Metadata Driven Clinical Data Integration – Integral to Clinical Analytics April 11, 2016

1. Metadata Driven Clinical Data Integration – Integral to Clinical AnalyticsApril 11, 2016 Kalyan Gopalakrishnan, Priya Shetty Intelent Inc. Sudeep Pattnaik, Founder, Thoughtsphere

2. Agenda

3. Role of Dynamism and Automation in Integration Dynamism Drivers for Dynamism and Automation • Storage structures at appropriate levels of hierarchy and stages of data lifecycle need to be dynamic • Such dynamism needs to be planned either by leveraging existing metadata or manufactured metadata • Alternatively or in addition, a robust user interface or means of configuration can address gaps. • Key is to minimize code change. • Source structure, transformation rules, target structure based on study analytical needs. Most could vary across studies. • This warrants a set of dynamic transformation rules to accommodate heterogeneous needs. • In addition the structure of the source, the physical storage, maturity of the data transfer mechanism and relevant data dictionaries could vastly vary as well • Important to minimize and possibly avoid any code change, transformation pre-processing services in data ingestion layer. • These are simply costly and time consuming, and discourages adoption within the enterprise. Automation • High availability of data to points of analysis • From disparate sources: Raw source data, integrated data across CTMS, IxRS, EDCs, Labs, Reconciled, cleansed/not cleansed, aggregated data • Based on use cases - interim analysis, submission, operational metrics, central monitoring, medical monitoring etc. • Key is to automate data delivery in appropriately usable format with minimal manual intervention

4. Integration Aspects Warehouse Approach Hub Approach Integration – Two Approaches • Pre-Modeling Required • Structure oriented • Generic content model (schema) required based on storage technology. For Ex. Form/Domain level storage • No Pre-Modeling, Loosely coupled • Storage granularity preserved as per the source system. • Data tagged at appropriate level after reconciliation. Storage and Modeling • System agnostic Integration. Data is ingested at source level granularity without pre-processing – ELT approach. • Requires source feeds to adhere to input descriptionsor requires setup / configuration • Robust mapping user interface @ Study level which utilizes a mapping library with auto (machine) learning technologies – promotes mapping reuse across studies • Post processing pipeline architecture • Requires source system adapters, Pre formatting to warehouse structure – ETL approach. • Enabling dynamism and automation for transformations, requires: • Availability of a repository of governed metadata – structural and transformational. • Interface that allows study level mappings and leveraging existing library of rules • Multiple adapter development, especially with external sources (Labs/partner data) Source Data Integration Data Processing • Transformations accomplished on an as-needed basis, in a post-processing layer, based on business needs. For Ex: • Operational review processes need subject level data granularity • Bio-statistical programming processes need SDTM +/- domain level tabulated data • Heavy reliance on data pre-processing before loading into the warehouse • Time consuming and costly

5. Metadata Driven Data Processing Business Issue • How do we provide quicker access to source and analysis ready data? • How do we adapt to changes in regulatory standards rapidly and apply these changes to business and operational processes? • How do we bring in more efficiency in the source to target mapping and transformation processes? Solution Overview Solution Impact • Data Ingestion Framework ingests data from Diverse Sources (clinical data, operational data, reference data) • Populate Structural Metadata (Source/Target/Reference) and Transformational Metadata (rules/derivations) in Metadata Repository • Dynamic Process applies transformation rules on source data to generate target datasets • High Availability of Data (Source, Integrated, Standardized) • Reusability of Standard Algorithms • Dynamic Automated Process • Accelerated Path for Submissions • Enhanced Support for Product Defense, Data Sharing, Data Mining • Traceability

6. Dynamic SAS process leverages SAS Macros corresponding to transformational metadata • Structural &Transformational Metadata extracted from Metadata Repository drives dynamic program for generating hybrid SDTM target datasets Approach 1 - Metadata Driven Dynamic SAS Engine • Source to Target Transformations – Updates in metadata repository applied in next run, MedDRA Merge, ISO Formats

7. Approach 2 – ClinDAP - Thoughtsphere’sMetadata Driven Source System Agnostic Clinical Data AggregationFramework ClinDAP - Next Generation Data Aggregation Platform • Source System Agnostic Data Aggregation Framework • Proprietary algorithms to aggregate disparate data sources (EDC, CTMS, IVRS, Labs, ePro, etc.) • Document-oriented database readily assembles any structured or unstructured data • Robust Mapping Engine, extensible rule library reusable across studies (Hybrid SDTM) • Interactive visualization-based data discovery • Robust Mapping Framework – Reusable mapping library, Leverage existing SAS libraries, Specify complex study level transformations, Extensible targets – Hybrid SDTM, ADaM • Ability to operationalize analytics is possible when you enable automation and dynamism to integrate data and generate standardized datasets