U.S. patent application number 13/925187 was filed with the patent office on 2013-12-26 for systems and methods for subject identification (id) modeling.
This patent application is currently assigned to Quintiles Transnational Corporation. The applicant listed for this patent is Joseph William Charles Goodgame. Invention is credited to Joseph William Charles Goodgame.
Application Number | 20130346111 13/925187 |
Document ID | / |
Family ID | 49774051 |
Filed Date | 2013-12-26 |
United States Patent
Application |
20130346111 |
Kind Code |
A1 |
Goodgame; Joseph William
Charles |
December 26, 2013 |
Systems and Methods for Subject Identification (ID) Modeling
Abstract
Systems and methods for subject identification (ID) modeling are
disclosed. A subject identification may be associated with
information contained in one or more core domains such as a patient
domain, a country domain, and/or an investigator domain. The
domains can be generically designed such that data sources that are
unknown at the time the domains are created can be managed. In this
way, using generic structures that support the domains, data
sources can be added and/or updated as additional information
and/or data sources become available. Using various graphical user
interfaces, a user can dynamically associate patient criteria,
country criteria, investigator criteria, and/or other information
with subject identifications. A subject identification may be
associated with a specified capture date. Information contained in
the various domains may be filtered such that only information
contained in the domain on or before the capture date is available
for the subject identification.
Inventors: |
Goodgame; Joseph William
Charles; (Zionsville, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Goodgame; Joseph William Charles |
Zionsville |
IN |
US |
|
|
Assignee: |
Quintiles Transnational
Corporation
Durham
NC
|
Family ID: |
49774051 |
Appl. No.: |
13/925187 |
Filed: |
June 24, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61663292 |
Jun 22, 2012 |
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61663057 |
Jun 22, 2012 |
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61663299 |
Jun 22, 2012 |
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61663398 |
Jun 22, 2012 |
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61663219 |
Jun 22, 2012 |
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61663357 |
Jun 22, 2012 |
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61663216 |
Jun 22, 2012 |
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Current U.S.
Class: |
705/3 |
Current CPC
Class: |
G06T 11/206 20130101;
G16H 10/60 20180101; G06F 16/345 20190101; G06F 17/10 20130101;
G06F 3/0484 20130101; G06F 16/248 20190101; G06F 16/211 20190101;
G16H 10/20 20180101; G16H 40/63 20180101; G06F 40/166 20200101 |
Class at
Publication: |
705/3 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A method comprising: receiving selection of a subject
identification, the subject identification corresponding with at
least a subject name; receiving selection of a patient parameter,
the patient parameter corresponding with at least one of a physical
characteristic or an illness; receiving selection of an
investigator parameter, the investigator parameter corresponding
with at least one of a trial, a phase, or a medical indicator;
receiving selection of a geographic parameter, the geographic
parameter corresponding with at least one of a geographic location
or a geographic statistic; dynamically creating associations
between the subject identification and the patient parameter, the
investigator parameter, and the geographic parameter; and
determining at least one potential site for a clinical trial based
at least in part on the dynamically created associations.
2. The method of claim 1, further comprising: conducting the
clinical trial based at least in part on the determined at least
one potential site.
3. The method of claim 1, where receiving selection of the subject
identification comprises: receiving selection of the subject name
from a plurality of predefined subject names.
4. The method of claim 1, where receiving selection of the subject
identification comprises: receiving a first input, the first input
indicating the subject name; receiving a second input, the second
input indicating a molecule team; and dynamically creating the
subject identification.
5. The method of claim 1, wherein receiving selection of the
patient parameter comprises receiving selection of at least one
medical code, wherein the at least one medical code comprises at
least one ICD9 code or ICD10 code.
6. The method of claim 1, further comprising: in response to
receiving the selection of the patient parameter, displaying a
number of patients having the selected patient parameter.
7. The method of claim 6, wherein the number of patients having the
selected patient parameter is selected from a plurality of patient
databases, each patient database in the plurality of patient
databases corresponding to a same generic patient data model.
8. The method of claim 1, wherein dynamically creating associations
between the subject identification and the patient parameter, the
investigator parameter, and the country parameter comprises:
creating at least one association between the subject
identification and at least one patient database comprising the
patient parameter; creating at least one association between the
subject identification and at least one investigator database
comprising the investigator parameter; and creating at least one
association between the subject identification and at least one
country database comprising the country parameter.
9. The method of claim 8, wherein each patient database corresponds
to a same patient data model, wherein each investigator database
corresponds to a same investigator data model, and wherein each
country database corresponds to a same country data model.
10. The method of claim 8, further comprising: receiving selection
of a capture date; and updating the subject identification such
that only information contained in the at least one patient
database, the at least one investigator database, and the at least
one country database on or before the capture date is available for
the selected subject identification.
11. The method of claim 1, further comprising: creating a graphical
representation based at least in part on the dynamically created
associations, the graphical representation providing a prediction
for one or more scenarios.
12. The method of claim 11, wherein the prediction corresponds to
at least one of a likely scenario based at least on part on average
performance, a best-case scenario, or a worst-case scenario.
13. A computer-readable medium comprising program code for:
receiving selection of a subject identification, the subject
identification corresponding with at least a subject name;
receiving selection of a patient parameter, the patient parameter
corresponding with at least one of a physical characteristic or an
illness; receiving selection of an investigator parameter, the
investigator parameter corresponding with at least one of a trial,
a phase, or a medical indicator; receiving selection of a
geographic parameter, the geographic parameter corresponding with
at least one of a geographic location or a geographic statistic;
dynamically creating associations between the subject
identification and the patient parameter, the investigator
parameter, and the geographic parameter; and sending information
corresponding to at least one of the dynamically created
associations to a clinical trial analysis application for
conducting a clinical trial.
14. The computer-readable medium of claim 13, further comprising
program code for: receiving a capture date, wherein only
information contained in at least one patient database, at least
one investigator database, and at least one country database as of
the capture date is available for the subject identification.
15. The computer-readable medium of claim 13, further comprising
program code for: creating a graphical representation for the
clinical trial based at least in part on the dynamically created
associations.
16. The computer-readable medium of claim 13, further comprising
program code for: associating a capture date with the subject
identification such that data on or before the capture date can be
maintained in a database while allowing the database to continue to
receive additional data after the capture date.
