention comprises a patient population editor for selecting criteria for shaping patient populations for clinical trials. The patient population editor allows the user to select patient population source data and to select and manipulate adjustable patient criteria to include or exclude segments of the overall patient population for a specific clinical trial and to visually see the effects of the selections. In one embodiment the adjustable criteria available for manipulation are dynamically loaded based on the selected patient population source data. The patient population editor in one such embodiment presents choices to the user in the form of slider input tools (“sliders”) and checkboxes. The user then adjusts the sliders to set limits for patient criteria associated with each slider and checks desired checkboxes associated with patient criteria. For example, limits may comprise upper and lower boundaries. In one embodiment, the patient criteria categories include: 
         [0035]    Co-morbidities 
         [0036]    Concomitant medications 
         [0037]    Age 
         [0038]    Gender 
         [0039]    Ethnicity 
         [0040]    The application executing on the application server  200  executes an algorithm to dynamically generate a query, based on the selected/manipulated adjustable patient criteria, directed to the database  400 . The application then generates graphical views of the data returned by the database  400  for presentation on the client  100 . The embodiments described herein may operate in a similar way within this architecture or may be implemented in other ways. For example, in some embodiments, the client  100  may perform much of the processing in addition to providing the display and receiving the user&#39;s input. 
         [0041]      FIG. 2  is a screen shot of a patient population editor view, comprising a patient criteria selection interface A, an age range line chart B, an ethnicity pie chart C, a patient thermometer bar chart D, and a Venn Bar diagram E. 
         [0042]    In the embodiment shown in  FIG. 2 , patient criteria selection interface A comprises data entry mechanisms such as sliders for selecting upper and lower bounds for the age range of the patient population and checkboxes for selecting gender and ethnicity criteria. In other embodiments, drop down menus, radio buttons, editable data entry fields, or any other user interface mechanism known in the art may be used. Furthermore, while the embodiment of the patient criteria selection interface A shown in  FIG. 2  allows for selection of co-morbidities, concomitant medications, body mass index, age, gender, and ethnicity, any other relevant criteria for shaping a patient population may be used. For example, criteria selection interfaces related to lab results, height, weight, lifestyle criteria, payment method, or any other relevant criteria may be provided in various embodiments. 
         [0043]    Age range line chart B, ethnicity pie chart C, patient thermometer bar chart D, and Venn Bar diagram E are examples of graphical display mechanisms that may be used to aid the user&#39;s understanding of the adjustable criteria selections/manipulations on the patient population. The age range line chart B shows the age range of the patient population by gender and percentage of the patients. Ethnicity pie chart C shows the breakdown of the ethnicity of the patient population based on the size of the pieces of pie representing each ethnicity. Patient thermometer bar chart D shows how many patients would be available in the patient population after applying the selected patient criteria. Finally Venn Bar E is operative to show overlaps between populations to allow the user to understand the individual impact of the user&#39;s choices on various segments of the patient population. The functionality of the Venn Bar is described in more detail in provisional application entitled “Method and System to Manipulate Multiple Selections against a Population of Elements,” Application No. 61/663,292, attorney docket #31006-842890, filed Jun. 22, 2012, which is fully incorporated herein by reference. 
         [0044]      FIG. 3  is a flowchart illustrating one method for performing patient population analysis and manipulation using the patient population editor according to one embodiment of the present invention. In the embodiment shown, the user would first select patient population source at step  302 . At step  304 , the user would then manipulate selected patient criteria using a patient population criteria selection interface such as the interface described above. Manipulating one or more patient criteria causes the patient population editor to receive the query parameters, generate a corresponding query, execute the query on one or more databases based on the criteria, receive the results of the query, and to use those results to update one or more graphical display mechanisms. At step  306 , the user then reviews the resulting prospective patient population and, at step  308 , determines if it is satisfactory. If so, the process ends at step  310 . If not, the user decides to modify selected patient population source data and/or manipulate one or more patient selection criteria at decision point  312 . This process continues iteratively until a satisfactory patient population is created. 
