centralized monitoring: the role of process and technology · •cro oversight •clinical data...

March 20, 2019

Centralized Monitoring: The Role of Process and Technology

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Speakers

Alec VardySenior Director, Data Management

Jazz Pharmaceuticals

Tom DekkerSenior Product Manager

Veeva Systems

Centralized MonitoringAlec VardyMarch 20th, 2019

Centralized Monitoring

Topics to be addressed: What is it? What are (some of) the implications? Thoughts on implementation

What is Centralized Monitoring?

Centralized Monitoring

On-site Monitoring

Remote Monitoring

What is Centralized Monitoring?ICH E6 R2 -- Addendum to 5.18.3 Extent and Nature of Monitoring tells us some of the things that centralized monitoring is expected to do.

Review, that may include statistical analyses, of accumulating data from centralized monitoring can be used to:

(a) identify missing data, inconsistent data, data outliers, unexpected lack of variability and protocol deviations.

(b) examine data trends such as the range, consistency, and variability of data within and across sites.

(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems.

(d) analyze site characteristics and performance metrics.(e) select sites and/or processes for targeted on-site monitoring

What is Centralized Monitoring?(a) identify missing data, inconsistent data, data outliers, unexpected lack

of variability and protocol deviations.

(b) examine data trends such as the range, consistency, and variability of data within and across sites.

(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems.

(d) analyze site characteristics and performance metrics.

(e) select sites and/or processes for targeted on-site monitoring

What is Centralized Monitoring?(a) identify missing data, inconsistent data, data outliers, unexpected lack

of variability and protocol deviations.

(b) examine data trends such as the range, consistency, and variability of data within and across sites.

(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems.

(d) analyze site characteristics and performance metrics.

(e) select sites and/or processes for targeted on-site monitoring

What is Centralized Monitoring?(a) identify missing data, inconsistent data, data outliers, unexpected lack

of variability and protocol deviations.

(b) examine data trends such as the range, consistency, and variability of data within and across sites.

(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems.

(d) analyze site characteristics and performance metrics.

(e) select sites and/or processes for targeted on-site monitoring

What is Centralized Monitoring?(a) identify missing data, inconsistent data, data outliers, unexpected lack

of variability and protocol deviations.

(b) examine data trends such as the range, consistency, and variability of data within and across sites.

(c) evaluate for systematic or significant errors in data collection and reporting at a site or across sites; or potential data manipulation or data integrity problems.

(d) analyze site characteristics and performance metrics.

(e) select sites and/or processes for targeted on-site monitoring

What are the implications?We need to look for unexpected lack of variability, examine data trends such as the range and variability of data within and across sites, and evaluate for potential data manipulation

We need to analyze site characteristics to select sites and/or processes for targeted on-site monitoring

We need to document plans, activities, conclusions and actions

What are the implications?

• Understand the mechanisms / processes that generate the data

• Target investigations accordingly

Data manipulation

• Typically done currently to identify and resolve issues

• Need to switch to a more predictive and proactive approach

Analyzing site characteristics

What are the implications?

• Support primary and key secondary objectives:

• Critical to subject safety• Support decision-making about

efficacy and safety of the drug

Critical Data

• Generate critical data• Support ethical and GCP

complianceCritical Processes

What are the implications?

• Variables assessed by comparison across countries, CRAs, and/or sites

• Include indicators relevant to critical data and to critical operational processes

Risk Indicators

• The level, point, or value associated with a Risk Indicator that will trigger an action

• Thresholds are set in order to clearly understand when the information from our Risk Indicators is inconsistent with the data we expect.

Thresholds

Thoughts on Implementation

Implement process and technology simultaneously

Work with entire study team to define risk indicators clearly

Clearly define actions to be taken if an indicator “fires”

Need training on root cause analysis and change management

Final thoughts

CROSS FUNCTIONALTEAM WORK IS KEY!

