Multifaceted aspects of metadata maximize efficiencies

Transcription

1 Multifaceted aspects of metadata maximize efficiencies May 10 th, 2012 Patrick Genyn, Senior Director Drug Development Information Governance 9th Annual SAS Health Care & Life Sciences Executive Conference

2 Content Hermes Initiative Next future-proof Clinical Data (CDM) practice P 4 : Process, People, Platform and Partner Drug Development Information Governance Master Data Master Data Governance 2

3 Hermes Initiative Improved predictability & transparent process towards our customers Sustainable roles, increased efficiency Solid foundation for further improvement Learning organization, innovation High quality deliverables & increased inspection readiness Deliverable-based model Globally consistent & well understood processes 3

4 Hermes - process Aligned with entire Clinical E2E Process map Linkage guaranteed to other functions (monitoring, stats and programming, clinical, ) Deliverables clearly defined with single responsibilities Integrate end-to-end the CDM processes across Therapeutic Areas Ensure consistency where possible Accept differences where they make sense Integrate end-to-end the CDM processes across all phases Early Development / phase 1 studies Exploratory & Confirmatory / phase 2 and 3 studies Medical Affairs and Post Marketing / phase 4 studies 4

5 Hermes - people Organizational principles Internal focus on customer interaction, oversight & innovation External focus on optimized end-2-end operations Minimize the number of roles 3 integrated organizational structures with focus on Therapeutic Area: Interaction with Clinical Team & R&D partners Delivery: Interaction with external e2e operational partners Infrastructure: Process, Platform and Partner performance Learning organization Formal class room training On-the-job training and coaching Comprehensive competency model 5

6 Hermes - partner Partnership Wikipedia definition: A partnership is an arrangement where parties agree to cooperate to advance their mutual interests. Challenge: Are our interests really mutual when a partner provides data management services? Principles Contract is deliverables based Responsibility of the quality of the deliverable is with the partner Accountability of the quality of the deliverable is with the sponsor Scope includes : ecrf build, Database build, Ongoing data cleaning/query resolution and Submission ready data package Multiple partners to maintain competition Niche providers for specialty deliverables 6

7 Hermes platform (1/5) 7

8 Hermes platform (2/5) The Data Standards Library contains : Standard CRF templates (CDASH) Metadata definitions (SDTM, Therapeutic area) used to create study metadata 8

9 Hermes platform (3/5) Generating study metadata by selecting CRF templates from the Data Standards Library and adding the trial specific metadata. 9

10 Hermes platform (4/5) Study Metadata Repository is used to measure the consistency of metadata Study metadata is sent to external or internal partners for ecrf and database build 10

11 Hermes platform (5/5) After study build, data and metadata will be: compared against the Study Metadata Repository validated against the Data Standards Library Verification 11

12 Content Hermes Initiative Next future-proof Clinical Data (CDM) practice P 4 : Process, People, Platform and Partner Drug Development Information Governance Master Data Master Data Governance 12

13 Problem Statement No to little cross domain information governance/transparency siloed strategies Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Meta Data Mgmt Pre-Clinical ChemPharm Clinical Medical Safety Project Mgt Office Regulatory Quality Assurance HCP 13

14 Current situation: close interdependency organization process systems - data 14

15 Targeted Future Situation Master/Meta Data /Governance Link to Discovery Pre-Clinical ChemOPharm Clinical Medical Safety Project Mgt Office Regulatory Quality Assuramnce HCP Link to Commercial & Supply Chain Data Quality & Oversight Optimal data exchange & deployment / Process Automation / Compound Data Strategies / Patient Outcomes support

16 Target situation: Multi-tier strategy for improved and sustainable data management

17 What is Data Governance? Data Governance is an organizational structure that creates and enforces policies & procedures for the business use and management of data across the development organizations Business Goals for Data Governance Compliance with internal and external regulations for data usage and reduce risk exposure relative to data and its use Business value generated from our data and information assets Technical Goals for Data Governance Establish and enforce standards for data Improve data quality; remediate its inconsistencies; share data; 17

18 Governance Structure and DDIG Policy Executive Stakeholders Governance Office Domain and Functional Experts Data Owners Data Users Terms and Data Definitions Data Ownership Data Processes Quality Requirements Business Rules Applicable industry Standards Applicable Regulations Monitoring and Metrics 18

19 What is Master Data in DDIG? The consistent and uniform set of identifiers and extended attributes that describe the core entities in drug development and are used across multiple business processes or communities, specifically Data relevant to 2 or more business communities Data critical to the drug development process (e.g. from a compliance perspective) Data created once and reused many times

20 The MDM Hub Process Contribute from Multiple Sources Master an Authoritative View Distribute to Multiple Functions Validation MDM Hub QC Integration Contributing Data Source Trustworthy Relevant Timely Adopting Data Store 20

21 Data Quality Framework - Requirements Uniqueness Unique identification of an instance Completeness Required, expected and permissible attributes. Accuracy The true value (in real life) of the data Compliance Formatting requirements Standard and regulatory requirements Consistency Not conflicting with any other data inc. timeliness Complete from referential integrity perspective Integrity All above quality criteria met through the entire lifecycle (create, update, use, distribute and retire) 21

22 Data Quality Framework - Procedures Selective Data Profiling Data is analyzed to find errors, inconsistencies, data redundancy and incomplete information Data Matching and Merging removing duplicates Data Cleansing (missing data, inconsistent data, formatting ) Data Data Enriching from third party sources Permanent Data Integration Validate from Contributing Data Source to MDM Hub QC-ing Adopting Data Store against MDM Hub Data quality issue reporting, analyzing, resolving and tracking Issue Remediation Data quality Issue severity, risk and priority management Data Monitoring Baseline, target and improvements from baseline Compliance to quality requirements and business rules

23 Current scope Therapeutic Areas Project Portfolio Product Development Non Clinical Development Clinical Development Medical Safety Regulatory Quality and Compliance Partnering Market Intelligence Product Portfolio Planning Portfolio Risk Product Portfolio Epidemiology Diagnostics Marketed Product Support Biomarkers Research/ Biosignature Research Genomics Target Product Profile Definition Project Resources Planning and Monitor and Update Resource Plan Functional Initiation & Planning Functional Maintenance & Control Change Control Senior Support API Small Mol API Large Mol Preformulation Pre-clinical and clinical Supply - Planning Pre-clinical and clinical Supply - Fulfillment Toxicology Cardiovascular Safety Clinical Pharmacology CDP Packaging Development Investigator Operational BioAnalysis (BA) Relationship Pilot Plant & New Product Procedures Clinical Research Site Introduction Regulatory Submission Conduct and Drug Analytical Development Aggregated Monitor Trials Partner Development Reporting Monitoring & Non-Clinical Drug Control Portfolio & Metabolism & Capacity Manage Pharmacokinetics Outcomes (DMPK) Research Global Product License Subject identification Strategic Training Operations Data, Analysis and Reporting Filing & Archiving Product Safety Planning and Compound Signal Detection Regulatory Intelligence Regulatory Submissions Study Design Formulation Development Laboratory Animal Strategies Dossier Planning Drug Development Program Medicine Case Excipients Development Trial Planning & - Manage Policies Clinical Activity Budgeting Adverse Event Regulatory Strategy Pre-Clinical Activity Labelling Information Gathering Global Compliance Requirements Healthcare Compliance Reference data & terminology Risk Discovery Manufacturing Commercial Legal External Partners Co Development Partners Authorities Marketing Intelligence External Provider 23

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