Data Privacy Framework (DPF)

AWS Partnership


Other Services

Real World Evidence (RWE)

Harnessing disparate data sources for patient-centric view

Data Lake

Transforming your Data Swamp into Data Lake.

Advanced Analytics and Machine Learning

Revealing patterns unnoticeable to the human eye

Real World Evidence (RWE)

By bringing together various real-world data sources such as ePRO, electronic health records (EHR), claims and medical device, PDH fosters advancement of clinical trial research. The ability to integrate and harmonize large disparate RWD sources is vital to uncovering the complete patient journey.

PDH supports the FDA’s call for “next generation” clinical research providing mechanism for lowering cost of drug development and expanding patient access to treatment.

Examples of SUMMA™ RWE uses:

Hybrid Trials
  • Connect historical patient data and multiple RWD sources
  • Increase enrollment breadth and depth of usable data
  • Lower costs for site and subject
  • Reduce overall patient burden
  • Expedite cohort building
  • Find the right patient without involving IT
  • Protocol manipulation “on the fly”
  • Efficiently adjust inclusion/exclusion criteria
  • Reduce manual coding and extensive download time
Synthetic Cohort
  • Increase patient treatment access
  • Eliminate enrollment hurdles
  • Truncate timelines and lower costs

Data Lake

SUMMA™ enables the integration of large real-world patient data sources into one repository or Data Lake. The platform has the capability to ingest these RWD assets and standardization of these disparate data into a common data model (e.g. CDISC, OMOP).

What sets SUMMA™ apart from other data lake providers is the emphasis placed on data harmonization and curation with processes such as:

  • Auto-cataloging
  • Auto-fill
  • Elastic search

This configurable Data lake creates cohesiveness across entie organization by breaking down silos and supporting a “Center of Excellence” approach for uniform and accessible enterprise-wide data utilization.

Creating one central repository with automated functions allows for many operational efficiencies in an organization:

  • Reduced IT complexity and costs
  • Increased efficiency of internal resources
  • Self-service – putting the power in the user’s hands

Advanced Analytics And Machine Learning

Given the volume and intricacies of disparate data sources, Machine Learning (ML) and Artificial Intelligence (AI) are necessary to reveal patterns in large, complex data not visible to the human eye.

SUMMA™ automates these advanced techniques across large volumes of linked data, leading to broader impact in less time to establish new patterns and insights for clinical discovery. These patterns can lead to more targeted outcomes while decreasing cost, time and resources.

SUMMA™ can effectively solve a variety of clinical challenges:

  • Segmentation and risk stratification – timely, accurate, diagnosis and treatment
  • Identification of gaps in care among specific patient or physician segments
  • Disease progression, treatment pathways and patient utilization and costs
  • Feasibility studies in trial design and implementation
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