Clinical Data Management, Research, and Analysis Software

InforSense ClinicalSenseT is a special clinical research software application for cohort identification, designed especially for clinical researchers. With ClinicalSense, clinicians and researchers can perform analysis of patient data on the fly with immediate results.

ClinicalSense enables clinical researchers to:

• Find the right candidates for clinical research studies by matching candidates from their own patient population against the eligibility requirements of a prospective study.
• Attract more clinical trials by uncovering the unique characteristics of their patient population and using that information to secure participation in the right trials.

ClinicalSense provides better clinical data management by supporting relevant clinical data types and visualization methods for those involved in clinical research, and eliminates clinical researchers'dependence on Excel and standard reporting and querying tools. Using the highly intuitive Clinical Population Explorer and Patient Event Viewer, they can:

• Easily browse large clinical datasets from patient populations
• Intuitively 'slice and dice' through aggregated clinical data analysis
• Identify subsets of patients with particular sets of clinical attributes
• Select and synchronize views of data that can be captured and shared for easy inter-patient comparison
• Switch between calendar-based timelines and age-based timelines, or align patients' timelines relative to particular events
• Drill down to investigate relevant data collected for any particular patient over a defined time period

While ClinicalSense can be configured to run on any existing clinical data warehouse designs, InforSense has developed a proprietary data model that can optionally be used in conjunction with ClinicalSense. The InforSense Clinical Data Model has been designed to handle the challenges and peculiarities of the clinical research environment, including:

• Large number of dimensions
• The 'time' element of data
• Multiple research studies in the same model
• Multiple (and evolving) terminology sets
• Sparsely distributed data
• Multiple perspectives on the same data