17. A system, comprising: a plurality of databases; an electronic
device comprising: an input device; a display; and a processor in
communication with the input device, the display, the plurality of
databases, the processor configured for: receiving selection of a
subject identification, the subject identification corresponding
with at least a subject name; receiving selection of a patient
parameter, the patient parameter corresponding with at least one of
a physical characteristic or an illness; receiving selection of an
investigator parameter, the investigator parameter corresponding
with at least one of a trial, a phase, or a medical indicator;
receiving selection of a geographic parameter, the geographic
parameter corresponding with at least one of a geographic location
or a geographic statistic; dynamically creating associations
between the subject identification and the plurality of databases
for use in a clinical trial analysis application, the associations
comprising a first association between the subject identification
and the patient parameter, a second association between the subject
identification and the investigator parameter, and a third
association between the subject identification and the country
parameter.
18. The system of claim 17, further comprising: a network; and a
server comprising: a memory, wherein the memory comprises program
code for the clinical trial analysis application; a second
processor, the second processor in communication with the memory,
the second processor configured for: executing the program code for
the clinical trial analysis application; receiving information
corresponding to at least one of the dynamically created
associations from the electronic device through the network; and
conducting a clinical trial using at least the clinical trial
analysis application and the received information.
19. The system of claim 17, wherein the plurality of databases
comprises a patient database, an investigator database, and a
country database, and wherein the processor is further configured
for: querying the patient database with the patient parameter;
querying the investigator database with the investigator parameter;
and querying the country database with the country parameter;
20. The system of claim 17, wherein the processor is further
configured for: receiving a capture date; and filtering information
in the plurality of databases such that only information in the
plurality of databases on or before the capture date is available
to the subject identification and such that new information can be
added to the plurality of databases after the capture date and the
new information is not available to the subject identification.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to 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. 61/663,219, filed Jun.
22, 2012, entitled "Systems and Methods for Analytics on Viable
Patient Populations;" 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 data associated with
clinical trials. The present invention relates more specifically to
systems and methods for subject identification (ID) modeling.
BACKGROUND
[0003] Clinical trials for molecules that may become pharmaceutical
products often last for years. The core cost of the that is
affected primarily by the length of the trial. And a delay of even
a single day can cost hundreds or thousands and even millions of
dollars.
[0004] Data associated with clinical trials is often associated
with various data sources and may be highly diverse. Highly diverse
data from numerous data sources is often difficult to organize,
assemble, and analyze. Therefore, systems and methods for the
collation of highly diverse data into usable data would be
advantageous. Furthermore, systems and methods for dynamic analysis
to support better understanding of the impacts of decisions against
clinical trials would be advantageous.
SUMMARY
[0005] Embodiments of the present invention provide systems and
methods for subject identification (ID) modeling. In one
embodiment, raw data is processed to an application using a tool
that enables a user to build subject identifications dynamically.
In some embodiments, such subject IDs can be created by a user
without technical expertise.
[0006] In one embodiment, a subject identification can be created
dynamically. For example, a user can interact with a user interface
to create a subject by entering or selecting a subject name and a
molecule team. In one embodiment, a unique subject identification
ID is automatically created or assigned once a subject name and a
molecule team have been entered or selected.
[0007] A subject identification may be associated with information
contained in other tables and/or databases. For example, in one
embodiment, subject identifications may be associated with
information contained in one or more core domains such as a patient
domain, a country domain, and/or an investigator domain. In some
embodiments, one or more domains are generically designed such that
data sources that are unknown at the time the domain(s) are created
can be managed. In this way, using a generic structure that
supports a domain, data sources can be added and/or updated as
additional information and/or data sources become available.
Exemplary models that depict generic structures which support such
domains are disclosed herein and variations are within the scope of
this disclosure.
[0008] Using various graphical user interfaces, a user can
dynamically associate patient criteria, country criteria,
investigator criteria, and/or other information with subject
identifications. In some embodiments, as the user interacts with
the graphical user interfaces, associations between subject
identifications and data in other tables and/or databases is
dynamically updated in real-time or substantially real time. For
example, when a user selects an indicator to be associated with a
particular subject identification, the association may be created.
As another example, when a user selects various indicators to be
associated with a subject identification and then clicks an update
button on the graphical user interface, the selected indicators may
be dynamically associated with the subject identification.
[0009] Information associated with a particular subject
identification may be frozen at a particular date and/or time. For
example, a subject identification may be associated with a
specified capture date. In this embodiment, information contained
in the various domains may be filtered such that only information
contained in the domain on or before the capture date is available
for the subject identification. In this way, information for a data
model may be updated as additional information for the data model
becomes available but the information available to a particular
subject identification can be limited to a static point in
time.
[0010] These embodiments are 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
[0011] 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:
[0012] FIG. 1 is a block diagram illustrating an exemplary
environment for implementation of one embodiment of the present
invention;
[0013] FIG. 2 is a flowchart illustrating a method for dynamically
creating and/or updating information associated with subject
identifications according to one embodiment of the present
invention;
[0014] FIG. 3 is a partial entity-relationship diagram illustrating
how a subject ID is linked to complex and/or varied data sets from
multiple sources according to one embodiment of the present
invention;
[0015] FIG. 4 is a screen shot of a subject table editor according
to one embodiment of the present invention;
[0016] FIG. 5 is a screen shot of patient selection according to
one embodiment of the present invention;
[0017] FIG. 6 is a screen shot of a data source table editor
according to one embodiment of the present invention;
[0018] FIG. 7 is a screen shot of investigators for investigator
performance loads according to embodiments of the present
invention;
[0019] FIG. 8 is a screen shot of investigators for investigator
performance loads according to embodiments of the present
invention;
[0020] FIG. 9 is a screen shot of investigators for investigator
performance loads according to embodiments of the present
invention;
[0021] FIG. 10 is a screen shot of a patient prevalence editor
according to one embodiment of the present invention
[0022] FIG. 11 is an exemplary investigator data model according to
one embodiment of the present invention;
[0023] FIG. 12 is an exemplary country data model according to one
embodiment of the present invention; and
[0024] FIG. 13 is an exemplary patient data model according to one
embodiment of the present invention.
DETAILED DESCRIPTION
[0025] Embodiments of the present invention provide systems and
methods for subject identification (ID) modeling.
Illustrative Embodiment of the Present Invention
[0026] One illustrative embodiment of the present invention
comprises an application for creating and/or updating subject
identification (ID) models for clinical trials. The embodiment
allows a user to access an application that presents a variety of
clinical trial-related parameters for various patients, countries,
and/or investigators. These parameters may include, for example,
the population of a country, the regulatory environment, and/or the
level of risk associated with conducting a trial in a particular
country.
