Risk based approcah

1. Abstract for Risk Based Approach in
Clinical Data Management
The Abstract for Risk Management in Clinical Data Management
This abstract will provide the maximum information about the Risk Management and
how to fore-see the risks and best approachable way to handle the risks which can be
responsible for a huge unexpected loss or collateral-damage or may possibly can be an
apocalypse in any way to an Organization.
Clinical Data Risk Management (CDRM) plays a crucial role in enabling CROs to identify,
contain, measure, monitor, validate and manage the risks to produce a quality and error free
data before it will go for analysis part. But so far, there is no specific instrument has been build
which can access and overcome the possible risk(s). Therefore our main objective is how to
handle these unseen risks and to implement it while managing the critical data whose main
interest is the safety and effectiveness of Subject(s).
Factor:
One of the problem is to Identify “What is the Risk” and How to deal with it i.e. “Risk
Management”. And our Risk management services for Clinical Data Management include Data
risk assessment, strategic planning, surveillance strategy design, and Quality Management
support. We do also provide complete Statistical Analyzing and Medical Writing services. And
with the service of site selection, patient recruitment.
Possible Risks in CDM:
1. Poor design of studies Data Base, study processes in themselves and lack of proper
communication during Study Start Up often being much more complicated than
necessary to achieve what is required.
2. Not able to identify the Priority.
3. Lack of proportionality (one size fits all) in the implementation of quality control
activities.
4. Lack of understanding of the impact of variability in trial conduct and measurement or
trail data collection on the study which may can bring a negative impact on reliability.
5. Lack of knowledge or more particularly real understanding of the goals.
1|Page

2. Abstract for Risk Based Approach in
Clinical Data Management
These issues are often deeply embedded in the culture and thinking of the
organizations and people involved in data management and are consequently very difficult to
change. This paper intends to open up the discussion on new approaches to clinical data
management and to new thinking, in order to facilitate the development of proportionate
clinical data management processes.
Areas that are most often raised as causing particular concern are the design and complexity of
the study protocols and data to be collected, the extent and nature of the monitoring that is
implemented, as well as the related data management and the extent and nature of
documentation required to be completed and retained for a given study.
Limits:
The acceptable variation in tolerance limits should be established bearing in mind the
data management process design of the specific trial and the potential impact of the different
levels of variability on the power of the trial.
Risk Identification & Assessment:
What may go wrong?
Chance of occurrence?
What would be in particular the impact on trial subjects’ rights/well being/safety
and/or on the reliability of the trial results?
Risk Control:
Decision made to reduce and/or accept risks
Where risks are to be mitigated, the methodology adapted to conventional GCDMP
should be defined
Risk Communication:
Documentation of Process with reviews of the measures as necessary
Communication to all decision makers
Review:
Results and new Information (e.g. new pre-clinical data, new safety data, any
critical data (points), Protocol Amendment (If any)) and ongoing review (e.g.
Data Monitoring Committee Meeting Output, Audit Report concerns)
2|Page

3. Abstract for Risk Based Approach in
Clinical Data Management
Initiate and Implementation:
Putting in place the actions identified, particularly for high risks, but conversely
there may be implication on low risks
How it can be resist:
Powerful Clinical Data Management System (CDMS)
Full range of data capture options, including well developed EDC, Tracker, Medical Coder, etc.
Real-time processing of clinical data, streamed to intuitive visual reports and dashboards
Customized, role-specific views to enhance individual performance
Automated validation of CRFs from any source
Scalable, fault-tolerant, 21 CFR Part 11 validated environment
Study Collaboration & Management Portal
Secure online hub for all project communication
Auditable, version-controlled document management
Training environment seamlessly integrates all systems, business practices
Recommended Actions:
Identify the business case for global initiatives that address data quality
Develop and implement health information management standards for data
content and documentation practices
Provide principles for designing systems which support collection of high-quality
data at the point of care, data aggregation, information retrieval and health
information exchange
Research known data integrity issues to identify cause and identify solutions
Provide workforce education and certification in clinical data management, data
mapping
Data analytics
3|Page