       Patient Population Query Editor 
       [0045]    One embodiment of the present invention comprises a patient population query editor for authoring complex queries for shaping a prospective patient population. The patient population query editor allows the user visually construct complex queries to shape a prospective patient population for a specific clinical trial by including or excluding segments of an overall patient population. The patient population query editor in one such embodiment allows a user to select patient population source data and provides a list of patient criteria elements that may be dragged and dropped into a query builder window and manipulated to visually construct a query. The patient criteria elements have associated values that define the scope related to the element. In one embodiment, patient criteria elements within a query builder window operate to show the information related to the number of patients from the patient population source data that meet the scope defined for the element. 
         [0046]    In one embodiment, the available criteria elements are dynamically loaded based on the selected patient population source data. In one such embodiment, the patient criteria elements may largely correspond to the patient criteria described above in relation to the patient population editor, and therefore may include: 
         [0047]    Co-morbidities 
         [0048]    Concomitant medications 
         [0049]    Age 
         [0050]    Gender 
         [0051]    Ethnicity 
         [0052]    Lab Results 
         [0053]    Height 
         [0054]    Weight 
         [0055]    Lifestyle Criteria 
         [0056]    Payment method 
         [0057]    The application executing on the application server  200  executes an algorithm to dynamically generate a query, based on a graphical representation of a query in a query builder window, directed to the database  400 . The application then updates the patient criteria elements within a query builder window to show the number of patients corresponding to the defined scope of each element, and updates an overall number of patients selected from the patient population source data displayed in the patient population query editor based on the data returned by the database  400  for presentation on the client  100 . The embodiments described herein may operate in a similar way within this architecture or may be implemented in other ways. For example, in some embodiments, the client  100  may perform much of the processing in addition to providing the display and receiving the user&#39;s input. 
         [0058]      FIG. 4  is a screen shot of a patient population query editor view according to one embodiment of the present invention. The screen shot of  FIG. 4  comprises an interface for selecting patient population source data, a list of patient criteria elements, and two query builder windows. In the embodiment shown, a single set of patient population source data, along with the total population of that source data, is listed in the Data Source field in the upper left area of the screen shot. Below the Data Source field is the list of patient criteria elements that may be dragged into query builder windows, such as query builder windows labeled “A” and “B” in  FIG. 4  (respectively “query builder window A” and “query builder window B”.  FIG. 4  illustrates the Body Mass Index patient population criteria element (“BMI element”) being dragged and placed into the query builder window A. As the BMI element is dragged into the area of an existing element, arrows are shown indicating allowed placement positions. In the embodiment shown, placing the element above or below an existing element creates a logical “OR” relationship, whereas placing an element to the left or right creates a logical “AND” relationship. Various other types of logical relationships and constructs may be created visually in embodiments of the present invention. 
         [0059]      FIG. 5  is a screenshot of a patient criteria element manipulation interface for defining the scope of a patient criteria element according to one embodiment of the present invention. This interface may be displayed automatically when the element is dragged and dropped into a query builder window. Alternatively, the interface may be opened by clicking on a button displayed on the element within a query builder window. 
         [0060]    In the embodiment shown in  FIG. 5 , the patient criteria element manipulation interface comprises sliders for selecting upper and lower bounds for the body mass index of the patient population. In other embodiments, checkboxes, drop down menus, radio buttons, editable data entry fields, or any other user interface mechanism known in the art may be used may be used for manipulating the body mass index criteria element or any other criteria element. Furthermore, while the embodiments shown in  FIGS. 4 and 5  list patient population criteria elements for co-morbidities, concomitant medications, body mass index, age, gender, and ethnicity, any other relevant criteria for shaping a patient population may be used. For example, criteria selection interfaces related to lab results, height, weight, lifestyle criteria, payment method, or any other relevant criteria element may be provided. 
