AI & Real World Evidence for Clinical Trials

Realise the opportunities from AI-enabled clinical innovation

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Course Overview

Clinical trials are essential to bringing cutting-edge and innovative treatments to patients and their carers. But trials are getting more expensive, with less return. Artificial Intelligence can transform the efficiency of the clinical trials process, enabling companies to leverage data to mitigate the uncertainties, operations burdens and inefficiencies of trials.

This course will give managers and executives in pharma, biotech and CROs core insights into the use cases of AI technologies and Real World Evidence, as well as the challenges around integration. On completion, participants will be empowered to gain competitive advantage in AI-enabled clinical innovation. Real World Evidence and AI will accelerate the drive towards patient-centric and precise medicine.

Book by 9 November, Save £100

London: 10th December

PRICE: £799 + VAT

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Why choose this course?

Why is this topic important?

  • Only 6% of trials complete on time in the US (Clinithink, 2017).
  • The median cost of a Phase I trial is $3.4mn, rising to $8.4mn and $21.4mn for Phases II and III (source: FDA, 2017).
  • It takes up to 12 years for a new drug to be commercialised (source: Bayer, 2017).
  • Overall, clinical trials cost the US $51bn per year (AiCure, 2017).

Is it for you?

  • Director of Clinical Informatics
  • Clinical Informatics Analyst / Specialist / Lead
  • Clinical Director of Innovation
  • Chief Operating Officer
  • Chief Clinical Officer
  • VP, Product and Strategy
  • Clinical Operations Manager / Lead
  • Product Manager
  • Head of Analytics
  • Manager of AI / ML
  • Technology Manager

Programme Modules

  • Session 1 - High-level overview of AI and the advances in AI-enabled clinical development
  • Session 2 - Analysing use cases for clinical trial design, patient recruitment and site selection
  • Session 3 - Real World Evidence (RWE) for Phase II and Phase III
  • Session 4 - Challenges in implementing AI solutions and advancing AI-enabled clinical innovation

Learning Outcomes

Key course benefits

  • Dr Chrysanthi Ainali, the course director, is an expert bioinformatician and data science consultant who has worked for years on applying Machine Learning to clinical trial research.
  • The course will provide participants with clear use cases of AI for clinical trial design, patient recruitment and site selection, among others.
  • The course will give participants a comprehensive overview and understanding of the opportunities now available to apply AI and Real World Evidence to many of the problems afflicting trials.

Benefits to the individual

  • Explore: Learn how to navigate the forces in play – forces which enable / add hurdles to the adoption of AI technologies in clinical development. 
  • Strategise: Learn use cases of AI technologies for clinical trial design, patient recruitment, and site selection.
  • Implement: Navigate the unique challenges faced by pharma in implementing AI technologies and solutions. 
  • Innovate: Use Real-World Evidence for more patient-centred outcomes and to utilise RWE and AI for process automation, predictability, improved ROI and time-to-market for new drugs.

Programme Details

Module 1

Session 1: High-level overview of AI and the advances in AI-enabled clinical development

  1. The recent advances and setbacks of advanced analytics in clinical development
  2. The proliferation of scientific data – integrating trial data with Real World Data
  3. The potential of AI to provide new efficiencies, increased ROI, and reduced operations burdens
  4. Moving towards a future of precision medicine and AI-enabled healthcare

Module 2

Session 2: Analysing use cases for clinical trial design, patient recruitment and site selection

  1. Machine Learning for optimising clinical trial design – trial endpoints and adaptive studies
  2. Natural Language Processing Text Mining for unstructured data (e.g. EHRs) and patient recruitment
  3. Machine Learning for predictive analysis, site selection and patient selection (financial planning)

Module 3

Session 3: Real World Evidence (RWE) for Phase II and Phase III

  1. RWE from wearable technology and environmental sensors (Phases II and III)
  2. Gaining real-time insights into study health and patients’ lifestyles
  3. Partnerships for data management / Electronic Data Capture
  4. Sequencing and molecular data analysis for patient stratification

Module 4

Session 4: Challenges in implementing AI solutions and advancing AI-enabled clinical innovation

  1. Data sensitivity and patient compliance – the unique challenges faced by pharma
  2. Data quality and standards, data management and visualization
  3. Recruiting for data science talent
  4. Data silos in health information
  5. Building infrastructure for accessing data – your next steps

Course Director

 

Dr Chrysanthi Ainali is a bioinformatician and consultant with expertise in the integration of AI technologies and techniques. She has a PhD in Machine Learning and Translational Medicine from King’s College London, where she focused on the development, validation, and application of Machine Learning algorithms to the integration of genomic data for biomarker identification, disease subtype identification, and patient stratification. 

In 2017, Chrysanthi founded DiGNOSIS to provide professional consultancy and data science services for pharmaceutical companies, and to pioneer the application of AI to clinical trials. Prior to founding DiGNOSIS, Chrysanthi worked for Thermo Fisher Scientific, where she drove the bioinformatics component of designing and developing clinical genetic tests, as well as the customer rollout of bespoke bioinformatics workflows.  

Is it for you?

The course will benefit managers and executives in the clinical space - in product development, innovation, analytics, informatics, clinical solutions and clinical operations departments across pharmaceutical, and biotechnology companies, and CROs.

Leaders & Managers

Who want to understand new techniques in the aggregation and analysis of trial data, enabling new efficiencies and increased ROI

Strategists

Who want to avoid disruption in the rapidly expanding e-clinical space, and effectively navigate the challenges faced by pharma

Innovators

Who want to drive the future of patient-centric and precision medicine

Job Titles Include:

  • Director of Clinical Informatics
  • Clinical Informatics Analyst / Specialist / Lead
  • Clinical Director of Innovation
  • Chief Operating Officer
  • Chief Clinical Officer
  • VP, Product and Strategy
  • Clinical Operations Manager / Lead Product Manager
  • Head of Analytics
  • Manager of AI / Machine Learning
  • Technology Manager

Academy, London

The Academy, London is a one-of-a-kind, customisable and inspirational learning space for Google customer education and collaboration in Victoria. Based at 123 Buckingham Palace Road and in walking distance from both the Victoria Underground and Coach Station; the Academy London’s purpose is to help deepen partnerships with Google's key clients and influencers. The Academy space helps customers to transform their business digitally, to accelerate growth, improve efficiency and create a sustainable competitive edge. Googlers host a wide variety of educational events in the space, including hackathons, workshops, masterclasses and conferences.