Building Quality by Design into design & planning of trial

Conducting early and ongoing risk assessments

Focusing on Critical Processes and Data

Using Risk Indicators, Thresholds & Action Plans

Adjusting monitoring activities based on risksRISK ASSESSMENT

CATEGORIZATIONTOOL

CRITICALVARIABLES

MONITORINGPLANS

Focus on what matters

Employ best mechanisms to monitor the remaining risks

Centralized Monitoring can: Ensure routine review of data in near

real time. Monitor data quality and critical data

reporting. Facilitate proactive and early

identification of quality, safety and operational risks

Identify unusual patterns in data earlier

Employ analytics to target on-site/off-site monitoring activities on areas of greatest need.

Track site performance metrics Identify higher risk sites to target

additional monitoring.

Final thoughts

Senior Product ManagerVault Clinical Operations

Tom Dekker

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Table of Contents

Process1Functional Capabilities2Unified View3Unified Technologies4Data Warehouse & Lake5Predictive Analytics6

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Process

Plan

ReviewReport

Act

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Capabilities

Report

PlanAct

• Define Roles• Capture Risk Assessment• Route with Workflow • Library of Risks• Leverage Learnings

Review

• Scheduling (monthly,quarterly)• Alerting / Notification• Unified View• Mediation Activities

Act

• Issue Management• Task Lifecyle• Workflow• Monitoring Plan

Report

• Dashboards• TMF• Inspection• Audit Trail

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Medical Review

Patient Safety

Site Monitoring Data

Management

Centralized Monitoring Holistic ApproachUnified View

• Previously siloed clinical data systems

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Medical Review

RIs, Alerts, Analytics

Patient Safety

Site Monitoring

Data Management

Unified View

• Previously siloed clinical data systems

• Technology breaks down these silos

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Unified View

• Previously siloed clinical data systems

• Technology breaks down these silos

• Unified view across the clinical trial

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Name Description Source

Subject Recruitment Enrollment, Screen Failure Rates, Discontinuation

IVR, EDC

Data Quality CRF Completion, Query Counts & Aging

CDMS/EDC

Safety AE & SAEs Counts Safety, EDC

Protocol Deviations Deviation Counts, Severity CTMS, EDC

Workload Overdue Visit & Trip Reports, Outstanding SDV

CTMS, EDC

IP Accountability Inventory Discrepancies IXR, Clinical Supply

Data SourcesRisk Indicators (RIs)

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• Unified− One system. Single database − Leveraging same instance of a records (study, site records)

• Connected− Separate applications, or databases − Connected by vendors− No customer configuration required

• Integrated− Application Programming Interfaces (APIs)− Batch, file based − Extract Transform & Load (ETL)

• Manual− Upload by user of formatted data− Excel, CSV

Unifying Technology

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• Data Warehouse (DW, EDW)− Repositories of integrated data from one or more disparate sources.− Used for analytical reporting across enterprise − Current and historical data− Sourced from operational systems− Extract Transform & Load (ETL)− Cleansing process− Highly structured schema designed for reporting− Valuable Data

Data Warehouse

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• Data Warehouse (DW, EDW)− Repositories of data from disparate sources.− Used for analytical reporting and predictive modeling− All data types

• Standard +, Sensor Data, Images, Documents, Web Logs

− All current and historical data− Unstructured Data, No transformation. − No Cleansing − All Data (may not be useful)

Data Lake

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Predictive analytics: includes techniques that analyze historical data to make predictions about future events.

• Anticipate Risk− Anticipate risk in the future based on historical data

• Identify sites with likely protocol non-compliance • Subject with likelihood of AEs

− Take actions ahead of problems

• Mediation Modeling− Model actions & results for impact

• Monitoring Visits• Training• SDV

− Optimize site visit activities

Predictive Analytics / AI

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Leveraging modern technology to proactively identifies emerging risk and take action to improve data quality and speed trial execution.

Central Monitoring

Question & Answers

Alec VardySenior Director, Data Management

Jazz Pharmaceuticals

Tom DekkerSenior Product Manager

Veeva Systems

Thank You