[0027] Using various graphical user interfaces associated with the
application, a user can create subject identifications and/or
select parameters related to patients, countries, and/or
investigators for subject identifications. For example, in one
embodiment, a user can select patients associated with one or more
ICD9 codes for a particular subject identification. As another
example, a user can add or remove various indicators--such as Ace
Inhibitors, Acne, etc.--and/or phases and/or trials.
[0028] As the user interacts with the graphical user interface
associations with data contained in various databases are added,
removed, or updated. In some embodiments, such associations may be
added, removed, or updated dynamically without requiring technical
expertise regarding the underlying data structures. For example, a
user can select one or more ICD9 codes to associate with a
particular subject identification. In one embodiment. When a user
selects an ICD9 code, associations between the selected ICD9 code,
patients corresponding to the ICD9 code, and/or the subject
identification are created. Similarly, when a user deselects an
ICD9 code, associations between subject identifications and
information in other tables and/or databases may be updated or
removed.
[0029] In the illustrative embodiment, for various investigators,
the user is able to specify investigator-specific parameters, such
as indicators, phases, trials and/or other relevant parameters. The
process is iterative; the user is able to change the parameters for
patients, countries and/or investigators to determine the most
appropriate sites to utilize for a clinical trial. As the user
changes these parameters, information and/or associations
corresponding to subject identifications and/or data structures may
be dynamically added, removed, or updated. The results of the
user's selections can then be used as part of a larger clinical
trial analysis application.
[0030] 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
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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. In one embodiment,
the visual representation appears as a spider-like layout of nodes
and connectors extending from each node to a central node.
[0035] 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#, Visual Basic, Java, Python, Perl, and JavaScript.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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
conesponding 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.
Illustrative Process for Adding/Updating Information for Subject
Identifications
[0041] FIG. 2 is a flowchart illustrating a method for dynamically
creating and/or updating information associated with subject
identifications according to one embodiment of the present
invention.
[0042] The method 200 shown in FIG. 2 begins when a subject is
created or selected 210. For example, an application may comprise a
user interface for creating or selecting a subject. In one
embodiment, a subject can be created by entering or selecting a
subject name and/or a molecule team. In such an embodiment, a
unique subject identification corresponding to the entered or
selected subject name and/or molecule team may be dynamically
created. For example, in one embodiment, when a cursor moves off a
particular row, data associated with the row may be added or
updated in a database and/or table containing information for
subjects. In one embodiment, a message is shown in the application
that indicates whether the database and/or table were successfully
modified.
[0043] After creating or selecting a subject 210, the method 200
proceeds to block 220. In block 220, patient parameters are
received. For example, codes corresponding to particular diseases
and/or illnesses, such as ICD9 or ICD10 codes, may be received
through a graphical user interface of the application. Based on the
selected patient parameters, a number of patients in one or more
databases that meet the selected criteria may be displayed in the
application. In some embodiments, information regarding patients
and/or patient parameters may be stored in multiple databases
and/or tables. In such an embodiment, the application may
dynamically create or update associations between the various
databases and/or tables containing information corresponding to
patients and selected subject identification(s). For example,
patient information may be stored in one or more patient databases
corresponding to one or more generic patient data models. In such
an embodiment, associations between the patient database(s) and
subject may be dynamically created, modified, or removed based on
selected or deselected patient parameters.
[0044] After patient parameters are received 220, the method 200
proceeds to block 230. In block 230, investigator parameters are
received. For example, various indicators, phases, and/or trials
may be selected or removed for one or more subject identifications.
In some embodiments, information regarding investigators and/or
investigators parameters may be stored in multiple databases and/or
tables. In such an embodiment, the application may dynamically
create or update associations between the various databases and/or
tables containing information corresponding to investigators and
selected subject identification(s). For example, investigator
information may be stored in one or more investigator databases
corresponding to one or more generic investigator data models. In
such an embodiment, associations between the investigator
database(s) and subjects may be dynamically created, modified, or
removed based on selected or deselected investigator
parameters.
[0045] After investigator parameters are received 230, the method
200 proceeds to block 240. In block 240, country parameters are
received. In some embodiments, information regarding countries
and/or country parameters may be stored in multiple databases
and/or tables. In such an embodiment, the application may
dynamically create or update associations between the various
databases and/or tables containing information corresponding to
countries and selected subject identification(s). For example,
country information may be stored in one or more investigator
databases corresponding to one or more generic country data models.
In such an embodiment, associations between the country database(s)
and subjects may be dynamically created, modified, or removed based
on selected or deselected country parameters.
[0046] After country parameters are received 240, the method 200
proceeds to block 250. In block 250, a capture date is received.
Based on the capture date for a particular subject, the application
may update the subject identification such that only information
associated with the databases on or before the capture date can be
included in any data analysis associated with the subject. For
example, one or more databases associated with a subject may
periodically be updated as new information becomes available. In
this embodiment, even if the database is updated, the information
available for a selected subject may be limited to information
associated with the database on or before the capture date
associated with the subject. In this way, a static snapshot of data
for a particular subject can be maintained. While allowing
databases to continue to receive additional data as it becomes
available. In some embodiments, the additional information may be
used by other subjects that either do not have a specified capture
date or that have a capture date that is after the information is
received. Numerous other embodiments are disclosed herein and
variations are within the scope of this disclosure.
Illustrative Associations for Subject ID and Data Sets
[0047] FIG. 3 is a partial entity-relationship diagram illustrating
how a subject ID is linked to complex and/or varied data sets from
multiple sources according to one embodiment of the present
invention. In the embodiment shown in FIG. 3, subject
identifications (IDs), subject names, molecule teams, and/or
capture dates are stored in a subject table. In this embodiment, a
unique subject ID may be selected or assigned for each subject. In
one embodiment, one or more of the subject name, molecule team,
and/or capture date may be optional. In other embodiments, one or
more of the subject name, molecule team, and/or capture date may be
required. In still other embodiments, additional information for a
subject ID may be optional or required. Referring back to FIG. 3,
numerous tables contain data that corresponds to subject IDs in the
subject table. For example, in FIG. 3, a subject ID in the Subject
(Dim) table may correspond with a subject IL) in the Staffing
(Fact) table. In this embodiment, the subject ID in the Staffing
(Fact) table corresponds with a country identification. In other
embodiments, information such as staffing, cycle times, monthly
recruitment, trial saturation, patient prevalence, patient
information, patient morbidity, patient concomitant information,
investigator performance information, and/or investigator trial
information--may be associated with a subject identification (ID)
in a subject table. Numerous other embodiments are disclosed herein
and variations are within the scope of this disclosure.
