A Model for Centralized Monitoring & Clinical Data Management Reducing costs while ensuring compliance, risk mitigation & quality

Transcription

1 A Model for Centralized Monitoring & Clinical Data Management Reducing costs while ensuring compliance, risk mitigation & quality Dr Michael J. Klein Regional Head & Vice President Quintiles Sub-Saharan Africa Copyright 2014 Quintiles

2 Future State Value Drivers The Clinical Trial Journey Today Tomorrow Operations Data & Analytics Process Optimization Complex execution capability Operational Control Performance-Based Execution taken as given Predictive Analytics GrowingSophistication Advanced Capabilities Knowledge Manifest in the Individual Heroic mindset Driven bycommunity of Knowledge Informed by a Vast Reservoir of Data 2

3 The Possibilities Harnessing Patient Data and Project Metadata How much does it cost per patient to execute a RBM trial in a public or private site for a typical phase 2 oncology trial in South Africa? What triggers and thresholds should I use on my Diabetes trial in Kenya? Why? What are the most common inbound calls you receive from sites in Ghana? What does that tell you about how to manage them? What are the leading indicators of a site quality risk for cardiovascular trials in South Africa? How are they different from the US? What does CDOS typically find when reviewing patient data on Alzheimer's trials that could help optimize site training? 3

4 Data-Driven Trial Execution Risk-based Monitoring expanded to total trial execution Data-driven Trial Execution begins with an in-depth risk assessment where our team of experts evaluates the scientific and operational risk of each protocol. Powered by Quintiles Infosario Data surveillance allows us to optimize and adapt monitoring throughout the trial, re-assessing risk and applying the right action at the right time. We use the right type of monitoring at the right time (on-site, remote, centralized), monitoring sites, data, patients and events that require more attention and focus. 4

5 The Shift to RBM Market Landscape *Survey Results 100% Never Other 80% RBM Non Users Don't know 2-3yrs Survey* found 50% of decision makers 60% have employed some aspect of RBM, while 60% of non-users plan to implement RBM 40% 1-2yrs in the next 2 years 20% RBM Users 0% Respondants % of respondents employee some aspect of RBM Next 6m Non-Users % of non-users timeline to implement RBM *Quintiles survey

6 The Shift to RBM Market Landscape Sponsors Survey Results Top RBM Implementation Drivers Key Barriers to RBM Implementation Reduced monitoring costs, CRA travel costs Shortened overall trial time Improved data quality Near real-time access to data Investigator complianceif sites not visited every 6 weeks Lack of face time between on-site CRA and investigator Perceived trade-off between qualityand risk 6

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8 Natural Resistance to Change 8

9 The Data-Driven Trial Execution (DTE) Model 9

10 The Data-Driven Trial Execution (DTE) Model Early Upfront Risk Assessment (CPM, Medical, DM, Bios, Customer) Medical / Medical Surveillance Project Management (Ongoing Risk Assessment and Iterative Operational Strategy) Issue escalation of any non triggered concerns picked up by talking to sites Project Triggers & Alerts Centralized Data & Operational Surveillance (CDOS) Data Management Subject-Level Review Triggers/ Analytics Site Data Queries Individual Subject Data Across Subject Data Centralized Clinical Operations (CCO) Across Site & Trial Data (Operational) Site Support (Contact C centre) Document Management Monitoring Support Patient / Site Triggers & Alerts Via On-site and Remote Monitoring Visits Site Relationship Site Monitoring Site Visits 10

11 Data Review Flow Three Levels of Innovation DATA FOR REPORTING, ANALYSIS = Quintiles Centralized Monitoring Team = Medical Monitoring Team Level 3: Data Flow Trend Review Medical review requires robust processes at lower levels to ensures efficient use of this high level medical resource Level 2: Data Flow Across Site / Across Subject Review Subject Level Data Review process seeks to ensure the subject s data is medically congruent and sound both inter + intra subject. Level 1: Data Flow Initial Review Initial cleaning process focuses on subject data point level Iterative data review cycles, resulting data fall-out and additional safety, quality, compliance, and operational indicators will trigger interventional actions FPFV LPLV TRIGGER EVENT #1 TRIGGER EVENT #2 TRIGGER EVENT N 11

12 Components of the DTE Engine Data Management Review Component What is it? Enhanced data management which is integrated with *new processes and technologies that enable Subject- Level Review and Data Analytics DM Review How it works? Data review focused on datapoint cleaning Systematic identification of clean patient status *Data cleaning prioritized by subject data segments Who does it? Clinical Data Coordinators Data Operation Coordinators Technology *Discrepancy Management System *Clean Patient Data Tracking and Reporting Tool *Data Segment Management and Reporting Tool 12

13 Components of the DTE Engine Data Management Review Component How it works? Data review focused on data-point cleaning Systematic identification of clean patient status Data cleaning prioritized by subject data flow ADMS Data Monitoring Services Workbench Clean Patient Tracker Automatednon-eDC discrepancy loading, obsolescing, and assignment management Systematic determinationof subject-level data cleanliness and study progression including edc queries and outstanding pages, nonedc discrepancies, labs, and coding V1 Clean data Subject 101 V2 V3 V4 V5 V6 Dirty data Unavailabledata (visits have not yet occurred) Specification & Validation Mgmt Oversight Mgmt Supports the definitionand management of data segmentation. Enhancedbusiness intelligence through system supported activities which provides timely reporting capabilities, quick resourcing decision-making, and high transparency to the customer Facilitates the CDOS engine by systematically managing the flow of data at a subject/visit level across systems to subject-level data review 13

