Diapositive 1

Report
Project management in data
management:
need for a shift in paradigm
Swati Tare, PhD. PGDCR-SIROCLINPHARM
An organized development of an end product is a
Project
and
the discipline driving the process is Project Management
which involves juggling between the basic elements, namely cost,
performance and schedule
2
Project management
3
So what’s new ?
Technologies have evolved at a faster pace, it is said
that 2000 is all about velocity!
--Changing timelines…
Complex trials,
--Changes in scope…
Long durations
--Quality issues…
Stringent regulatory
requirements
--Resource attrition…
Reduce time to market
Cost effectiveness
4
Time for a shift in paradigm
Changes are inevitable and so are the risks,
So acknowledge them!!
Reactive
Proactive
Proactive
5
Gearing up for pro-activity
•
•
•
•
•
•
Process improvement
Training
Knowledge sharing
Project plan
Task ownership matrix
Rewards
•
•
•
•
•
Reporting metrics,
Issue trackers
Deviations log
Governance plan
Risk management plan
6
Process improvement
Robust designsdynamism,
flexibility,
Edits to prevent
wrong entries
Standards-- CRF,
DVP, SOPs, GLIB
Data Quality
Management—
Data entry
conventions
Standard query
text
CRF completion
guidelines
Coding Guidelines
Audits
7
Training
Training on protocol, efficacy end points,
regulatory submission
Knowledge Sharing
Gives an opportunity to capitalize on
way-outs and avoid the slip–ups
8
Task ownership matrix
Accountability
--responsibility
--answerability
--trustworthiness
--liability
Rewards
Belongingness!
9
Reporting metrics
and issue trackers
• Trend analysis on queries
Lesser queries-cost saving-better CRO-sponsor
relationships
• Reports on discrepancy ageing
• Reports on entry to verification gap
• Trackers on data issue
Avoidance rather than Alleviation
10
Communication
Communication
Between
stakeholders plays an important role
“No one can whistle a symphony.
It takes a whole orchestra.”
H.E. Luccock
11
Risk management
Identify
Assess
Prioritize
Minimize, Monitor and Control
12
Case study
Background
Rescue study, homegrown system
quantum of data unknown, status of
data cleaning unknown
Challenges
To build a database in absence of
complete data specifications
protocol amendments for new
recruitments, stringent timelines
Approach
Detailed plan for migration
TOM
RMP
13
Risk
Impact
Mitigation
Delay in test data
transfer
Increase in
timelines
Meetings and follow up
with third vendor for
data transfer
Different technical
platforms
Extensive data
More resources
mapping and pre- Migration of complex
processing of
datasets on priority
datasets
Different outlook and Acceptability and
functionality of
compliance at site
database
Training to site
personnel
Non acceptable
characters in data
Identification during
review,
Data entry for such
characters
Failure of data
migration
Surprises
• Despite doing a thorough planning for mitigating risks,
there were surprises..
• Few datasets were merged
• Whilst few were separated
15
Take away..
• Accepting the existence of challenges helps us to
make a shift in the paradigm from reactive to
proactive
• As quoted by Benjamin Franklin--it is easy to see,
hard to foresee
• So better to be equipped for “Fire fighting” as well
16
17
18

similar documents