Data Steward Application and Screen Shots
[0048] Data Steward is a software tool that can be used to directly
modify databases according to embodiments of the present invention.
Appendix A, which is hereby incorporated by reference in its
entirety, comprises a User Guide for a Data Steward according to
embodiments of the present invention.
[0049] FIG. 4 is a screen shot of a subject table editor according
to one embodiment of the present invention. In the subject table
editor interface of the Data Steward application, a user can sort
information displayed in the subject table editor interface based
on subject identifications, subject names, molecule teams, capture
dates, whether the subject ID is located, whether a subject ID has
been processed, descriptions, and/or other information associated
with a subject ID. In the embodiment shown in FIG. 4, a user can
lock or unlock a particular subject ID by selecting or deselecting
a checkbox con to a particular subject ID. Likewise, in FIG. 4, a
user can selected whether or not a subject is processed by
selecting or deselecting a checkbox corresponding to a particular
subject ID. Numerous other embodiments are disclosed herein and
variations are within the scope of this disclosure.
[0050] FIG. 5 is a screen shot of patient selection according to
one embodiment of the present invention. In the patient selection
interface of the Data Steward application, a user can select ICD
codes for a subject such that matching patient records can be
identified. In the embodiment shown in FIG. 5, a user can select a
subject identification and a data source for the subject
identification. In this embodiment, a user can add and/or remove
ICD9 codes. In other embodiments, other information which
identifies a disease, illness, or other intbrmation usable to
filter patient records may be selected. In the embodiment shown in
FIG. 5, information regarding patient counts, such as patient
counts in fact stage and/or patient counts in facts, may be
displayed based at least in part on the selected information. For
example, in FIG. 5, an Electronic Health Records (EHR) data source
has been selected as well as various ICD9 codes. In this
embodiment, a patient count, condition count, and medication count
are displayed based at least in part on the selected data source
and the selected ICD9 codes. Numerous other embodiments are
disclosed herein and variations are within the scope of this
disclosure.
[0051] FIG. 6 is a screen shot of a data source table editor
according to one embodiment of the present invention. In the
embodiment shown in FIG. 7, information corresponding to various
data sources is displayed. For example, a data source in the
DataSource Tables Editor can have a corresponding data source
identification, data source name, description, create date, file
date, and/or data source owner. In other embodiments, additional or
less information for one or more data sources is shown in the
DataSource Tables Editor user interface. Numerous other embodiments
are disclosed herein and variations are within the scope of this
disclosure.
[0052] FIGS. 7, 8, and 9 are screen shots of investigators for
investigator performance loads according to embodiments of the
present invention. These screenshots illustrate performance load
criteria that may be selected or removed for a particular subject
identification according to embodiments. For example, various
indicators, phases, and/or trials may be selected or removed for
one or more subject identifications. Numerous other embodiments are
disclosed herein and variations are within the scope of this
disclosure.
[0053] FIG. 10 is a screen shot of a patient prevalence editor
according to one embodiment of the present invention. In the
embodiment shown in FIG. 10, a user can filter information from
various databases based on one or more of the following: subjects,
data sources, countries, country population, prevalence, prevalence
factor, prevalence per population, and/or supporting evidence. Data
displayed in the graphical user interface of the Data Steward
application may be sorted by subject, data source, country name,
country population, prevalence, prevalence rate, prevalence per
population, and/or supporting evidence. In the embodiment shown in
FIG. 10, a column header can be dragged to a particular location of
the user interface to group information by that column. Numerous
other embodiments are disclosed herein and variations are within
the scope of this disclosure.
Exemplary Investigator Data Model
[0054] FIG. 11 is an exemplary investigator data model according to
one embodiment of the present invention. In the embodiment shown in
FIG. 11, a FactInvestigatorPerformance table contains information
associated with the patient randomization, enrollment rates, and
failure rates as well as information associated with investigator
identifications, subject identifications, data source
identifications, and location identifications which correspond with
additional information contained in other tables and/or databases.
For example, a particular subject identification (SubjectID) in the
FactInvestigatorPerformance table may correspond with a SubjectID
in the DIM.Subject table.
[0055] By using the SubjectID in the FactinvestigatorPerformance
table to query the SubjectID in the DIM.Subject table, additional
information such as the subject name, molecule team, and/or capture
can be determined for the SuhiectID contained in the
FactinvestigatorPerformance table. In some embodiments, information
contained in various tables and/or databases may be linked in a
chain. For example, a DataSourceID in the
FactinvestigatorPerformance table correspond with a DataSourceID in
the Dim.Dim.DataSource table. The Dim.Dim.DataSource table, in
turn, may contain a DataSourceOwnerID fro a particular DataSourceID
which corresponds with a DataSourceOwnerID in the
Dim.DataSourceOwner table. Thus, by querying the various tables
and/or databases, a DataSourceID in the FactinvestigatorPerformance
table can be used to determine information such as the
LastModifiedDate in the Dim. DataSourceOwner table for the
DataSource associated with the DataSourceID. Numerous other
embodiments are disclosed herein and variations are within the
scope of this disclosure.
[0056] Below is a description of the various tables of an
investigator data model according to one embodiment of the present
invention:
TABLE-US-00001 List of Tables in Investigator Data Model Name
Comment DataSourceOwner DIM.Country Conformed country dimension
consisting of ISO-3166 standards and minor manual updates to
streamline country presentation data for Semio. Dim.DataSource
Dim.Location Location dimension table contains location information
about country, state, city, latitude, and longitude. DIM.Subject
Subject table is the dimensional table used to snapshot fact data
by molecule team, search phrase, and date.
Fact.InvestigatorPerformance Fact information about Investigator
performance.
