The XB Bio-Integration Suite:
Solutions and Capabilities

The XB Bio-Integration Suite represents a complete analytical and data management solution for health sciences researchers and clinicians with several key capabilities:

Integrate All Data

Within XB—BIS, longitudinal, phenotypic, environmental and protocol data is directly integrated with molecular data generated from experiments performed on samples derived from the subject. Installations define their own set of unique attributes and fields to capture and maintain to ensure consistency with existing data environments. Data can be captured and analyzed longitudinally to support higher level trend analysis based not only on measurements of bio-medical or molecular data points, but changes in these values over time. This allows for addressing the issue of consequence versus cause effect. Users search and navigate the data base on conditions of any combination of data fields, both genotypic and phenotypic in nature. Users generate canned and dynamic reports against any combination of attributes, including user defined fields. The system provides interactive visualization of the data so that users can refer back to data records by clicking on points or areas of interest on graphical representations of results. Users define their own groups to be used for analysis. Groups can be defined through dynamic queries/filters and/or within analysis. Data can be imported from and exported. Cohesive sets of user defined fields are grouped into modules that form the data type template for importing and exporting.

Integrated Research Platform

Utilizing XB Bio-Integration Suite™, researchers are able to access multi-variant data sources (both internal to the organization and external public data stores), create unique patient cohorts, analyze the data utilizing a broad spectrum of analytic and statistical tools, and create unique, testable hypotheses. A spoke-level data mart is developed for the researcher, where he is able to integrate his data with the organization's hub data warehouse, creating a data mart unique to his study. After the completion of the study, the information the researcher gathered from the primary care providers as well as the ultrasound images is then merged back into the hub with appropriate enterprise data standards and metadata, where it can be leveraged by other researchers looking for similar information. The data is managed by the appropriate security services so that it is only utilized for the use cases approved by the IRB, even when brought into the hub.

Personalized Medicine Module

TMS offers a Personalized Medicine Module (PMM) as a component-off-the-shelf (COTS) add-on to XB-BIS. PMM leverages XB-BIS to generate a personalized medicine report, which identifies molecularly targeted therapies based upon the unique molecular profile of a patient's cancer. The report correlates molecular characteristics of the patient's disease with potential drug therapies. The module leverages multiple algorithms and the results are merged and ranked for the oncologist's consideration. Following are the steps for producing a personalized medicine report:

Self-Service Cohort Identification

One of XB-BIS' main strengths is its ease of use. Researchers and clinicians are not required to learn SQL or other programming languages in order to perform complex queries and basic analysis. XB-BIS provides a simple and intuitive user experience for querying and viewing data. TMS presents XB-BIS' cohort identification features in a series of vignettes—starting simple and progressively becoming more complex. Imagine a researcher interested in identifying and evaluating mesothelioma patients and the detection of tumor markers at various stages based upon age of patient, race and ethnicity. The researcher can quickly and easily utilize the self-service cohort identification application to identify the number of consented patients and tumors in the biorepository, and then further refine the analysis set based on queried phenotype and molecular data across all time. Utilizing the cohort identification application, sequence and relative duration between events (key input to identifying members of the cohort) are easily provided to the researcher.