Automation in CDM & Best Clinical Research Training

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Best clinical Research Course In Pune

• By-
• Shreya Gupta.
• Clariwell Global Services.
• Pune.

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.