TABLE-US-00002 Column(s) of "DataSourceOwner" Table Is Is Name
Datatype Comment PK FK DataSourceOwnerID int Yes No DataSourceOwner
nvarchar(50) No No CreationDate datetime No No LastModifiedDate
datetime No No LastModifiedByID varbinary(85) No No IsActiveFlag
bit No No
TABLE-US-00003 Column(s) of "DIM.Country" Table Is Is Name Datatype
Comment PK FK CountryID integer Country Identification Yes No
Number. The primary key CountryName varchar( ) Country Description
No No Abbrevia- nchar(2) Two letter No No tionSmall abbreviation
name of the country Abbrevia- nchar(3) Three letters name No No
tionLarge of the country IsActiveFlag char(18) bit indicator for
the No No validity of the record IsInferredFlag char(18) No No
AuditETLID char(18) Reference to Audit. No No ExecutionLog key for
auditing FIPS nvarchar(255) Two lettres code for No No the country
GMI nvarchar(255) Three lettres code for No No the country
Population bigint Population of the No No country SQKM float The
total square No No kilometer of a country SQMI float The total
miles of a No No country Geometery geometry Geomatrical informa- No
No tion of the country LandLocked char(1) The country has a No No
landlocked or not? CTReferenceCode varchar(10) Clinical Trial No No
reference code Latitude decimal(19, 12) Latitude information No No
of the country Longitude decimal(19, 12) Longitude information No
No of the country ISOName nvarchar(255) No No
TABLE-US-00004 Column(s) of "Dim.DataSource" Table Is Is Name
Datatype Comment PK FK DataSourceID int Data Source Yes No Unique
Identity Number DataSourceName nvarchar(50) Name of the Data No No
Source Provider DataSourceOwner Nvarchar(100) Name of the Data No
No Source Owner DataSourceDe- nvarchar(255) Description of No No
scription the Data Source CreationDate datetime The date on No No
which the data is being inserted LastModifiedDate datetime Last
modified No No date LastModifiedByID varbinary(85) Last modified by
No No Identity number IsActiveFlag bit Bit indicator for No No the
validity of the record DataSourceOwnerID int No Yes
TABLE-US-00005 Column(s) of "Dim.Location" Table Is Is Name
Datatype Comment PK FK LocationID int Location identity number Yes
No CountryID integer Country identity number. No Yes Foriegn key
referenced from Dim.Country table. State nvarchar(30) State
information No No City nvarchar(30) City information No No Latitude
float(19, 12) Latitude information No No Longitude float(19, 12)
Longitude information No No GeoNameID int Geographical detail No No
about a location
TABLE-US-00006 Column(s) of "DIM.Subject" Table Is Is Name Datatype
Comment PK FK SubjectID integer Subject Identification Yes No
SubjectName varchar(20) Subject Name No No Molecule_Team char(18)
Molecule team No No information CaptureDate char(18) No No
TABLE-US-00007 Column(s) of "Fact.InvestigatorPerformance" Table Is
Is Name Datatype Comment PK FK InvestigatorID int Investigator
Identity. The Yes No primary key in the table. SubjectID integer
Subject Identification. Yes Yes Foreign key from Subject Dimension
Table. DataSourceID int Data Source Unique Yes Yes Identity Number,
apperaing in this table as foreign key LocationID int Location
identity number No Yes TotalTrials int How many trials No No
Investigator & Site participated in this particular indication
SiteStartupCyleTime int Average time for No No Investigator &
Site to open enrollment after the contract signed
EnrolledINLast16Months bit Number of enrollment No No for the last
16 months PatientRandomizedMedian decimal(19, 12) Median
randamization of No No patients across all trials for the
indication. PatientRandomized int Average randamization No No of
patients across all trials for the indication.
PatientRandomizedMaximum decimal(19, 12) Maximum randamization No
No of patients across all trials for the indication.
EnrollmentRateMonthlyMean decimal(19, 12) No No
EnrollmentRateMonthly int Enrollment rate per No No month
EnrollmentRateMonthlyMeadian decimal(19, 12) Median Enrollment Rate
No No per month EnrollmentRateMonthlyStandardDev decimal(19, 12)
Standard Deviation No No Monthly Data. PatientScreened int The
number of patient No No screened for this indication.
ScreenFailureRate decimal(19, 12) Percentage of patients No No that
were unable to participate due to failed screening. DropoutRate
decimal(19, 12) Percentage of enrolled No No patients who dropped
out from the trial. QueryRate int Number of queries per No No 100
pages of CRFs InvestigatorEnrollmentFactor int Calculated
performence No No ranking of the Investigators. IsActiveFlag bit No
No AuditETLFlag int No No SiteStartUpCycleTimeStandardDev
decimal(19, 12) Standard Deviation for No No Investigator &
Site to open enrollment after the contract signed
Exemplary Country Data Model
[0057] FIG. 12 is an exemplary country data model according to one
embodiment of the present invention. In the embodiment shown in
FIG. 13, each country is associated with a unique country
identification (CountryID). In this embodiment, the CountryID is
associated with other information such as a country name, country
abbreviations, population, and/or GPS coordinates. The CountryID
for a particular country may be associated with information
contained in other tables and/or databases. For example, in the
FactPatientPrevalence table shown in FIG. 12, a CountryID and a
subject identification (SubjectID) may be used to determine a
prevalence rate, prevalence per population, and incidence per
population. As another example, a Country and a SubjectID may be
used to query a FactTrialSaturation table to determine an active
trial count. The embodiment shown in FIG. 12 depicts numerous other
associations between countryIDs and information in other tables
and/or databases. Numerous other embodiments are disclosed herein
and variations are within the scope of this disclosure.
[0058] Below is a description of the various tables of a country
data model according to one embodiment of the present
invention:
TABLE-US-00008 List of Tables in Country Data Model Name Comment
DataSourceOwner DIM.Country Conformed country dimension consisting
of ISO-3166 standards and minor manual updates to streamline
country presentation data for Semio. Dim.CountryCycleTime
Dim.DataSource DIM.Subject Subject table is the dimensional table
used to snapshot fact data by molecule team, search phrase, and
date. Fact.CycleTime Fact table containing cycle time information
for a given Country, Subject, and type (CT Materials, throughput,
etc) Fact.MonthlyRecruitment Fact table containing cycle time
information for a given Country, Subject, and type (CT Materials,
throughput, etc) Fact.PatientPrevalence Contains the prevalence of
a particular disease condition (captured in SubjectID) for a given
Country (countryID) Fact.Staffing Fact table containing staff
information for a given Country, Subject, and type.
Fact.TrialSaturation By Country, By Subject (molecule team + search
phrase) - the number of active trials.
