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Title: Automation in CDM & Best Clinical Research Training (1)


1
Best clinical Research Course In Pune
  • By-
  • Shreya Gupta.
  • Clariwell Global Services.
  • Pune.

2
Introduction to Clinical Data Management (CDM)
  • Clinical Data Management (CDM) holds the entire
    life cycle of clinical data from its collection
    to exchange for statistical analysis in support
    of performing regulatory activities. It primarily
    focuses on data integrity and dataflow. Clinical
    Data Science (CDS) has expanded the scope of CDM
    by ensuring the data is reliable and credible.
    Risk-based data strategies are essential to
    consider as the most important component in the
    automation of clinical data management. Other
    solutions include identifying sites for clinical
    trials, targeting the right audience, recruiting
    the right patients, collecting reported outcomes,
    obtaining digital consent, remotely screening
    patients, and conducting decentralized trials.

3
What is CDM?
  • Not all data collected is useful for statistical
    or other analysis. There has been a steady
    increase in data volume CDM can ensure which
    data needs to be collected to support further
    analysis. CDM is responsible for generating
    structured and unstructured data from various
    sources and transforming that data into useful
    information. Generating, integrating, and
    interpreting different data type new data
    technology strategies. Take Clinical Research
    Course from the Best.
  • Sponsors have incredibly increased the use of
    healthcare apps and digital health technologies
    to collect other real-world data (RWD) and
    reported outcomes. Over 200 new health apps are
    added every day to app stores. Phase IV is most
    likely of all clinical trial phases to witness
    experiments with digital health. However, this is
    unfortunate since it can improve the efficacy of
    clinical research trials in various ways.

4
Continued
  • Automation of clinical data management presents
    myriad possibilities for clinical research
    trials. Streamline clinical trial management,
    enhance data collection, analysis, and sharing,
    better matching of eligible patients with trials,
    and an overall improvement in experience for all
    stakeholders are some ways suggested and tested
    strategies. Still, a lot still needs to be done
    to enhance and maximize the benefits of
    automation.
  • Currently, electronic health records (EHRs) and
    electronic data capture (EDC) can rarely be
    integrated. The problems of exchange and the
    non-standardization of data should be solved for
    the clinical research industry to achieve the
    full potential of automated processes

5
Below we will discuss the pain points that can be
fixed to automation in Clinical Data Management
  • Standardization of data
  • Data should be standardized before automated
    sharing. It will lead to a faster collection of
    trial evidence and better analysis, enhanced
    transparency, faster start-up times, increasing
    the predictability of data and processes, and
    easier reuse of case reports across different
    studies. . Take the Best Training in Clinical
    Research.
  • Interoperability of EHRs for automation
  • Although the use of EHRs has not been optimal,
    they have yielded great benefits at low costs and
    less time and presented significant possibilities
    for research. The collection, organization,
    exchange, and automation of data depends on the
    effective use of electronic health records
    (EHRs). However, EHRs have a history of poor
    interoperability and insufficient quality control
    and security of data. The way data is stored in
    these records often varies across institutions
    and organizations. Sharing the data becomes a
    struggle since there is no standard format for
    EHRs.

6
Improvement in AI and automation
  • Artificial intelligence (AI) has great potential
    to identify eligible patients for clinical
    trials. However, the reality is quite different
    from expectations. The major problem has been the
    development of sophisticated algorithms. Other
    barriers include the unstructured format of data
    and how to integrate that data into the clinical
    workflow of stakeholders. Clinical trial
    stakeholders can indefinitely benefit from a data
    exchange network, particularly one established
    between clinical trial sites and sponsors.
  • The network would collect and analyze data
    before sharing it with relevant stakeholders,
    improving overall quality. Sponsors shall be able
    to share important information with sites,
    including draft budgets and protocol documents.
    At the same time, sites shall be able to update
    sponsors in real-time on impending matters, such
    as patient registrations. This would ensure an
    unhindered flow of information through integrated
    systems.

7
Continued
  • However, sites should remember that not all
    information can flow freely and should be careful
    while sharing protocol-specified data with
    sponsors. EHRs have protected health information
    (PHI) and non-protocol-specific data, which would
    put patients confidential data at risk if
    shared.

8
Problem with current data sharing
  • The current mindset that all data, automated or
    otherwise, is proprietary and its exchange could
    prove competitively disadvantageous is a
    hindrance. Apparently, some data is proprietary
    but more data should be shared to mitigate the
    complexity and rising costs of clinical trials,
    prompting sponsors to run more efficient clinical
    trials with faster enrollments. These outcomes
    will lead to enhanced medical research and
    development, bringing new therapies and
    treatments to the market faster. An intelligent
    Clinical Data Management System (CDMS) shall
    prove beneficial for scientists who look forward
    to interacting with the data, rather than just
    collect, organize and integrate them.

9
Conclusion
  • A data system is required that allows free flow
    of data, connects patients, monitors,
    researchers, data managers, CROs, and sponsors,
    ensuring best clinical decision making in
    real-time. It will also lead to quantitative
    analysis of data and data-driven decision-making.
    It will automated information exchange and ensure
    elevating clinical trials to new levels.
    Standardization and automation of clinical data
    will make it more easily accessible and usable,
    and shareable. Automation in Clinical Data
    Management shall push the boundaries of what can
    be achieved in the clinical research industry.
  • A new and advanced approach to data collection,
    management, integration, and analysis shall
    enable data exchange, prompting researchers,
    sponsors, and medical professionals to make fast
    evidenced-based decisions. Data sharing has made
    it possible to quickly determine the safety and
    efficacy of new drugs and treatments for
    different patient populations.
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