Introduzione

Scope of this online training is to enable participants to make informed decisions and plan/implement strategies on the use of artificial intelligence (AI) in pharmacovigilance to manage the available pharmacovigilance information.

The course covers:

  • digital technologies from the regulatory framework to strategies, operational methods, and supporting technologies;
  • how to exploit the wealth of up-to-date structured and unstructured pharmacovigilance related information available (from traditional and innovative sources);
  • how to comply with current and future regulations;
  • gives an overview of the available data in the pharmacovigilance domain and of how to turn it into useable information and valuable knowledge to improve effectiveness and quality.

The following topics will be covered:

  • The scenario
    • Current and possible future regulations
    • Recent and future developments
    • Volume vs. quality
    • Case reports vs. intelligence
    • Automated reporting
    • What are the differences?
      • Machine learning
      • Artificial intelligence
      • Data science
      • Automation
  • Why do we need to do that?
    • Examples from real life
  • Challenges and rewards
  • A possible approach: modular
  • A use case: social networks
  • A case analysis: pharmacovigilance and signal management
    • Entry-level solutions
    • Next-generation solutions
      • Automated searches
      • Filtering and summarizing information
  • Building a simple case
    • Analysis
    • Implementation
    • Testing
    • Reporting
  • Where to find more information
  • The “human factor”
    • How will employees react?
    • What are the implications for training and career paths?

 

This 3 module online training course is designed to benefit functional/technical professionals coming from pharmaceutical and biotech companies, clinical research organizations (CROs) and public health centres dealing with the pharmacovigilance, such as:

  • Qualified Person for Pharmacovigilance (QPPV)
  • Pharmacovigilance officers
  • Quality assurance
  • Pharmacovigilance auditor
  • Knowledge manager 
  • IT manager

Participant experience 

– Knowledge of basic pharmacovigilance

– Basic knowledge of computers and productivity packages (Office, etc.)

Presentation, including hands-on exercises, and debates.  

Docente/i
Marco Anelli
Info

Marco Anelli

Head of Medical Affairs and Pharmacovigilance Advisory Practice - PLG (Product Life Group)
Marco Anelli has been appointed in January 2016 “Head of Pharmacovigilance and Medical Affairs Advisory Services” at PLG. As “Deputy Chief Scientific Officer” of PLG, Marco coordinates all delivery and research projects (internal and on behalf of clients) linked to Big Data, Knowledge Management, Artificial Intelligence and Machine Learning. Previously, Marco was R&D Director at Keypharma, an Italy-based ProductLife Group company, where was responsible for the coordination of all clinical and preclinical aspects of projects run internally and on behalf of clients. Drawing on a career in the pharmaceutical industry that spans 25 years, Marco provides expert oversight on a wide range of R&D and Medical Affairs related activities. Marco has participated in and coordinated all stages of drug development – from formulation to Phase I-IV and pharmacovigilance. In addition, Marco is a qualified QPPV and has prepared and overseen more than 200 non-clinical and clinical overviews and summaries. Before joining Keypharma and PLG, Marco was Medical Affairs Director at Eurand. In the last few years, he has extensively worked in the fields of pharmacoeconomics and health technology assessment. He has a medical degree from the University of Milan, specializations in Medical Statistics and Clinical Pharmacology from the University of Pavia and an international master’s degree in health economics and pharmacoeconomics from the University of Pompeu Fabra in Barcelona, along with formal training in Data Science (Johns Hopkins University) and a Professional Certificate on Applied AI from IBM.

Online training – 3 modules

Module 1| October 21th, 2020            9:30 am – 13:00 pm CEST
Module 2| October 28th, 2020            9:30 am – 13:00 pm CEST
Module 3| November 04th, 2020         9:30 am – 13:00 pm CEST

After the registration, you will receive all details about the connection.

 

This online training admits a maximum number of 10-12 attendees

 

The course will proceed with a minimum number of participants. Should this number not be reached the registered participants will be notified one week prior to the commencement of the course.

 

Early Bird: € 1.080,00* (until 30 September 2020)

Ordinary: € 1.260,00*

Freelance – Academy – Public Administration**: € 663,00*

* for Italian companies: +22% VAT

** Early Bird discount not applicable to Freelance – Academy – Public Administration fee

The fee includes: Access to the online training, teaching materials in pdf format provided post-webinar, organizational office support, certificate of attendance that will be sent after the training via e-mail

Acquista

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Cosa saprai fare dopo il corso
Have a working knowledge of relevant information on Artificial Intelligence in Pharmacovigilance
Have a working knowledge of the new technologies that could affect Pharmacovigilance operations in the near and medium-term future
Select the appropriate tools and processes to implement a new technologies-based approach
Set up simple but specific strategies
Interact with the tools and service vendors/providers to set up more complex solutions
Manage and report the information

<p><span>Online interactive training on Zoom platform. </span></p>
<p><em>LS Academy will provide the access link to the virtual platform a few days before the training.</em></p>

Online interactive training on Zoom platform. 

LS Academy will provide the access link to the virtual platform a few days before the training.

<p><span>Online interactive training on Zoom platform. </span></p>
<p><em>LS Academy will provide the access link to the virtual platform a few days before the training.</em></p>