TABLE-US-00009 Column(s) of "DataSourceOwner" Table Name Is PK Is
FK Comment DataSourceOwnerID Yes No DataSourceOwner No No
CreationDate No No LastModifiedDate No No LastModifiedByID No No
IsActiveFlag No No
TABLE-US-00010 Column(s) of "DIM.Country" Table Name Is PK Is FK
Comment CountryID Yes No Country Identification Number. The primary
key CountryName No No Country Description AbbreviationSmall No No
Two letter abbreviation name of the country AbbreviationLarge No No
Three letters name of the country IsActiveFlag No No bit indicator
for the validity of the record IsInferredFlag No No AuditETLID No
No Reference to Audit. ExecutionLog key for auditing FIPS No No Two
lettres code for the country GMI No No Three lettres code for the
country Population No No Population of the country SQKM No No The
total square kilometer of a country SQMI No No The total miles of a
country Geometery No No Geomatrical information of the country
Landlocked No No The country has a landlocked or not?
CTReferenceCode No No Clinical Trial reference code Latitude No No
Latitude information of the country Longitude No No Longitude
information of the country ISOName No No
TABLE-US-00011 Column(s) of "Dim.CountryCycleTime" Table Name Is PK
Is FK Comment CountryID Yes No IterationID Yes No CycleTimeTypeID
Yes No DataSourceID Yes No CycleTimeDecimal No No
CycleTimeStandardDev No No TheorecticalApprovalTimeInDays No No
AverageApprovalTimeInDays No No SupportingEvidence No No
IsActiveFlag No No AuditETLID No No CreatedDate No No ModifiedDate
No No Modifiedby No No
TABLE-US-00012 Column(s) of "Dim.DataSource" Table Name Is PK Is FK
Comment DataSourceID Yes No Data Source Unique Identity Number
DataSourceName No No Name of the Data Source Provider
DataSourceOwner No No Name of the Data Source Owner
DataSourceDescription No No Description of the Data Source
CreationDate No No The date on which the data is being inserted
LastModifiedDate No No Last modified date LastModifiedByID No No
Last modified by Identity number IsActiveFlag No No Bit indicator
for the validity of the record DataSourceOwnerID No Yes
TABLE-US-00013 Column(s) of "DIM.Subject" Table Name Is PK Is FK
Comment SubjectID Yes No Subject Identification SubjectName No No
Subject Name MoleculeTeam No No Molecule team information
CaptureDate No No The date data was inserted in the database
IsLocked No No IsActiveVariableFlag No No ProcessFlag No No
SubjectDetailDescription No No
TABLE-US-00014 Column(s) of "Fact.CycleTime" Table Name Is PK Is FK
Comment CountryID Yes Yes Country Identification Number. The
primary key SubjectID Yes Yes Subject Identification
CycleTimeTypeID Yes Yes DataSourceID Yes Yes Data Source Unique
Identity Number CycleTime No No Cycle time information
SupportiveEvidence No No The xml document containing the supportive
information. IsActiveFlag No No bit indicator for the validity of
the record AuditETLID No No Reference to Audit. ExecutionLog key
for auditing TheorecticalAp- No No Approval time in days
provalTimeInDays for the country AverageApprov- No No Average time
in days for alTimeInDays the country CyleTimeStandardDev No No
Average randamization of patients across all trials for the
indication. IterationID No Yes
TABLE-US-00015 Column(s) of "Fact.MonthlyRecruitment" Table Name Is
PK Is FK Comment CountryID Yes Yes Country Identification Number.
The primary key SubjectID Yes Yes Subject Identification MonthID
Yes No Month Identification PatientPerSitePerMonth No No Number of
patients per site per month SupportiveEvidence No No The xml
document containing the supportive information. IsActiveFlag No No
bit indicator for the validity of the record AuditETLID No No
Reference to Audit. ExecutionLog key for auditing
EnrollmentRateMonth- No No Standard Deviation lyStandardDev
calculation of Enroll- ment rate per month
LowerEnrollmentRateMonth- No No Lower side Standard lyStandardDev
Deviation calculation of Enrollment rate per month
UpperEnrollmentRateMonth- No No Upper side Standard lyStandardDev
Deviation calculation of Enrollment rate per month
PatientRandomized No No Randomized Average Number of patients at
country level. DataSourceID No Yes Data Source Unique Identity
Number
TABLE-US-00016 Column(s) of "Fact.PatientPrevalence" Table Name Is
PK Is FK Comment CountryID Yes Yes Country Identification Number.
The primary key SubjectID Yes Yes Subject Identification
PrevalencePer No No The column contains the number of patient for
calculation PrervalenceRate No No The prevalence rate calculated
number SupportingEvidence No No The xml document containing the
supportive information. IsActiveFlag No No bit indicator for the
validity of the record AuditETLID No No Reference to Audit.
ExecutionLog key for auditing DataSourceID No Yes Data Source
Unique Identity Number PrevalencePerPopulation No No IncidencePer
No No IncidencePerPopulation No No IncidenceSupportingEvidence No
No
TABLE-US-00017 Column(s) of "Fact.Staffing" Table Name Is PK Is FK
Comment CountryID Yes Yes Country Identification Number. The
primary key SubjectID Yes Yes Subject Identification CRANumber No
No Total number of CRA in a country SupportiveEvidence No No XML
document containing evidentiary details of the findings in the
fact. IsActiveFlag No No bit indicator for the validity of the
record AuditETLID No No Reference to Audit. ExecutionLog key for
auditing StaffTotal No No Total number of staff DataSourceID No Yes
Data Source Unique Identity Number
TABLE-US-00018 Column(s) of "Fact.TrialSaturation" Table Name Is PK
Is FK Comment CountryID Yes Yes Country Identification Number. The
primary key SubjectID Yes Yes Subject Identification
ActiveTrialCount No No Number of Active Trail information
SupportiveEvidence No No The xml document containing the supportive
information. IsActiveFlag No No bit indicator for the validity of
the record AuditETLID No No Reference to Audit. ExecutionLog key
for auditing DataSourceID No Yes Data Source Unique Identity
Number
Exemplary Patient Data Model
[0059] FIG. 13 is an exemplary patient data model according to one
embodiment of the present invention. In the embodiment shown in
FIG. 13, a FactPatient table stores information associated with
various patients. For example, patients in the FactPatient table
may be assigned a unique patient identification number (PatientID).
In this embodiment, the Patient ID can be associated with other
information such as the patient's age, gender, year of birth,
and/or other information. The PatientID may also be associated with
information contained in the other tables and/or databases. For
example, a Patient ID may be associated with a location
identification (LocationID). In this embodiment, the LocationID for
the patient corresponds with a location identification of a
separate table (DimLocation). The LocationID) for the patient can
be used to determine information such as a city, a state, and/or
GPS coordinates associated with the PatientID based on the
LocationID. As another example, a SubjectID associated with a
particular PatientID may be used to determine a subject's name,
molecule team, capture date, and/or other information contained in
another table and/or database having a corresponding SubjectID. The
embodiment shown in FIG. 13, depicts numerous other associations
between information corresponding to PatientIDs and information in
other tables and/or databases. Furthermore, numerous other
embodiments are disclosed herein and variations are within the
scope of this disclosure. Numerous other data models and variations
of the data models described herein are likewise within the scope
of this disclosure.