14 Components of the DTE Engine Subject-Level Review Component What is it? Comprehensive Subject-Level reviews that evaluate the medical congruency of data DM Review How it works? Subject Level review aligned to readiness of data segments Take action based on reviews (query sites directly, communicate findings to medical monitoring and study management) Who does it? Subject-Level Data Reviewers Technology Data Segment Management and Reporting Tool Visualizations that support efficient reviews Activity and Collaboration Tracker 14

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16 Components of the DTE Engine Subject-Level Review Component How it works? Subject Level review aligned to readiness of data segments Take action based on reviews (query sites directly, communicate findings to medical monitoring and study management) Subject-Level Review Visualization Example 16

17 Components of the DTE Engine Data Analytics Component Subject Level Review Component Subject Level Review Component What is it? The ongoing review and trend analysis of data issues, operational metrics and triggers that supports a dynamic, flexible and targeted site monitoring strategy as well as driving high quality study execution DM Review How it works? Monitor issues from data reviews and analyse for trends Monitor operational triggers Present consolidated data to site monitors to support site monitoring strategy Who does it? Clinical Data Scientist Technology Automated trigger management Visualizations that support efficient reviews Activity and Collaboration Tracker 17

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19 Components of the DTE Engine Data Analytics Component Subject Level Review Component Subject Level Review Component How it works? Monitor issues/actions from Data Cleaning and Medical Reviews Monitor query rates and metrics Monitor relationship between data issues and reduced SDV Monitor Site performance metrics dataflow, SDV backlogs, Outstanding queries Present data reports to Site monitors to facilitate dynamic, informed decision making around site monitoring strategy Critically review the process regularly Examples of operational metrics/triggers include: Query rates, Data Entry and SDV backlogs, Protocol Deviations, Enrollment Rates Data Analytics Visualization Example 19

20 Where are we today? The journey 20 Sites Studies active in model as of 31 Dec ,505 active in the model as of 31 Dec Established team of Medical Data Reviewers; 55% qualified Medics 22,697 Subjects active in the model as of 31 Dec Additional studies to be implemented during 2014 As of endof December 2013 approximately 33%of currently active sites and 48% of currently active subjects are in DTEfor top 5 Pharma 20

21 Quality Assessment Results Subjects Sampled 168 Total Data Points Assessed Data Findings 155 Miss Rate 0.4% 2 Regulatory Inspections with no Critical or Major findings 21

22 Impact to Clinical Monitoring Costs DTE enables reduced SDV - reduces the time CRAs spend on-site 90% Sample Clinical Monitoring Visit Schema % of Sites 80% 70% 60% 50% 40% 30% 20% 10% 0% DTE-enabled model provides the opportunity to redistribute on-site visits and subsequently reduce clinical monitoring costs by ~20% Traditional DTE-enabled Model Frequency of On Site Visit Impacted by: 1. Reduced SDV 2. Decreased monitoring of high performing sites through operational data analytics 22

23 Decreased Time to Analysis-Ready Data Targeted Data Cleaning focusing on priority data Systematic data flow management allows for prioritization of data cleaning activities which leads to more timely analysis-ready data. Clean Patient Tracking Data Segment 1 Data Segment 2 V1 V2 V3 V4 V5 V6 V7 edc Runs every 1-2 weeks V1 V3 V1 V2 V3 V4 V5 V6 V7 Lab Front-end Discrepancies Back-end Discrepancies V2 V4 V6 Data Segment 1 Data Segment 2 Data Coordination Tracking Other Source V5 V7 Data Segment Promotion to Level 2 Review Allows for Site and Subject data analytics based on pre-defined data grouping Subject 1001 Data Seg DS1 DS2 DS3 DS4 Visits V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 Subject 1002 Data Seg DS1 DS2 DS3 DS4 Visits V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 Subject 1003 Data Seg DS1 DS2 DS3 DS4 Visits V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 Subject 1004 Data Seg DS1 DS2 DS3 DS4 Visits V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 Subject 1005 Data Seg DS1 DS2 DS3 DS4 Visits V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 23

24 Ability to Impact Medical Monitoring Enhanced Subject Level Data Review Data Cleaning and Analysis 100% 80% 60% Medical Monitoring Safety Signal Analysis Medical and Consistency Review Medical Monitoring Subject-Level Review Safety Signal Analysis Medical Review Medical Consistency Review The time Medical Monitors must devote to ensure medically congruent data is significantly reduced in a DTE-enabled model Morecost effective resources can perform high quality subject-level reviews in place of Medical Monitors 40% Additionally.. 20% Data Mgmt CDOS Data Mgmt Medical Monitors receive higher quality data because CDOS reviews for medical congruency 0% Traditional CDOS-enabled Model 24

25 Value Proposition.. Summary Data-drivenTrial Execution DTE leveraging optimal cleaning methods, specialist roles and new processes, enabled by expert therapeutic knowledge and bespoke technology, to support overall reduction in study costs while improving quality and site compliance through world class analytics Element Ability to Impact Clinical Monitoring Costs Decreased Time to Analysis- Ready Data Increased Quality/Reduced Risk Ability to Impact Medical Monitoring Value CDOS data reviews enable reduced SDV models without impacting risk or quality CDOSenables reduced clinical monitoring costs by ~20% Decreasedtime to analysis-ready data for subject-level reviews and medical monitoring Increased efficiencies during data review process Holisticsubject-level reviewsidentify data quality issues not caught in a traditional model Cross-subject/cross-site reviews not identified in a traditional model Receives analysis-ready data faster Can perform more effective reviews due to improved technologies Reduces amount of time spent on data-cleaning type issues 25

26 Natural Resistance to Change 26

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