         [0061]      FIG. 6  is a screen shot of a patient population query editor view according to one embodiment of the present invention. In particular,  FIG. 6  illustrates the patient population query editor view of  FIG. 4  after the BMI element has been added to the query represented in query builder window A. As can be seen in  FIGS. 4 and 6 , the values selected/entered to define the scope of the patient criteria elements, and the corresponding patient population, are displayed within the criteria elements within the query builder windows. With the addition of the BMI element in query builder window A, the query represented in query builder window A requires that the prospective patient population must be age 18-82 or have a BMI between 25 and 40.9, and must be female. 
         [0062]      FIG. 7  is a flowchart illustrating one method for performing patient population analysis and manipulation using the patient population query editor according to one embodiment of the present invention. In the embodiment shown, the user would first select patient population source data at step  702 . The user would then drag a patient population criteria element into a desired location in a query builder window at step  704 . The user would then define or adjust the scope of the patient population criteria element at step  706 . Adding and/or manipulating one or more patient population criteria elements causes the application  200  to query one or more databases based on the patient criteria elements and to update the patient population information shown within each criteria element and the overall population tally displayed within the patient population query editor view. At step  708 , the user then reviews the resulting prospective patient population and, at step  710 , determines if it is satisfactory. If so, the process ends at step  712 . If not, the user decides to modify the selected patient population source data, drag an additional patient criteria element into a query builder window, reposition an existing patient criteria element within a query builder window, and/or adjust the scope of a patient criteria element at decision point  714 . This process continues iteratively until a satisfactory patient population is created. 
         [0063]      FIG. 8  is a screen shot of a patient population query editor view according to one embodiment of the present invention. In particular,  FIG. 8  illustrates the patient population query editor view of  FIG. 6  after a user clicked the green flag displayed within the BMI element. Clicking the green flags in patient criteria elements causes the flag to turn red, and vice-versa. Toggling the flag between green and red changes whether the element is including or excluding patient segments falling within the scope of the element&#39;s settings. 
         [0064]      FIG. 9  is a screen shot of a patient population query editor view according to one embodiment of the present invention. In particular,  FIG. 9  illustrates the use of a Venn Bar for comparing the query represented by the element configuration displayed in query builder window A and the query represented by the element configuration displayed in query builder window B. The green segments in bars A and B and located in the column “A, B,” represent a portion of the population that result from both queries. The blue segment in bar A located in the column “A” that is not in bar B represents a portion of the population that only results from query A. The grey segment in the row and column labeled “0” indicates the portion of the population that is not a result of either query. 
       Advantages 
       [0065]    Embodiments of the present invention provide many advantages over conventional methods of analyzing the effects of inclusion and exclusion criteria on a patient population. For example, employing a blended approach using RDBMS technologies and SOLR allows the present invention provides fast access to large data sources with all queries taking less than approximately 30 seconds. Consequently, the present invention provides substantially real-time analysis capabilities of the effects of inclusion and exclusion criteria on a prospective patient population, whereas earlier systems typically required 30 minutes or more to provide results to similar queries. 
         [0066]    The patient population editor provides the ability for a user to easily adjust inclusion and exclusion criteria and view easily-readable and intuitive graphical representations of the results within seconds. For example, the patient population editor may use a Venn Bar diagram that shows overlaps between populations to allow the user to more easily understand the individual impact of choices on various segments of the patient population. 
         [0067]    The patient population query editor provides a graphical interface that allows a user to visually construct complex queries using patient criteria elements. The patient population query editor provides one or more query builder windows to allow a user to simultaneously view and edit multiple queries. Further, the patient population query editor provides a graphical representation, such as a Venn Bar, that allows a user to easily view patient population segments common to multiple queries, population segments that result from a single query, and population segments that do not result from any of the queries. 
       General 
       [0068]    The foregoing description of the embodiments of the invention has been presented only for the purpose of illustration and description and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Numerous modifications and adaptations are apparent to those skilled in the art without departing from the spirit and scope of the invention.