[0060] Below is a description of the various tables of a patient
data model according to one embodiment of the present
invention:
TABLE-US-00019 List of Tables for Patient Data Model Name Comment
DataSourceOwner Dim.Concomi- ConcomitantMedicationn dimension
tantMedication table contains name of the medication, class
information. Dim.DataSource Dim.Ethnicity Ethnicity dimension table
containsethnicity information about patient Dim.Location Location
dimension table contains location information about country, state,
city, latitude, and longitude. DIM.Subject Subject table is the
dimensional table used to snapshot fact data by molecule team,
search phrase, and date. Fact.Patient Fact table containing
information for a patient, Subject Fact.PatientCo-
MorbidityCondition Fact.PatientCon- comitantMedication ICD9.Codes
Medication TreatmentType
TABLE-US-00020 Column(s) of "DataSourceOwner" Table Is Is Name
Datatype Comment PK FK DataSourceOwnerID int Yes No DataSourceOwner
nvarchar(50) No No CreationDate datetime No No LastModifiedDate
datetime No No LastModifiedByID varbinary(85) No No IsActiveFlag
bit No No
TABLE-US-00021 Column(s) of "Dim.ConcomitantMedication" Table Is Is
Name Datatype Comment PK FK Concomi- int Concomitant Medication Yes
No tantMedicationID Identity. The Primary Key of the table
MedicationName nvarchar(50) Name of the medication No No
MedicationClass nvarchar(50) Medical Class No No information. DDI
int Drug Index No No NDC bigint Drug Index No No GPI bigint Drug
Index No No DataSourceID int Data Source Identity No No
TABLE-US-00022 Column(s) of "Dim.DataSource" Table Is Is Name
Datatype Comment PK FK DataSourceID int Data Source Yes No Unique
Identity Number DataSourceName nvarchar(50) Name of the Data No No
Source Provider DataSourceDe- nvarchar(255) Description of No No
scription the Data Source FileDate datetime No No CreationDate
datetime The date on No No which the data is being inserted
LastModifiedDate datetime Last modified No No date LastModifiedByID
varbinary(85) Last modified by No No Identity number IsActiveFlag
bit Bit indicator No No for the validity of the record
DataSourceOwnerID int Data Source No Yes Owner description
TABLE-US-00023 Column(s) of "Dim.Ethnicity" Table Is Is Name
Datatype Comment PK FK EthnicityID int Ethnicity Identity. Yes No
Primary key of the table Ethnicity nvarchar(50) Details about
ethnicity No No NISNumber int No No SelfReferenceID int No No
TABLE-US-00024 Column(s) of "Dim.Location" Table Is Is Name
Datatype Comment PK FK LocationID int Location identity number Yes
No CountryID int Country identity number. No No Foriegn key
referenced from Dim.Country table. State nvarchar(30) State
information No No City nvarchar(30) City information No No
AsciiName Nvarchar(200) No No Latitude float(19, 12) Latitude
information No No Longitude float(19, 12) Longitude information No
No GeoNameID int Geographical detail No No about a location
SelfReference int Self referenced number No No
TABLE-US-00025 Column(s) of "DIM.Subject" Table Is Is Name Datatype
Comment PK FK SubjectID integer Subject Yes No Identification
SubjectName varchar(20) Subject Name No No MoleculeTeam char(18)
Molecule team No No information CaptureDate char(18) The date on No
No which the record was captured. IsLocked bit No No
IsActiveVariableFlag bit No No ProcessFlag bit No No
SubjectDetailDescription nvarchar(max) No No
TABLE-US-00026 Column(s) of "Fact.Patient" Table Is Is Name
Datatype Comment PK FK PatientID int Patient Identification Yes No
Number SubjectID integer Subject Identification No Yes EthnicityID
int Ethnicity Identity. No Yes Primary key of the table LocationID
int Location identity No Yes number PatientSourceID int This field
indicates No No to the source for the patient information.
DataSourceID int Data Source Identity No Yes number indicating the
source of the data Gender varchar( ) Patient Gender No No
Information Age numeric(,) Patient Age No No BirthYear int Date of
Birth Year No No SourcePatientID int Source Patient No No Identity
Ethnicity nvarchar(50) Information about No No Ethnicity AuditETLID
int Reference to Audit. No No ExecutionLog key for auditing
IsActiveFlag bit Bit indicator for No No the validity of the record
BMI decimal(19, 12) Body Mass Index No No CreatinineValue
decimal(19, 12) No No eGERValue decimal(19, 12) No No
ProteinCreati- decimal(19, 12) No No nineRatio
TABLE-US-00027 Column(s) of "Fact.PatientCoMorbidityCondition"
Table Is Is Name Datatype Comment PK FK PatientCoMor- int Patient
CoMorbidity Yes No bidityCondtionID Condition Unique Identity
Number. The Primary Key of the PatientID int Patient Identifi- No
Yes cation Number. Foreign Key from Fact.PatientType CodeID int No
No SubjectID integer Subject Identifi- No Yes cation Number.
