From Data Capture to Decisions Making Innovation through Standardization How Can Standardization...

From Data Capture to Decisions Making Innovation through Standardization

How Can Standardization Help Innovation

Michaela Jahn, Stephan Laage-WittPHUSE 2010, DH04October 19th,2010

2

3

BackgroundBroad Range of Responsibilities for Clinical Science

Ongoing work of the study management

team

Medical data review during study conduct

Signal detection on study/project

level

Publications & presentations at congresses

Data base closure preparation and

clinical study report writing

Communication to project team and

management

Innovate!

Clinical Pharmacologist

BiomarkerExpert

TranslationalMedicine Leader

Drug Safety Expert

Radiologist

The complexity of clinical trials is increasing constantly

Preliminary analysis for study decisions during

conduct

Exchange information

Many Demands from Science and OthersEnabling Innovation

4

Thinking time and space

Room for exploration – no guarantee of successEarly and speedy access to quality data

Integrated data displays

Further improved operational efficiency

High quality and regulatory compliance

Flexibility for different study designs and new data typesSupport for study amendments before and after enrolment

Clinical Data Flow & Tools

Processes and Data on Study Level

Processes and data on Project Level

Cross-functional SOPs& Business Processes

Standards for:

Enabling Innovation - Facilitated via Standardization

Dataflow & Tools • Less tools and system interfaces• Cross-functional alignment on standard platforms

Study Level • Simplified and standardized data flow

Project Level • Standardized data formats and displays

SOPs & Processes • Clarified and documented business processes

5

4 Key TopicsDriving Innovation Through Standardization

2

1

3

4

Edison's light bulb became a global successstory due to its standardized bulb socket .

6

Simplified Data Flow for Clinical DataDeveloping a 2 years roadmap

In 2007, a detailed analysis of the existing data flow revealed a fairly complex system environment with a number of gray areas.

A cross-functional team designed a new data flow and a target system environment which we implemented over the recent 2 years. Key elements are:

• Streamlined data flow• Less systems and fewer interfaces• Minimize redundant data storage• EDC for all studies

1

7

Implementing the Roadmap Standards for Data, Systems, Processes

Key Decisions for clinical data withinRoche Exploratory Development (pRED)

– Use of Medidata Rave as the standard data capture tool

– Use of SAS for data extraction and reformatting across all involved functions

– Implementation of CDISC/CDASH as data capture standard

– Implementation of CDISC/SDTM as data extraction standard

– Single, cross-functional repository for clinical data

– The same standardized data flow for preliminary data during study conduct and final data after study closure

– Grant scientists access to the data during study conduct

– Allow state of the art tool for medical data review and early decision making

1

8

Clinical Science requires early access to quality data

Addressed by• Studies are handled in the same way• Reduce study start up times• First data extraction within study are done earlier• Clinical Science gets data earlier

Providing Speedy Access To Study Data

2

Study setup ready First data extraction Medical Data Review

Study setup ready First data extraction Medical Data Review

without standards

with standards80% savings* ~50% savings*

* Gartner report 2009

Study time

Decision point during study conduct

Data accumulation / cleaningTime until enrolment start

9

Clinical Science requires easy access to interpretable data

Addressed by• Standardized e-Forms are used to capture data (CDASH)• Extraction of data into a standardized data model (SDTM)• Standardized data model is translated into language beyond variable

names (data model repository)

Standardizing Data Formats and Displays

3

Medidata Rave

Standardized e-Forms

Standardized

Extractions

10

•Clear distinction between mandatory steps and deliverables versus flexible ways of working

•Clear identification of roles and responsibilities•Consistent and integrated graphical

representation of the business processes

Clarifying Business ProcessesA smarter way to manage the “Who is Doing What”

4

The process redesign using a database approach delivered an integrated view of processes and RACI charts.

CustomQueries

AdobePDF

HTML

11

Receiving data early

New Responsibilities for Clinical Science

Accept unclean data

Accessing study data

More responsibility to protect the integrity of the study

Reading study data directly

Learn and understand the concept of data models and standards

Managing flexibility via protocol amendments

Moving away from standards costs time and resources

Exploring study data Understand the concept of exploration and noise

12

Summary of Success

The implementation of the changes to systems, data flow and process began in 2008 and finished in 2010.

Experience to date

Fast Study Setup eCRF and DB build is kept off critical path, and can be reduced to a few weeks if required

Fast Data Access Overall fast availability of study data during conduct, if required, data availability within hours after the assessment

Tailored Graphical Displays

Data displays in Spotfire showing up-to-date study data, receiving very positive feedback from clinical science

Flexibility for changes to running studies

Very fast implementation of changes to studies during conduct as required for many exploratory studies.

Strong partnership between Data Management, Biostatistics, Programming and Clinical Science

Collaboration on the development of standardized data extraction and cross-functional business processes. Enabling pragmatic solutions where needed.

Speed

Flexibility

13

Conclusions & Learning

• The key elements for enabling scientific innovation are:

• Access to data in a usable format

• Time for the clinical scientists to work with it

• The clinical data flow relies on a complex machinery of systems and processes across multiple disciplines.

• Changing one single component will not deliver the expected benefits

• Innovation does not necessarily come with sophistication. Key critical factors are rather the opposite:

• Simplification and standardization across all components of the data flow

• Access to timely data during the entire lifecycle of a study comes with responsibilities

• Use it wisely!

… and it still uses the same standardized bulb socket.

Thank you