Foreign Key from Dim.Subject. MedcinID int Medication No No
Identity Number Type nvarchar(55) No No Category nvarchar(55) No No
Status nvarchar(55) No No OnSetDate datetime No No CreationDate
datetime No No LastModifiedDate datetime No No LastModifiedByID
varbinary(85) No No IsActiveFlag bit No No DataSourceID int Data
Source No Yes Unique Identity Number ICD9CodeID int Code Identity
No Yes DiagnosisTypeID int No No
TABLE-US-00028 Column(s) of "Fact.PatientConcomitantMedication"
Table Is Is Name Datatype Comment PK FK PatientConcomi- int
PatientConcomi- Yes No tantMedicationID tantMedicationID is the
unique primary key to the table PatientID int Patient Identifi- No
Yes cation Number ConcomitantMed- int Concomitant Medi- No Yes
icationID cation Identity. The Primary Key of the table SubjectID
integer Subject Identifi- No Yes cation Dose nvarchar(55) Dose of
the Medi- No No cation Strength nvarchar(55) Strength of the No No
Medication Form nvarchar(55) Form of the Medi- No No cation Units
nvarchar(55) Unit of the Medi- No No cation Quantity nvarchar(55)
Quantity of the No No Medication Status nvarchar(55) Status of the
Medi- No No cation CreationDate datetime Bit indicator for No No
the validity of the record LastModifiedDate datetime No No
LastModifiedByID varbinary(85) No No IsActiveFlag bit No No
DataSourceID int Data Source Unique No Yes Identity Number
MedicationID int No Yes TreatmentTypeID int No Yes
TABLE-US-00029 Column(s) of "ICD9.Codes" Table Is Is Name Datatype
Comment PK FK ICD9CodeID int Yes No IndicationID int No No
IndicationGroupID int No No Code varchar(20) No No Description
varchar(255) No No IndicationGroupCode varchar(20) No No
ParentCodeID int No No DataSourceID int Data Source Identity No No
number indicating the source of the data
TABLE-US-00030 Column(s) of "Medication" Table Is Is Name Datatype
Comment PK FK MedicationiD int Yes No MedicationName nvarchar(75)
No No DataSourceiD int No No
TABLE-US-00031 Column(s) of "TreatmentType" Table Is Is Name
Datatype Comment PK FK TreatmentTypeiD int Yes No
TreatmeniTvoeDescriotion nvarchar(50) No No CreationDate date No
No
Advantages
[0061] Embodiments of the present invention provide many advantages
over conventional methods of predicting the enrollment for clinical
trials. For example, embodiments of the present invention allow
subject identifications (Os) to be created through one or more user
interfaces. In one embodiment, a user can create one or more
subject IDs without technical expertise. For example, using one or
more user interfaces, a user can create a new subject by entering
or selecting a subject name, molecule team, and/or a capture date.
A unique subject identification may be dynamically created for the
subject In another embodiment, a user can update an existing
subject. For example, a user may be able to add or update a capture
date or other information associated with a particular subject ID.
Based at least in part on the capture data, data from various
tables and/or databases associated with a subject ID may be
limited. For example, the capture date may provide a static point
in time for which information contained in the tables and/or
databases is available. Thus, if the information for a particular
table and/or database specifies that the information is before the
capture date, then the information is available to the subject
identification. Alternatively, if the information for a particular
table and/or database specifies that the information is after the
capture date, then this information may not be available to the
subject identification.
[0062] Embodiments of the present invention provide one or more
core domains of information that may be used for analysis of a
clinical trial plan. For example, patient domains, country domains,
and/or investigator domains of information can be used according to
one embodiment. In some embodiments, one or more core domains are
built generically such that the system can manage data sources that
are unknown at the time the core domain is created. In this way,
using a generic structure that supports a domain, additional data
sources can be added and/or updated as additional inthrmation
and/or data sources become available.
[0063] Subject identifications may be associated with at least a
portion of the information for one or more domains. For example,
subject identifications may be associated with information
contained in a patient domain, a country domain, an investigator
domain, and/or other domains or data sources.
[0064] Once various parameters are chosen and associations between
subject identifications and information in the domains have been
created, embodiments of the present invention are able to take a
mathematical approach to analyzing and presenting data regarding
the actual investigators and investigation sites. The embodiments
can then create graphical representations, e.g., line graphs that
display information, such as predictions for likely scenarios based
on average performance as well as best and worst-case scenarios
based on outlier data.
General
[0065] Numerous specific details are set forth herein to provide a
thorough understanding of the claimed subject matter. However,
those skilled in the art will understand that the claimed subject
matter may be practiced without these specific details. In other
instances, methods, apparatuses or systems that would be known by
one of ordinary skill have not been described in detail so as not
to Obscure claimed subject matter.
[0066] Some portions are presented in terms of algorithms or
symbolic representations of operations on data bits or binary
digital signals stored within a computing system memory, such as a
computer memory. These algorithmic descriptions or representations
are examples of techniques used by those of ordinary skill in the
data processing arts to convey the substance of their work to
others skilled in the art. An algorithm is a self-consistent
sequence of operations or similar processing leading to a desired
result. In this context, operations or processing involves physical
manipulation of physical quantities. Typically, although not
necessarily, such quantities may take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared or otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to such
signals as bits, data, values, elements, symbols, characters,
terms, numbers, numerals or the like. It should be understood,
however, that all of these and similar terms are to be associated
with appropriate physical quantities and are merely convenient
labels. Unless specifically stated otherwise, it is appreciated
that throughout this specification discussions utilizing terms such
as "processing," "computing," "calculating," "determining," and
"identifying" or the like refer to actions or processes of a
computing device, such as one or more computers or a similar
electronic computing device or devices, that manipulate or
transform data represented as physical electronic or magnetic
quantities within memories, registers, or other information storage
devices, transmission devices, or display devices of the computing
platform.
[0067] The system or systems discussed herein are not limited to
any particular hardware architecture or configuration. A computing
device can include any suitable arrangement of components that
provide a result conditioned on one or more inputs. Suitable
computing devices include multipurpose microprocessor-based
computer systems accessing stored software that programs or
configures the computing system from a general purpose computing
apparatus to a specialized computing apparatus implementing one or
more embodiments of the present subject matter. Any suitable
programming, scripting, or other type of language or combinations
of languages may be used to implement the teachings contained
herein in software to be used in programming or configuring a
computing device.
[0068] Embodiments of the methods disclosed herein may be performed
in the operation of such computing devices. The order of the blocks
presented in the examples above can be varied--for example, blocks
can be re-ordered, combined, and/or broken into sub-blocks. Certain
blocks or processes can be performed in parallel.
[0069] The use of "adapted to" or "configured to" herein is meant
as open and inclusive language that does not foreclose devices
adapted to or configured to perform additional tasks or steps.
Additionally, the use of "based on" is meant to be open and
inclusive, in that a process, step, calculation, or other action
"based on" one or more recited conditions or values may, in
practice, be based on additional conditions or values beyond those
recited. Headings, lists, and numbering included herein are for
ease of explanation only and are not meant to be limiting.
[0070] While the present subject matter has been described in
detail with respect to specific embodiments thereof, it will be
appreciated that those skilled in the art, upon attaining an
understanding of the foregoing may readily produce alterations to,
variations of, and equivalents to such embodiments. Accordingly, it
should be understood that the present disclosure has been presented
for purposes of example rather than limitation, and does not
preclude inclusion of such modifications, variations and/or
additions to the present subject matter as would be readily
apparent to one of ordinary skill in the art.
* * * * *